Abstract-In this paper, a novel control scheme is developed for a teleoperation system, combining the radial basis function (RBF) neural networks (NNs) and wave variable technique to simultaneously compensate for the effects caused by communication delays and dynamics uncertainties. The teleoperation system is set up with a TouchX joystick as the master device and a simulated Baxter robot arm as the slave robot. The haptic feedback is provided to the human operator to sense the interaction force between the slave robot and the environment when manipulating the stylus of the joystick. To utilize the workspace of the telerobot as much as possible, a matching process is carried out between the master and the slave based on their kinematics models. The closed loop inverse kinematics (CLIK) method and RBF NN approximation technique are seamlessly integrated in the control design. To overcome the potential instability problem in the presence of delayed communication channels, wave variables and their corrections are effectively embedded into the control system, and Lyapunov based analysis is performed to theoretically establish the closed-loop stability. Comparative experiments have been conducted for a trajectory tracking task, under the different conditions of various communication delays. Experimental results show that in terms of tracking performance and force reflection, the proposed control approach shows superior performance over the conventional methods.

This paper studies both indirect and direct global neural control of strict-feedback systems in the presence of unknown dynamics, using the dynamic surface control (DSC) technique in a novel manner. A new switching mechanism is designed to combine an adaptive neural controller in the neural approximation domain, together with the robust controller that pulls the transient states back into the neural approximation domain from the outside. In comparison with the conventional control techniques, which could only achieve semiglobally uniformly ultimately bounded stability, the proposed control scheme guarantees all the signals in the closed-loop system are globally uniformly ultimately bounded, such that the conventional constraints on initial conditions of the neural control system can be relaxed. The simulation studies of hypersonic flight vehicle (HFV) are performed to demonstrate the effectiveness of the proposed global neural DSC design.

In this paper, a neural networks (NNs) enhanced telerobot control system is designed and tested on a Baxter robot. Guaranteed performance of the telerobot control system is achieved at both kinematic and dynamic levels. At kinematic level, automatic collision avoidance is achieved by the control design at the kinematic level exploiting the joint space redundancy, thus the human operator would be able to only concentrate on motion of robot's end-effector without concern on possible collision. A posture restoration scheme is also integrated based on a simulated parallel system to enable the manipulator restore back to the natural posture in the absence of obstacles. At dynamic level, adaptive control using radial basis function NNs is developed to compensate for the effect caused by the internal and external uncertainties, e.g., unknown payload. Both the steady state and the transient performance are guaranteed to satisfy a prescribed performance requirement. Comparative experiments have been performed to test the effectiveness and to demonstrate the guaranteed performance of the proposed methods.

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Sliding mode control (SMC) is an attractive for nonlinear systems due to its invariance for both parametric and nonparametric uncertainties. However, the invariance of SMC is not guaranteed in a reaching phase. Integral SMC (ISMC) eliminates the reaching phase such that the invariance is achieved in an entire system response. To reduce chattering in ISMC, it was suggested that the switching element is smoothed by using a low-pass filter and an integral sliding variable is modified. This study discusses several crucial problems regarding the performance, modification, and improvement of ISMC. Firstly, the modification of the integral sliding variable is revealed to be unnecessary as it degrades the performance of a sliding phase; secondly, ISMC is shown to be a kind of global SMC; thirdly, it is manifested thata high-order ISMC design with super twisting involves in a stability condition that may be infeasible in theory; finally, an efficient solution is suggested to attenuate chattering in ISMC without the degradation of tracking accuracy and the solution is extended to the case with uncertain control gain functions. Comprehensive simulation results have verified the arguments of this study.

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In this paper, an admittance control scheme is proposed for physical human-robot interaction with human subject's intention motion as well as dynamic uncertainties of the robotic exoskeleton. Human subject's intention motion is represented by the reference trajectory when the exoskeleton manipulator is complying with the external interaction force. Online estimation of the stiffness is employed to deal with the variable impedance property of the exoskeleton manipulator. An admittance control approach is firstly presented based on the measurable force in order to generate a differentiable reference trajectory in interaction tasks. Then a stability criterion can be obtained due to the proposed control method. The designed controller includes linearly parameterization and estimation for the unknown items of the dynamics. Bounded and convergent error is shown in the tracking process while the robustness of the variable stiffness control method is guaranteed. The control approach is then verified on a robotic exoskeleton interacting with human via experiments. The results show that the presented approach can make for an effective pHRI performance.