Hai Wang

Autonomous mobile robots have been applied in many industries, but fast and robust trajectory tracking control remains a major challenge. This paper investigates a predefined-time trajectory tracking problem of a Mecanum-wheeled omnidirectional mobile robot (MWOMR) under the conditions of parameter uncertainties and external disturbances. First, a novel predefined-time stable system with adjustable convergence rate is proposed. Second, a predefined-time sliding mode control scheme is developed, which has better convergence performance and smaller control input. Based on Lyapunov stability theory, the effectiveness of the scheme is proven. The numerical simulation results show that the compared with traditional predefined-time control schemes, the proposed scheme has the advantages of a shorter convergence time and smaller control input.

This paper investigates the issue of active fault tolerant control (AFTC) for manipulator with actuator faults. First, a higher-order sliding mode (HOSM) observer for fault diagnosis is constructed to estimate unmeasured states and compensate for the actuator faults simultaneously. Then, a feedback fault tolerant control (FTC) strategy is proposed to guarantee system's convergence in finite time and to reduce chattering phenomena based on the nonsingular fast terminal sliding mode (NFTSM) and barrier function-based adaptive super twisting (BFAST) algorithm. The finite time stability of the proposed FTC scheme is demonstrated by Lyapunov theory. Finally, simulations are performed to demonstrate the effectiveness of the proposed control method.

Permanent magnet linear synchronous motor (PMLSM) is widely used in robot systems. Nonsingular terminal sliding mode control based on robust compensator was proposed to improve position tracking ability and anti-interference ability of PMLSM to improve the system control performance. Firstly, the sliding mode controller was designed by using nonsingular terminal sliding mode, which ensures that the system has good control accuracy and faster convergence speed. Then, the super twisting algorithm was used to construct the auxiliary sliding mode function, and deigned the robust compensator. The controller handles the uncertainty of the system by smoothing the control input, and ensures that the system can reach a stable state in a limited time. Finally, the model is built and verified by simulation. The comparison results showed that the controller has high tracking performance and strong anti-interference ability, which can significantly reduce the position tracking error of the system and weaken the chattering phenomenon of sliding mode.

For the second order nonlinear system, a robust intelligent tracking control scheme is addressed in the presence of system uncertainties. Considering the dynamics with system uncertainties, the robust neural control is designed to obtain robust tracking performance, where a switching mechanism is employed to achieve the coordination between robust design and composite neural learning. To reduce the sliding mode chattering of terminal sliding mode controller (TSMC), the adaptive recursive integral TSMC (ARTSMC) is proposed, where the parameters of ARTSMC are online estimated by updating laws. Furthermore, the proposed method is applied to the dynamics of MEMS gyroscopes and simulations results are presented to verify that more accurate system tracking can be obtained.

In this paper, a new extended sliding mode observer for second-order linear systems is proposed. It is shown that, when the sampling period is small enough, the unmeasurable state estimation errors can be numerically estimated and then used for constructing the switching functions of the sliding mode observer. The advantages of the proposed new sliding mode observer are that (i) it has a solid theoretical background of stability and convergence from the view-point of the observer-like system; (ii) it can sufficiently approximate the observer-like system when the sampling period is small enough; (iii) the fast error convergence is good for practical applications. Simulation results are given in support of the advantages and effectiveness of proposed new sliding mode observer.

In this paper, a novel parameter estimation approach for manipulator system using the steady state sinusoidal angular position measurement in the frequency domain is proposed. The torque input has been designed to be a linear combination of sinusoidal. Since the output measurement will be dominated by its steady state after sufficient long time and the coefficients of the steady state sinusoidal can be extracted, in which contains all information of manipulator system dynamics. All unknown parameters within the system model can then be estimated based on the extracted coefficients in finite frequency bands. The simulations for a two-link manipulator are carried out to illustrate the effectiveness of the proposed method.

In this paper, the discrete time dynamics model of robotic fish is established by using data-assisted method, and a step-by-step coordination control strategy for two robotic fishes is investigated based on the model. Two steps are included in the control strategy, where the attitude is first adjusted to align the position of follower with the leader, and then, follows the position control to ensure the distance toward the leader. Furthermore, for achieving the fast convergence rate and robust position tracking performance, a discrete time integral terminal sliding mode controller is designed. The simulation results are demonstrated to verify the effectiveness and the excellent performance of the proposed control strategy.

In this paper, a discrete-time fast terminal sliding mode (DFTSM) control scheme with a discrete-time nonlinear disturbance observer (NDO) is developed for steer-by-wire (SBW) systems. It is shown that the proposed DFTSM controller can drive the dynamic error of the close-loop system to zero, and a discrete-time NDO can effectively cope with the lumped uncertainties and external disturbance. Compared with ordinary discrete-time sliding-mode-based SBW control systems, the proposed controller not only ensures the fast time zero convergence, but also achieves stronger robustness with respect to parameter uncertainties and external disturbance. The comparison simulations are conducted which verified the remarkable robustness and anti-disturbance ability.

In this paper, a robust discrete integral terminal sliding mode (DITSM) control strategy for the steer-by-wire (SBW) systems with uncertain dynamics is presented. Different from most of existing sliding-mode control strategies using continuous design method, this paper establishes the system model and designs the controller from a discrete perspective, and implements it in the data sampling system. Since the proposed control scheme adopts an integral-type nonlinear terminal sliding surface, it can not only enjoy the finite-time convergence of both the sliding variable and the error dynamics, but also significantly mitigate the system steady-state error. The proposed digital control strategy is developed based on the system's output feedback alone, whereas the knowledge on system states is not needed. The stability of the control system is demonstrated in theory. The simulation results are presented in support of the superior performance and effectiveness of the proposed control.

This paper proposes a novel extreme-learning-machine-based fast nonsingular terminal sliding mode (FNTSM) control strategy for permanent magnet linear motor (PMLM) with partially known dynamics. The proposed control strategy consists of two components: a FNTSM controller, and an estimator based on extreme learning machine (ELM), which is implemented to estimate the equivalent control of the FNTSM control. Compared with conventional sliding mode control implemented, the proposed control strategy not only assures the finite-time error convergence and strong robustness, but also requires no prior knowledge of the system parameters since the ELM is used to estimate the equivalent control in the process of designing controller. Numerical simulation results are given to verify excellent tracking performance of the proposed control strategy and show its advantages over some existing approaches.