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This paper presents a sliding mode control based on particle swarm optimization neural network and adaptive reaching law, and the proposed control method solves the problem of chattering and tracking performance degradation of a multi-joint manipulator caused by uncertainties such as external disturbances and modeling error. First, to address the problem that the precise dynamic system of the manipulator is difficult to establish, the radial basis function neural network (RBFNN) is used to approximate the uncertainty of the manipulator model, and the parameters of the neural network are optimized through the adaptive natural selection particle swarm optimization algorithm (ASelPSO) to improve the approximation ability and reduce the approximation error. Second, to eliminate chattering, adaptive reaching law is selected to improve the dynamic quality of approaching motion. Finally, a comparative simulation experiment is carried out with a 3-DOF manipulator as the research object. The results show that the control method has obvious improvements in eliminating chattering, improving tracking accuracy, and increasing convergence speed, which verifies the feasibility and superiority of the control scheme. [ABSTRACT FROM AUTHOR]

Steering angle and driving force saturations are inevitable in autonomous vehicle motions, which will degrade the motion control performance and even deteriorate the driving safety. In addition, the strong coupling of lateral and longitudinal motions of autonomous vehicles also contributes to the saturations damage and increases the control difficulty. To reduce these negative impacts on motion control, a novel motion controller for automated vehicles is presented in this paper. Firstly, an extended 3 degree of freedom vehicle dynamic model is built with the unknown saturations, strong-coupled motion characteristics and external disturbance considered simultaneously. Then, a coordinated controller is designed to complete motion control task based on sliding-mode control (SMC) and non-singular fast terminal sliding-mode control (NFTSM). Moreover, a three-layer radial basis function neural network (RBFNN) is adopted as a compensator to approximate modelling uncertainties. Based on Lyapunov stability theory, the uniform ultimate boundednesses of motion control errors are guaranteed. Simulation results show that the proposed controller owns smaller steady-state errors and faster convergence speed. [ABSTRACT FROM AUTHOR]