Publications

Abstract: In face of an increasing number of automotive cyber-physical threat scenarios, the issue of adversarial destabilization of the lateral motion of target vehicles through direct attacks on their steering systems has been extensively studied. A more subtle question is whether a cyberattacker can destabilize the target vehicle lateral motion through improper engagement of the vehicle brakes and/or anti-lock braking systems (ABS). Motivated by such a question, this paper investigates the impact of cyber-physical attacks that exploit the braking/ABS systems to adversely affect the lateral motion stability of the targeted vehicles. Using a hybrid physical/dynamic tire-road friction model, it is shown that if a braking system/ABS attacker manages to continuously vary the longitudinal slips of the wheels, they can violate the necessary conditions for asymptotic stability of the underlying linear time-varying (LTV) dynamics of the lateral motion. Furthermore, the minimal perturbations of the wheel longitudinal slips that result in lateral motion instability under fixed slip values are derived. Finally, a real-time algorithm for monitoring the lateral motion dynamics of vehicles against braking/ABS cyber-physical attacks is devised. This algorithm, which can be efficiently computed using the modest computational resources of automotive embedded processors, can be utilized along with other intrusion detection techniques to infer whether a vehicle braking system/ABS is experiencing a cyber-physical attack. Numerical simulations in the presence of realistic CAN bus delays, destabilizing slip value perturbations obtained from solving quadratic programs on an embedded ARM Cortex-M3 emulator, and side-wind gusts demonstrate the effectiveness of the proposed methodology.

Abstract: Investigation of the folding pathways of protein molecules plays a key role in studying diseases such as Alzheimer’s and designing viral drugs at the molecular level. Despite recent advances in visualization techniques, effective sonification (i.e., non-speech auditory representation) of large datasets associated with protein folding pathways is still an open question. This paper investigates the problem of sonification of protein folding pathway datasets by using the wave space sonification (WSS) framework due to Hermann (2018). In particular, this paper utilizes the powerful WSS framework to develop a sonification methodology for the dihedral angle folding trajectories of protein molecules, which are modeled as hyper-redundant robotic mechanisms with many rigid nano-linkages. As an example, the developed sonification methodology is applied to a protein molecule backbone chain with a dihedral angle space of dimension 82, where a canonical wave space function based on a sum-of-sinusoids with conformation-dependent frequencies and a sample-based wave space function based on Mozart’s Alla Turca are utilized for sonification of the folding trajectories of this peptide chain.

Abstract: Compact tether-based actuation is a suitable approach for the deployment of femto/picosatellite bodies from CubeSats using ultrasmall electrodynamic tethers for fuel-free propulsion and deorbiting. Despite the advantages of tethered satellite systems, control technologies for these have yet to mature in several domains including robustness to structural faults and unmodeled dynamics. A proposed solution for the identification of disturbances to tethered satellite dynamics is to use a data-driven algorithm to learn the system’s behavior over previous orbits and then provide an estimated prediction for the evolution of system states. To achieve the goal of state prediction via a globally linearized system model, this paper employs the Koopman operator constructed from observed dynamics to extrapolate future motion of a tethered subsatellite subject to unknown disturbances while being deployed from its mothership. Numerical simulations of the constructed model versus the nonlinear model of the tethered satellite system demonstrate the effective prediction capabilities of the proposed Koopman operator-based numerical algorithm for the general flight characteristics many orbits into the future.

Abstract: Koopman operator theory has proven to be a promising approach to nonlinear system identification and global linearization. For nearly a century, there had been no efficient means of calculating the Koopman operator for applied engineering purposes. The introduction of a recent computationally efficient method in the context of fluid dynamics, which is based on the system dynamics decomposition to a set of normal modes in descending order, has overcome this long-lasting computational obstacle. The purely data-driven nature of Koopman operators holds the promise of capturing unknown and complex dynamics for reduced-order model generation and system identification, through which the rich machinery of linear control techniques can be utilized. Given the ongoing development of this research area and the many existing open problems in the fields of smart mobility and vehicle engineering, a survey of techniques and open challenges of applying Koopman operator theory to this vibrant area is warranted. This review focuses on the various solutions of the Koopman operator which have emerged in recent years, particularly those focusing on mobility applications, ranging from characterization and component-level control operations to vehicle performance and fleet management. Moreover, this comprehensive review of over 100 research papers highlights the breadth of ways Koopman operator theory has been applied to various vehicular applications with a detailed categorization of the applied Koopman operator-based algorithm type. Furthermore, this review paper discusses theoretical aspects of Koopman operator theory that have been largely neglected by the smart mobility and vehicle engineering community and yet have large potential for contributing to solving open problems in these areas.

Abstract: Super-knock is a phenomenon triggered by pre-ignition and has limited the design envelope of internal combustion engines (ICEs) in terms of power density. This poses a huge challenge for the automotive industry where engine sizes have been continuously decreasing due to the demand for weight savings and integration with electrified powertrains. Such downsized engines typically require increased boost pressure, availing conditions conducive to pre-ignition, which in turn may trigger super-knock. Traditionally, this and other forms of knock have been managed by way of a “detection and mitigation” approach in place of perdition and avoidance” due to an evolving understanding of corresponding combustion dynamics, as well as the incapability of emerging real-time computational methods to perform and actuate over the timescale required. In this study, a data-driven algorithm is used to extract (and adapt) a globally linearized system representation using eigen-time-series, isolating the dynamic modes of the system to capture underlying effects leading to pre-ignition without the need for physics-based modeling. This approach is a unique application of the “Hankel Alternative View of Koopman” (HAVOK) analysis for chaotic systems and can be executed on board an engine control module supplying a buffer of recent to latest time-step data to predict an impending pre-ignition event. The proposed design does not require any change to existing sensors and actuators in the existing knock management system architecture, nor would it require any significant increase in computational capacity in terms of the associated engine control unit. A simulation was conducted with real super-knock data to nominally test the applicability of the algorithm. From this training dataset, the linearized dynamic system was able to predict pre-ignition approximately 2.27 s prior to the event, which is adequate to take mitigating action. Further validation runs covering low, medium, and high engine speeds within the envelope of low-speed pre-ignition (LSPI) generated similar results.

Abstract: Autonomous ground vehicle convoys heavily rely on wireless communications to perform leader-follower operations, which make them particularly vulnerable to denial-of-service attacks such as jamming. To mitigate the effects of jamming on autonomous convoys, this paper proposes a behavior-based architecture, called the Behavior Manager, that utilizes layered costmaps and vector field histogram motion planning to implement motor schema behaviors. Using our proposed Behavior Manager, multiple behaviors can be created to form a convoy controller assemblage capable of continuing convoy operations while under a jamming attack. To measure the performance of our proposed solution to jammed autonomous convoying, simulated convoy runs are performed on multiple path plans u nder different types of jamming attacks, using both the assemblage and a basic delayed follower convoy controller. Extensive simulation results demonstrated that our proposed solution, the Behavior Manager, can be leveraged to dramatically improve the robustness of autonomous convoys when faced with jamming attacks and can be further extended due to its modular nature to combat other types of attacks through the development of additional behaviors and assemblages. When comparing the performance of the Behavior Manager convoy to that of the basic convoy controller, improvements were seen across all jammer types and path plans, ranging from 13.33% to 86.61% reductions in path error.

Abstract: Limited spectrum access and complex resource allocation have impeded the cost-effective and robust deployment of large groups of noncellular unmanned aerial vehicles (UAVs) for new use cases in extreme conditions under dynamic spectrum management requirements. This letter introduces a novel design for UAV-to-UAV communications that relies on joint noncontiguous spectrum aggregation and allocation of sub-6 GHz and millimeter wave (mmWave) bands. To achieve bandwidth efficiency and reduced complexity, the proposed location-aware waveform design utilizes target sensing/positioning information in the aggregation as well as spectrum fragment allocation for UAV-based joint sensing and communications. The proposed method, which relies on the prediction of the position and direction of motion of the network UAVs, utilizes a delay-tolerant observer-based estimator–predictor in the presence of uncertainties. The performance of the proposed location-aware spectrum aggregation is validated using numerical simulations.

Abstract: Numerous communications and networking challenges prevent deploying unmanned aerial vehicles (UAVs) in extreme environments where the existing wireless technologies are mainly ground-focused; and, as a consequence, the air-to-air channel for UAVs is not fully covered. In this paper, we address the crucial problem of beamforming in the UAV-based communications by utilizing a spatial estimation algorithm, which relies on the theory of observers in the control systems literature. The proposed spatial estimation algorithm relies on using a delay-tolerant observer-based predictor, which can accurately predict the positions of the target UAVs in the presence of uncertainties due to factors such as wind gust. The solution, which uses discrete-time unknown input observers (UIOs), reduces the joint target detection and communication complication notably by operating on the same device and performs reliably in the presence of channel blockage and interference. The effectiveness of the proposed approach is demonstrated using simulation results.

Abstract: Understanding the process of protein unfolding plays a crucial role in various applications such as design of folding-based protein engines. Using the well-established kinetostatic compliance (KCM)-based method for modeling of protein conformation dynamics and a recent nonlinear control theoretic approach to KCM-based protein folding, this letter formulates protein unfolding as a destabilizing control analysis/synthesis problem. In light of this formulation, it is shown that the Chetaev instability framework can be used to investigate the KCM-based unfolding dynamics. In particular, a Chetaev function for analysis of unfolding dynamics under the effect of optical tweezers and a class of control Chetaev functions for synthesizing control inputs that elongate protein strands from their folded conformations are presented. Based on the presented control Chetaev function, an unfolding input is derived from the Artstein-Sontag universal formula and the results are compared against optical tweezer-based unfolding.

Abstract: This paper investigates the problem of collaborative robotic car-painting using a team of industrial manipulators that can be heterogeneous. Given the CAD model of the car, a collection of heterogeneous articulated robotic arms, and their corresponding fixed base positions on the factory floor/ceiling, the objective is to generate a collection of joint trajectories for each robot in a computationally efficient manner such that the car body can be painted by the nozzles attached to the arms while collisions during the painting process are avoided. Our solution to this computationally intensive collaborative coverage path planning relies on decoupling the collision avoidance from the coverage path planning by exploiting the inherent two-dimensional structure of the problem. In particular, our algorithm relies on partitioning the reachable space of the forearms of these robots, projecting the resulting volumes of intersection on the sides and the top of the car body, and performing the coverage planning on the resulting projected volumes. Simulation results on several industrial arms that are collaboratively painting a Ford Motor Company F-150 truck demonstrate the effectiveness of our proposed solution.

Abstract: The first edition of this book inspired many researchers and educators working at the intersections of robotics, control theory, and computer vision from the early days of its publication. This second edition, published 14 years after its predecessor, maintains the central perspective that robots are machines that transform sensing into action through feedback control, with the goal of manipulation of objects or locomoting in their ambient environment. Given its central focus on feedback control, along with its comprehensive treatment of linear, nonlinear, and geometric control algorithms for robotic manipulators and mobile robots, this book holds a special place among other robotics textbooks [1]–[5]. In addition to updating the material on motion planning, vision, and vision-based control to reflect state-of-the- art changes in the field, the current edition contains two new chapters dedicated to the emerging topic of underactuated robotic mechanisms, including a significant extension of the coverage of mobile robots. This edition also contains two new appendices dedicated to the topics of optimization and camera calibration.

Abstract: Input images are the main source of information for vision-based algorithms. The presence of raindrops in input images degrades their quality and, consequently, reduces the quality of the target vision-based algorithm that consumes them. Many image restoration algorithms were proposed in the literature to remove rain presence in images to improve the input image quality. These algorithms, however, cannot remove all the raindrop presence and sometimes introduce undesirable side-effects, such as the blurring rain-occluded sections of the image and incorrectly de-raining areas in the image that are clear. It is hypothesized that a comparable performance improvement can be achieved by decreasing the sensitivity of vision-based algorithms to noisy input images, rather than denoising these images, through the process of de-raining. To test this hypothesis, the performance of state-of-the-art object detection and semantic segmentation models was evaluated, with de-rained image datasets used as input, and compared it to that performance of the same models, retrained with rained image sets. Results showed that the performance of the retrained models was better than that of the baseline detector with de-rained images used as input.

Abstract: Utilizing military convoys in humanitarian missions allows for increased overall performance of healthcare logistical operations. To properly gauge performance of autonomous ground convoy systems in military humanitarian operations, a proper framework for comparative performance metrics needs to be established. Past efforts in this domain have had heavy focus on narrow and specialized areas of convoy performance such as human factors, trust metrics, or string stability analysis. This article reviews available Army doctrine for manned convoy requirements toward healthcare missions and establishes a framework to compare performance of autonomous convoys, using metrics such as spacing error, separation distance, and string stability. After developing a framework of comparison for the convoy systems, this article compares the performance of two autonomous convoys with unique convoy control strategies to demonstrate the application and utility of the framework.

Abstract: Predicting the final folded structure of protein molecules and simulating their folding pathways is of crucial importance for designing viral drugs and studying diseases such as Alzheimer's at the molecular level. To this end, this paper investigates the problem of protein conformation prediction under the constraint of avoiding high-entropy-loss routes during folding. Using the well-established kinetostatic compliance (KCM)-based nonlinear dynamics of a protein molecule, this paper formulates the protein conformation prediction as a pointwise optimal control synthesis problem cast as a quadratic program (QP). It is shown that the KCM torques in the protein folding literature can be utilized for defining a reference vector field for the QP-based control generation problem. The resulting kinetostatic control torque inputs will be close to the KCM-based reference vector field and guaranteed to be constrained by a predetermined bound; hence, high-entropy-loss routes during folding are avoided while the energy of the molecule is decreased.

Abstract: This paper investigates the problem of straight-line path following for magnetic helical microswimmers. The control objective is to make the helical microswimmer to converge to a straight line without violating the step-out frequency constraint. The proposed feedback control solution is based on an optimal decision strategy (ODS) that is cast as a trust-region subproblem (TRS), i.e., a quadratic program over a sphere. The ODS-based control strategy minimizes the difference between the microrobot velocity and an integral line-of-sight (ILOS)-based reference vector field while respecting the magnetic saturation constraints and ensuring the absolute continuity of the control input. Due to the embedded integral action in the reference vector field, the microswimmer will follow the desired straight line by compensating for the drift effect of the environmental disturbances as well as the microswimmer weight.

Abstract: This letter presents a method of designing relative-degree-two nonholonomic virtual constraints (NHVCs) that allow for stable bipedal robotic walking across variable terrain slopes. Relative-degree-two NHVCs are virtual constraints that encode velocity-dependent walking gaits via momenta conjugate to the unactuated degrees of freedom for the robot. We recently introduced a systematic method of designing NHVCs, based on the hybrid zero dynamics (HZD) control framework, to achieve hybrid invariant flat ground walking without the use of dynamic reset variables. This work addresses the problem of walking over variable-inclined terrain disturbances. We propose a methodology for designing NHVCs, via an optimization problem, in order to achieve stable walking across variable terrain slopes. The end result is a single controller capable of walking over variable-inclined surfaces, that is also robust to inclines not considered in the optimization design problem, and uncertainties in the inertial parameters of the model.

Abstract: This paper proposes an extremum seeking controller (ESC) for simultaneously tuning the feedback control gains of a knee-ankle powered prosthetic leg using continuous-phase controllers. Previously, the proportional gains of the continuousphase controller for each joint were tuned manually by trialand- error, which required several iterations to achieve a balance between the prosthetic leg tracking error performance and the user's comfort. In this paper, a convex objective function is developed that incorporates these two goals. The developed cost function is then minimized by ESC in real-time to simultaneously tune the proportional gains of the knee and the ankle joints. The optimum of the objective function shifts at different walking speeds, and our algorithm is suitably fast to track these changes, providing real-time adaptation for different walking conditions. Benchtop and walking experiments verify the effectiveness of the proposed ESC across various walking speeds.

Abstract: Achieving coordinated motion between transfemoral amputee patients and powered prosthetic joints is of paramount importance for powered prostheses control. In this article we propose employing an algebraic curve representation of nominal human walking data for powered knee prosthesis controller design. The proposed algebraic curve representation encodes the desired holonomic relationship between the human and the powered prosthetic joints with no dependence on joint velocities. For an impedance model of the knee joint motion driven by the hip angle signal, we create a continuum of equilibria along the gait cycle using a variable impedance scheme. Our variable impedance-based control law, which is designed using the parameter-dependent Lyapunov function framework, realizes the coordinated hip-knee motion with a family of spring and damper behaviors that continuously change along the human-inspired algebraic curve. In order to accommodate variability in the user's hip motion, we propose a computationally efficient radial projection-based algorithm onto the human-inspired algebraic curve in the hip-knee plane.

Abstract: The Kalman filter is optimal with respect to minimum mean square error (MMSE) if the process noise and measurement noise are Gaussian. However, the Kalman filter is suboptimal in the presence of non-Gaussian noise. The maximum correntropy criterion Kalman filter (MCCKF) is a Kalman-type filter that uses the correntropy measure as its optimality criterion instead of MMSE. In this paper, we modify the correntropy gain in the MCC-KF to obtain a new filter that we call the measurement-specific correntropy filter (MSCF). The MSCF uses a matrix gain rather than a scalar gain to provide better selectivity in the way that it handles the innovation vector. We analytically compare the performance of the Kalman filter with that of the MSCF when either the measurement or process noise covariance is unknown. For each of these situations, we analyze two mean square errors (MSEs): the filter-calculated MSE (FMSE) and the true MSE (TMSE). We show that the FMSE of the Kalman filter is less than that of the MSCF. However, the TMSE of the Kalman filter is greater than that of the MSCF under certain conditions. Illustrative examples are provided to verify the analytical results.

Abstract: This letter investigates the hybrid zero dynamics for planar bipedal robots with one degree of underactuation subject to nonholonomic virtual constraints (NHVC). We first derive the closed form expression of the bipedal robot zero dynamics under NHVCs. We next present conditions that make the NHVCs invariant with respect to rigid impacts with the ground. Lastly, a reduced dimensionality test, which is independent of the number of degrees of freedom of the bipedal robot, is proposed for checking existence and exponential stability of hybrid periodic orbits under NHVCs. Simulation results using the RABBIT biped robot demonstrate the robustness of the proposed NHVCs against a randomized horizontal push disturbance. A statistical significant difference between the mean number of steps until failure is shown between the NHVC and VHC control schemes.

Abstract: Developing methods for autonomous landing of an unmanned aerial vehicle (UAV) on a mobile platform has been an active area of research over the past decade, as it offers an attractive solution for cases where rapid deployment and recovery of a fleet of UAVs, continuous flight tasks, extended operational ranges, and mobile recharging stations are desired. In this work, we present a new autonomous landing method that can be implemented on micro UAVs that require high-bandwidth feedback control loops for safe landing under various uncertainties and wind disturbances. We present our system architecture, including dynamic modeling of the UAV with a gimbaled camera, implementation of a Kalman filter for optimal localization of the mobile platform, and development of model predictive control (MPC), for guidance of UAVs. We demonstrate autonomous landing with an error of less than 37 cm from the center of a mobile platform traveling at a speed of up to 12 m/s under the condition of noisy measurements and wind disturbances.

Abstract: This article investigates the problem of enforcing a virtual holonomic constraint (VHC) on a mechanical system with degree of underactuation one while simultaneously stabilizing a closed orbit on the constraint manifold. This problem, which to date is open, arises when designing controllers to induce complex repetitive motions in robots. In this paper, we propose a solution which relies on the parameterization of the VHC by the output of a double integrator. While the original control inputs are used to enforce the VHC, the control input of the double-integrator is designed to asymptotically stabilize the closed orbit and make the state of the double-integrator converge to zero. The proposed design is applied to the problem of making a PVTOL aircraft follow a circle on the vertical plane with a desired speed profile, while guaranteeing that the aircraft does not roll over for suitable initial conditions.

Abstract: Estimation of unknown inputs and/or disturbances has been a topic of constant interest in the control engineering for the past several decades. Disturbance observers (DOB) are a special class of unknown input observers that were introduced for robust motion control applications in the early 1980s and have found numerous industrial applications since then. In this article, a type of nonlinear disturbance observer (NDOB) structure is investigated and some properties of this NDOB class such as semi/quasi-passivity property, which have not been discussed in the previous NDOB literature, are presented. We show that a previous NDOB, which was first proposed for serial robotic manipulators, can be used for the more general class of Euler-Lagrange systems. Moreover, we show how the proposed NDOB can be used along with well-established control schemes such as passivity-based control. Finally, the proposed NDOBs are incorporated into the framework of the 4-channel teleoperation in order to achieve full transparency in the presence of unknown disturbances.

Abstract: This paper investigates a class of Lagrangian control systems with n degrees-of-freedom (DOF) and n–1 actuators, assuming that n–1 virtual holonomic constraints have been enforced via feedback, and a basic regularity condition holds. The reduced dynamics of such systems are described by a second-order unforced differential equation. We present necessary and sufficient conditions under which the reduced dynamics are those of a mechanical system with one DOF and, more generally, under which they have a Lagrangian structure. In both cases, we show that typical solutions satisfying the virtual constraints lie in a restricted class which we completely characterize.

Abstract: This paper investigates the problem of maneuvering control for planar snake robots. The control objective is to make the center of mass of the snake robot converge to a desired path and traverse the path with a desired velocity. The proposed feedback control strategy enforces virtual constraints encoding a lateral undulatory gait, parametrized by the states of dynamic compensators used to regulate the orientation and forward speed of the snake robot.

Abstract: This paper investigates the problem of planar maneuvering control for bio-inspired underwater snake robots that are exposed to un known ocean currents. The control objective is to make a neutrally buoyant snake robot which is subject to hydrodynamic forces and ocean currents converge to a desired planar path and traverse the path with a desired velocity. The proposed feedback control strategy enforces virtual constraints which encode biologically inspired gaits on the snake robot configuration. The virtual constraints, parametrized by states of dynamic compensators, are used to regulate the orientation and forward speed of the snake robot. A two-state ocean current observer based on relative velocity sensors is proposed. It enables the robot to follow the path in the presence of unknown constant ocean currents. The efficacy of the proposed control algorithm for several biologically inspired gaits is verified both in simulations for different path geometries and in experiments.

Abstract: Robotic manipulators are highly nonlinear and coupled systems that are subject to different types of disturbances such as joint frictions, unknown payloads, varying contact points, and unmodeled dynamics. These disturbances, when unaccounted for, adversely affect the performance of the manipulator. Employing a disturbance observer is a common method to reject such disturbances. In addition to disturbance rejection, disturbance observers can be used in force control applications. Recently, research has been done regarding the design of nonlinear disturbance observers (NLDOs) for robotic manipulators. In spite of good results in terms of disturbance tracking, the previously designed nonlinear disturbance observers can merely be used for planar serial manipulators with revolute joints [Chen, W. H., Ballance, D. J., Gawthorp, P. J., O'Reilly, J. (2000). A nonlinear disturbance observer for robotic manipulators. IEEE Transactions on Industrial Electronics, 47 (August (4)), 932–938; Nikoobin, A., Haghighi, R. (2009). Lyapunov-based nonlinear disturbance observer for serial n-link manipulators. Journal of Intelligent & Robotic Systems, 55 (July (2–3)), 135–153]. In this paper, a general systematic approach is proposed to solve the disturbance observer design problem for robotic manipulators without restrictions on the number of degrees-of-freedom (DOFs), the types of joints, or the manipulator configuration. Moreover, this design method does not need the exact dynamic model of the serial robotic manipulator. This method also unifies the previously proposed linear and nonlinear disturbance observers in a general framework. Simulations are presented for a 4-DOF SCARA manipulator to show the effectiveness of the proposed disturbance observer design method. Experimental results using a PHANToM Omni haptic device further illustrate the effectiveness of the design method.

Abstract: Teleoperation systems are subject to different types of disturbances. Such disturbances, when unaccounted for, may cause poor performance and even instability of the teleoperation system. This study presents a novel non-linear bilateral control scheme using the concept of `disturbance observer-based control` for non-linear teleoperation systems. Lumping the effects of dynamic uncertainties and external disturbances into a single disturbance term enables us to design a disturbance observer to suppress these disturbances and alleviate their adverse effects on the teleoperation system. A disturbance observer-based control law is proposed for non-linear teleoperation systems which will guarantee global asymptotic force tracking and global exponential position and disturbance tracking when the bilateral teleoperation system is experiencing slow-varying disturbances. In the case of fast-varying disturbances, the tracking errors are shown to be globally uniformly ultimately bounded, with an ultimate bound that can be made as small as desired using the design parameters. Simulations are presented to show the effectiveness of the proposed approach.

Abstract: This paper investigates development of an efficient numerical integrator for forward dynamics simulation of the protein folding process, where protein molecules are modeled as robotic mechanisms consisting of rigid nano-linkages with many degrees-of-freedom. To address the computational burden associated with fixed step-size explicit Euler methods, we develop a fast numerical scheme with an adaptive step-size strategy for computing the folding pathway of protein molecules.

Abstract: This paper proposes a sign gradient descent (SGD) algorithm for predicting the three-dimensional folded protein molecule structures under the kinetostatic compliance method (KCM). In the KCM framework, which can be used to simulate the range of motion of peptide-based nanorobots/nanomachines, protein molecules are modeled as a large number of rigid nano-linkages that form a kinematic mechanism under motion constraints imposed by chemical bonds while folding under the kinetostatic effect of nonlinear interatomic force fields. In a departure from the conventional successive kinetostatic fold compliance framework, the proposed SGD-based iterative algorithm in this paper results in convergence to the local minima of the free energy of protein molecules corresponding to their final folded conformations in a faster and more robust manner. KCM-based folding dynamics simulations of the backbone chains of protein molecules demonstrate the effectiveness of the proposed algorithm.

Abstract: Increasingly, instructors are challenged by growing complexity in knowledge domains and the need to prepare students with specific skills relevant to an uncertain future. The speed of technological advance and shifting societal conditions make this ever more arduous. One of the promises of project-based learning (PBL) is to cultivate many of the most important student qualities for facing such an uncertain world by exposing them to cross disciplinary problems. Indeed, providing the students with a plethora of perspectives from seemingly unrelated fields enhances their creative problem solving skills and enables them to better adapt to complex scenarios. This paper describes a multidisciplinary effort between faculty from the Electrical and Computer Engineering department at the University of Michigan-Dearborn and the Department of Chemistry and Biochemistry at the Worcester Polytechnic Institute (WPI). The project involved students modeling protein folding as a robotic mechanism and studying the problems associated with this complex system from multiple perspectives. After providing a brief technical background about the robotics-based approaches to the problem of protein folding/unfolding, this paper elaborates on the pedagogical elements of the project. Assessment results highlight the student learning outcomes and perspectives on this interdisciplinary, and intercollegiate project-based learning endeavor. The authors comment on challenges and opportunities associated with such PBL efforts and provide suggestions for disseminating these types of impactful PBL initiatives.

Abstract: The safety-critical nature of vehicle steering is one of the main motivations for exploring the space of possible cyber-physical attacks against the steering systems of modern vehicles. This paper investigates the adversarial capabilities for destabilizing the interaction dynamics between human drivers and vehicle haptic shared control (HSC) steering systems. In contrast to the conventional robotics literature, where the main objective is to render the human-automation interaction dynamics stable by ensuring passivity, this paper takes the exact opposite route. In particular, to investigate the damaging capabilities of a successful cyber-physical attack, this paper demonstrates that an attacker who targets the HSC steering system can destabilize the interaction dynamics between the human driver and the vehicle HSC steering system through synthesis of time-varying impedance profiles. Specifically, it is shown that the adversary can utilize a properly designed non-passive and time-varying adversarial impedance target dynamics, which are fed with a linear combination of the human driver and the steering column torques. Using these target dynamics, it is possible for the adversary to generate in real-time a reference angular command for the driver input device and the directional control steering assembly of the vehicle. Furthermore, it is shown that the adversary can make the steering wheel and the vehicle steering column angular positions to follow the reference command generated by the time-varying impedance target dynamics using proper adaptive control strategies. Numerical simulations demonstrate the effectiveness of such time-varying impedance attacks, which result in a non-passive and inherently unstable interaction between the driver and the HSC steering system.

Abstract: This paper investigates the problem of prediction of protein molecule folding pathways under entropy-loss constraints by formulating a control synthesis problem whose solutions are obtained by solving large-scale quadratic programming (QP) optimizations with nonlinear constraints. The utilized non-iterative and computationally efficient algorithm, which is based on solving generalized eigenvalue problems, prevents an unpredictable and potentially large number of iterations at each protein conformation for computing the folding control inputs. The synthesized control inputs remain close to the renowned kinetostatic compliance method (KCM) reference vector field while satisfying proper quadratic inequality constraints that limit the rate of molecule entropy-loss during folding.

Abstract: There are a variety of ways, such as reflashing of targeted electronic control units (ECUs) to hijacking the control of a fleet of wheeled mobile robots, through which adversaries can execute attacks on the actuators of mobile robots and autonomous vehicles. Independent of the source of cyber-physical infiltration, assessing the physical capabilities of an adversary who has made it to the last stage and is directly controlling the cyber-physical system actuators is of crucial importance. This paper investigates the potentials of an adversary who can directly manipulate the traction dynamics of wheeled mobile robots and autonomous vehicles but has a very limited knowledge of the physical parameters of the traction dynamics. It is shown that the adversary can exploit a new class of closed-loop attack policies that can be executed against the traction dynamics leading to wheel lock conditions. In comparison with a previously proposed wheel lock closed-loop attack policy, the attack policy in this paper relies on less computations and knowledge of the traction dynamics. Furthermore, the proposed attack policy generates smooth actuator input signals and is thus harder to detect. Simulation results using various tire-ground interaction conditions demonstrate the effectiveness of the proposed wheel lock attack policy.

Abstract: We propose a general mobile robot velocity and distance control framework that relies on using the Keitz irradiance-flux equation, which relates the measured irradiance and the output power of far-UVC double-light fixture, along with an exponential decay model for evaluating the survival of viruses and pathogens subjected to far-UVC exposure. Our mathematical framework finds the optimal distance from the vertical surfaces. Given a moving rover base, we solve a suitable optimal control problem to derive the optimal velocity profile of the mobile robot for achieving efficient disinfection in terms of disinfection time and energy expenditure.

Abstract: This short paper investigates the problem of curved path following for helical microswimmers actuated by rotating magnetic dipoles. The proposed solution, which relies on an integral line-of-sight (ILOS) guidance law, can be utilized in both below and beyond step-out frequency regimes.

Abstract: With the global coronavirus pandemic still persisting, the repeated disinfection of large spaces and small rooms has become a priority and matter of focus for researchers and developers. The use of ultraviolet light (UV) for disinfection is not new; however, there are new efforts to make the methods safer, more thorough, and automated. Indeed, continuous very low dose-rate far-UVC light in indoor public locations is a promising, safe and inexpensive tool to reduce the spread of airborne-mediated microbial diseases. This paper investigates the problem of disinfecting surfaces using autonomous mobile robots equipped with UV light towers. In order to demonstrate the feasibility of our autonomous disinfection framework, we also present a teleoperated robotic prototype. It consists of a robotic rover unit base, on which two separate UV light towers carrying 254 nm UVC and 222 nm far-UVC lights are mounted. It also includes a live-feed camera for remote operation, as well as power and communication electronics for the remote operation of the UV lamps. The 222 nm far-UVC light has been recently shown to be non-inflammatory and non-photo carcinogenic when radiated on mammalian skin, while still sterilizing the coronavirus on irradiated surfaces. With far-UVC light, disinfection robots may no longer require the evacuation of spaces to be disinfected. The robot demonstrates promising disinfection performance and potential for future autonomous applications.

Abstract: Motivated by ample evidence in the automotive cybersecurity literature that the car brake ECUs can be maliciously reprogrammed, it has been shown that an adversary who can directly control the frictional brake actuators can induce wheel lockup conditions despite having a limited knowledge of the tire-road interaction characteristics [1]. In this paper, we investigate the destabilizing effect of such wheel lockup attacks on the lateral motion stability of vehicles from a robust stability perspective. Furthermore, we propose a quadratic programming (QP) problem that the adversary can solve for finding the optimal destabilizing longitudinal slip reference values.

Abstract: Recent automotive hacking incidences have demonstrated that when an adversary manages to gain access to a safety-critical CAN, severe safety implications will ensue. Under such threats, this paper explores the capabilities of an adversary who is interested in engaging the car brakes at full speed and would like to cause wheel lockup conditions leading to catastrophic road injuries. This paper shows that the physical capabilities of a CAN attacker can be studied through the lens of closed-loop attack policy design. In particular, it is demonstrated that the adversary can cause wheel lockups by means of closed-loop attack policies for commanding the frictional brake actuators under a limited knowledge of the tire-road interaction characteristics. The effectiveness of the proposed wheel lockup attack policy is shown via numerical simulations under different road conditions.

Abstract: There is ample evidence in the automotive cybersecurity literature that the car brake ECUs can be maliciously reprogrammed. Motivated by such threat, this paper investigates the capabilities of an adversary who can directly control the frictional brake actuators and would like to induce wheel lockup conditions leading to catastrophic road injuries. This paper demonstrates that the adversary despite having a limited knowledge of the tire-road interaction characteristics has the capability of driving the states of the vehicle traction dynamics to a vicinity of the lockup manifold in a finite time by means of a properly designed attack policy for the frictional brakes. This attack policy relies on employing a predefined-time controller and a nonlinear disturbance observer acting on the wheel slip error dynamics. Simulations under various road conditions demonstrate the effectiveness of the proposed attack policy.

Abstract: This paper presents an innovative robotic mechanism for generating peristaltic motion for robotic locomotion systems. The designed underactuated peristaltic robot utilizes a minimum amount of electromechanical hardware. Such a minimal electromechanical design not only reduces the number of potential failure modes but also provides the robot design with great potential for scaling to larger and smaller applications. We performed several speed and force generation tests atop a variety of granular media. Our experiments show the effective design of robot mechanism where the robot can travel with a small input power (1.14W) at 6.0 mm/sec with 2.45 N force atop sand.

Abstract: Autonomous landing of micro unmanned aerial vehicles (UAVs) on moving targets has the potential to resolve many limitations of small-scale UAVs, such as uninterrupted flight tasks, rapid deployment and recovery of multiple UAVs, and extended operational ranges through mobile recharging stations. In this work, we present and experimentally verify a new vision-based method that enables a micro UAV to land autonomously on a mobile landing platform. Our method, which can be implemented on small-scale UAVs with limited payload capabilities and computational resources, incorporates model predictive control, vision-based localization, and extended Kalman filter for path following, navigation, and guidance. Our method uses a closed-loop controlled gimbaled camera for visual navigation and relative localization of the landing platform, a sensor fusion technique based on extended Kalman filters for target localization, and a model predictive control scheme for autonomous landing of the UAV under system uncertainties and wind disturbances. We demonstrate flight experiments of autonomous landing with an average error of 39 cm from the center of a mobile platform.

Abstract: In this paper, we propose a deep neural network-based feature learning methodology for fault diagnosis in electromechanical systems such as rotary machines. In order to classify faults in bearings and gears, an unsupervised algorithm, which trains one layer of a deep neural network at a time and minimizes the reconstruction error, is used for feature learning. To validate our approach, the proposed method is applied to two datasets for bearing and gearbox faults. Using the proposed method, we achieved fault classification accuracies of up to 99% and 96% for bearing and gearbox faults, respectively. Three different computational hardware platforms are used to validate the efficiency of real-time implementation of our proposed algorithm.

Abstract: Conventional perturbation-based extremum seeking control (ESC) employs a slow time-dependent periodic signal to find an optimum of an unknown plant. To ensure stability of the overall system, the ESC parameters are selected such that there is sufficient time-scale separation between the plant and the ESC dynamics. This approach is suitable when the plant operates at a fixed time-scale. In case the plant slows down during operation, the time-scale separation can be violated. As a result, the stability and performance of the overall system can no longer be guaranteed. In this paper, we propose an ESC for periodic systems, where the external time-dependent dither signal in conventional ESC is replaced with the periodic signals present in the plant, thereby making ESC time-invariant in nature. The advantage of using a state-based dither is that it inherently contains the information about the rate of the rhythmic task under control. Thus, in addition to maintaining time-scale separation at different plant speeds, the adaptation speed of a time-invariant ESC automatically changes, without changing the ESC parameters. We illustrate the effectiveness of the proposed time-invariant ESC with a Van der Pol oscillator example and present a stability analysis using averaging and singular perturbation theory.

Abstract: Miniature blimps will have numerous applications in future smart cities. This paper presents the design of an autonomous blimp that can be autonomously operated and controlled. In order to be able to operate over long periods of time, the blimp design employs a novel actuation mechanism with only one servomotor and two DC motors. Experiments are carried out to demonstrate the capabilities of the constructed autonomous blimp.

Abstract: Autonomous bicycles offer numerous potentials for smart city applications thanks in part to their light weight, safe autonomy, being optionally manned, and last-mile delivery. This paper describes the design of a self-stabilizing autonomous bicycle with electric linear actuators. The high-speed linear actuator is mounted between the seat and the handlebar of the autonomous bicycle, which provides the bicycle with high peak power and energy efficiency. Physical tests are carried out to verify automatic steering and speed regulation capabilities of the autonomous bicycle.

Abstract: Riderless bicycles, which belong to the class of narrow autonomous vehicles, offer numerous potentials to improve living conditions in the smart cities of the future. Various obstacles exist in achieving full autonomy for this class of autonomous vehicles. One of these significant challenges lie within the synthesis of automatic control algorithms that provide self-balancing and maneuvering capabilities for this class of autonomous vehicles. Indeed, the nonlinear, underactuated, and non-minimum phase dynamics of riderless bicycles offer rich challenges for automatic control of these autonomous vehicles. In this paper, we report on implementing linear parameter varying (LPV)-based controllers for balancing our constructed autonomous bicycle, which is equipped with linear electric actuators for automatic steering, in the upright position. Experimental results demonstrate the effectiveness of the proposed control strategy.

Abstract: Having unified representations of human walking gait data is of paramount importance for wearable robot control. In the rehabilitation robotics literature, control approaches that unify the gait cycle of wearable robots are more appealing than the conventional approaches that rely on dividing the gait cycle into several periods, each with their own distinct controllers. In this article we propose employing algebraic curves to represent human walking data for wearable robot controller design. In order to generate algebraic curves from human walking data, we employ the 3L fitting algorithm, a tool developed in the pattern recognition literature for fitting implicit polynomial curves to given datasets. For an impedance model of the knee joint motion driven by the hip angle signal, we provide conditions by which the generated algebraic curves satisfy a robust relative degree condition throughout the entire walking gait cycle. The robust relative degree property makes the algebraic curve representation of walking gaits amenable to various nonlinear output tracking controller design techniques.

Abstract: Existence of disturbances in unknown environments is a pervasive challenge in robotic locomotion control. Disturbance observers are a class of unknown input observers that have been extensively used for disturbance rejection in numerous robotics applications. In this paper, we extend a class of widely-used nonlinear disturbance observers to underactuated bipedal robots, which are controlled using hybrid zero dynamics-based control schemes. The proposed hybrid nonlinear disturbance observer provides the autonomous biped robot control system with disturbance rejection capabilities, while the underlying hybrid zero-dynamics based control law remains intact.

Abstract: This paper investigates the problem of straight-line path following for planar swimming magnetic helical microrobots. The control objective is to make the microrobot to converge to a straight line without violating the input saturation limits. The proposed feedback control solution is based on an optimal decision strategy (ODS) that is cast as a quadratic program. The ODS-based control law minimizes the difference between the microrobot velocity and a line-of-sight (LOS)-based reference vector field while respecting the input saturation constraints.

Abstract: Hybrid zero dynamics-based control is a promising framework for controlling underactuated biped robots and powered prosthetic legs. In this control paradigm, stable walking gaits are implicitly encoded in polynomial output functions of the robot configuration variables, which are to be zeroed via feedback. The biped output functions are parameterized by a suitable mechanical phasing variable whose evolution determines the biped gait progression during each step. Determining a proper phase variable, however, might not always be a trivial task. In this paper, we present a method for generating output functions from given parametric walking gaits without any explicit knowledge of the phase variables. Our elimination method is based on computing the resultant of polynomials, an algebraic tool widely used in computer algebra.

Abstract: State-of-art powered prosthetic legs are often controlled using a collection of joint impedance controllers designed for different phases of a walking cycle. Consequently, finite state machines are used to control transitions between different phases. This approach requires a large number of impedance parameters and switching rules to be tuned. Since one set of control parameters cannot be used across different amputees, clinicians spend enormous time tuning these gains for each patient. This paper proposes a virtual constraint-based control scheme with a smaller set of control parameters, which are automatically tuned in real-time using an extremum seeking controller (ESC). ESC, being a model-free control method, assumes no prior knowledge of either the prosthesis or human. Using a singular perturbation analysis, we prove that the virtual constraint tracking errors are small and the PD gains remain bounded. Simulations demonstrate that our ESC-based method is capable of adapting the virtual-constraint based control parameters for amputees with different masses.

Abstract: This talk proposes a virtual constraint-based control scheme with a smaller set of control parameters, which are automatically tuned in real-time using an extremum seeking control (ESC)-based scheme. ESC, being a model-free control method, assumes no a priori knowledge of either the powered prosthesis or human. These advantageous factors make ESC a suitable candidate for automatic tuning of powered prosthetic leg control system parameters, with a minimal need for clinicians’ intervention.

Abstract: Virtual holonomic constraints (VHCs) framework is a recent control paradigm for systematic design of motion controllers for wheel-less biologically inspired snake robots. Despite recent developments for VHC-based control systems for ground and underwater robotic snakes, they employ only two families of propulsive virtual holonomic constraints, i.e., lateral undulatory and eel-like virtual constraints. In this paper we extend the family of propulsive virtual constraints that can be used with VHC-based controllers by presenting a VHC analysis and synthesis methodology for planar snake robots that are subject to ground friction forces. In particular, we present a nonlinear differential inequality that guarantees forward motion of planar snake robots under the influence of VHCs. Furthermore, we provide a family of hyperbolic partial differential equations that can be employed to generate propulsive virtual holonomic constraints for these biologically inspired robots. Simulations are presented to verify the proposed analysis/synthesis methodology.

Abstract: Bearing faults are one of the main reasons for rotary machine failure. Monitoring bearing vibration signals is an effective method for diagnosing faults and preventing catastrophic failures in rotary mechanisms. The state-of-the-art vibration monitoring algorithms are mainly based on frequency or time-frequency domain analysis of rotary machines that are operating in steady state. However, the steady state assumption is not valid in applications where the loads and speeds are time-varying. Finding a method for capturing the variability in vibration signals, which are caused by varying loads and speeds, is still an open research problem with potentially many applications in emerging areas such as electric vehicles. In this paper, we address the problem of vibration signal monitoring by applying a feature extraction algorithm to rotary machine signals measured by accelerometers. The proposed method, which is based on the wavelet scattering transform, achieves overall high accuracy while being computationally affordable for real-time implementation purposes. In order to verify the effectiveness of the proposed methodology, we apply our technique to a well-known vibration benchmark dataset with variable load. Our algorithm can diagnose various faults with different intensities with an average accuracy of 99% and thus effectively outperforming all prior reported work on this dataset.

Abstract: This paper investigates the problem of direction following for planar snake robots. The control objective is to regulate the linear velocity vector of the snake robot to a constant reference while guaranteeing boundedness of the system states. The proposed feedback control strategy enforces virtual constraints encoding a lateral undulatory gait, parametrized by states of dynamic compensators used to regulate the orientation and forward speed of the snake robot.

Abstract: This paper considers direction following control of planar snake robots for which the equations of motion are described based on a simplified model. In particular, we aim to regulate the orientation and the forward velocity of the robot to a constant vector, while guaranteeing the boundedness of the states of the controlled system. To this end, we first stabilize a constraint manifold for the fully-actuated body shape variables of the robot. The definition of the constraint manifold is inspired by the well-known reference joint angle trajectories which induce lateral undulatory motion for snake robots. Subsequently, we reduce the dynamics of the system to the invariant constraint manifold. Furthermore, we design two dynamic compensators which control the orientation and velocity of the robot on this manifold. Using numerical analysis and a formal stability proof, we show that the solutions of the dynamic compensators remain bounded. Numerical simulations are presented to validate the theoretical design.

Abstract: This paper investigates a class of Lagrangian control systems with n degrees-offreedom (DOF) and n– 1 actuators, assuming that n– 1 virtual holonomic constraints have been enforced via feedback, and a basic regularity condition holds. The reduced dynamics of such systems are described by a second-order unforced differential equation. We present necessary and sufficient conditions under which the reduced dynamics are those of a mechanical system with one DOF and, more generally, under which they have a Lagrangian structure.

Abstract: Instability and poor performance are two wellknown problems encountered in bilateral teleoperation over a communication channel with variable time delays, where force feedback from the slave side is provided to the master side. When unknown disturbances or external forces act on the master and/or the slave manipulators, the teleoperation system will be even more prone to stability and performance degradation. By adopting a Lyapunov approach, we present a novel nonlinear disturbance observer based control scheme for teleoperation systems that are subject to variable time delays and disturbances. Lumping the effects of dynamic uncertainties, unknown forces/torques exerted by the human operator and the remote environment, and external disturbances into a single disturbance term enables us to use a disturbance observer and suppress these disturbances in order to alleviate their adverse effects on the teleoperation system stability and performance. The proposed disturbance observer based control laws guarantee asymptotic disturbance tracking, asymptotic position tracking, and stability of teleoperation system in both constrained and free motions. Experimental results are presented to verify the effectiveness of the proposed approach.

Abstract: Teleoperation systems, consisting of a pair of master and slave robots are subject to different types of disturbances such as joint frictions, varying contact points, unmodeled dynamics and unknown payloads. Such disturbances, when unaccounted for, cause poor teleoperation transparency and even instability. This paper presents a novel nonlinear bilateral control scheme, based on the concept of disturbance observer based control, to counter these disturbances and their negative effects on the teleoperation systems. The proposed disturbance observer based bilateral control law is able to achieve global asymptotic force tracking, and global exponential position and disturbance tracking in the presence of various disturbances. The minimum exponential convergence rate of the position and the disturbance tracking errors can be tuned by the controller parameters. Simulations are presented to show the effectiveness of the proposed control scheme.

Abstract: Robotic manipulators are highly nonlinear and coupled dynamic systems, which may be subject to different types of unknown disturbances such as joint frictions and end-effector external payloads. Such disturbances, when unaccounted for, cause poor tracking performance of the robot and may even destabilize the robot control system. In this paper we propose a novel nonlinear control scheme for robotic manipualtors subject to disturbances using the concept of disturbance observer-based control by modifying the disturbance observers proposed in [1] and [2]. The proposed control scheme and disturbance observer guarantee global asymptotic position and disturbance tracking and remove the previous restrictions on the number of degrees of freedom (DOFs), joint types, or manipulator configuration. Computer simulations are presented for a 4-DOF SCARA manipulator to show the effectiveness of the proposed disturbance observer-based control scheme.

Abstract: The PHANToM devices (SensAble Technologies Inc., MA, USA) provide the users in industry and academia with an opportunity for research and education in virtual reality, haptics, robot motion control and teleoperation. Traditionally, one has to develop C/C++ codes using the OpenHaptics R software development kit (SDK) in order to use these devices. The PHANSIM Toolkit is an academic/non-commercial Simulink toolkit for real-time motion control and teleoperation of the PHANToM haptic devices. This toolkit facilitates using the PHANToM haptic devices in Simulink. The usefulness of this toolkit is demonstrated through two illustrative experiments using two different types of PHANToM products, namely the Omni and the Premium 1.5 models.

Abstract: In this thesis we investigate virtual holonomic constraints (VHCs) for mechanical control systems. A VHC is a relation among the configuration variables of a mechanical system that does not physically exist, but can be emulated via feedback in a precise sense that is defined within this thesis. An example of VHC is the requirement that the end effector of a robot should only move along a plane. Over the past decade, VHCs have raised to prominence in robotics as they have been successfully employed to induce stable walking motions in biped robots. There is a growing body of evidence that, for complex motion control problems, the VHC paradigm might be more appropriate than the traditional reference tracking approach. Motivated by the hope to establish a new paradigm for complex motion control problems in robotics, this thesis makes contributions in two main directions. First, the thesis presents a complete theory employing VHCs for the stabilization of repetitive â behaviorsâ in underactuated mechanical control systems with degree of underactuation one. The theory in question has a number of components, the development of which takes us along a journey that includes, among other things, the solution of an inverse Lagrangian problem, the development of an algorithm to implicitize parametric constraints, and the stabilization of closed orbits by means of VHCs parametrized by states of dynamic compensators. The second direction of this thesis is the exploration of the hypothesis that the design of controllers enforcing VHCs might represent a viable paradigm for complex motion control problems. To this end, we solve a challenging open problem in snake robotics: make a planar snake approach and follow a path with desired speed.

Abstract: Employing disturbance observers is an effective way of enhancing the stability and performance of the control systems subject to disturbances. Disturbance observers have been extensively used for control of mechatronic systems since their introduction in the 1980s.
This thesis studies the design of nonlinear disturbance observers for robotic manipulators and their applications in the control of telerobotic systems. A novel framework is introduced, based on linear matrix inequalities, for the design of nonlinear disturbance observers for serial robotic manipulators. This design method removes the restrictions encountered in previous design methods. In spite of the many applications of the disturbance observers in mechatronic systems, there are few studies that address the design of such observers for telerobotic systems. Moreover, these studies cannot guarantee the stability of telerobotic systems with time delay. This thesis presents a rigorous theoretical basis for the disturbance observer based control of telerobotic systems with variable time delays.