Improved sliding mode control for mobile manipulators based on an adaptive neural network

• Published in: Journal of mechanical science and technology Vol. 37; no. 5; pp. 2569 - 2580
• Main Authors: Li, Zhengnan, Ma, Lidong, Meng, Zhijuan, Zhang, Jin, Yin, Yufeng
• Format: Journal Article
• Language: English
• Published: Seoul Korean Society of Mechanical Engineers 01.05.2023; Springer Nature B.V; 대한기계학회
• Subjects: Adaptive control; Algorithms; Control; Control methods; Control theory; Dynamical Systems; Engineering; Equations of state; Industrial and Production Engineering; Kinematics; Liapunov functions; Manipulators; Mechanical Engineering; Neural networks; Original Article; Sliding mode control; Subsystems; Twisting; Vibration; 기계공학; Super-twisting; Neural network; Mobile manipulators; Sliding mode control
• ISSN: 1738-494X; 1976-3824
• DOI: 10.1007/s12206-023-0432-7

In this study, a wheeled mobile manipulator was modeled while considering the case of wheel-ground slippage. Considering the whole system, the number of states in the dynamic system equation was reduced by the kinematic solution, which facilitates the control of the system. For the system state equation, the improved sliding mode control method was applied, a flush continuous control term and a super-twisting algorithm were used to achieve the improved sliding mode control, and the gain of the super-twisting algorithm was adaptively controlled. The modeling error in the subsystem and the uncertain slippage were approximated by a neural network. The middle layer weights of the neural network were updated by the adaptive control law, and the stability of the system was demonstrated by a quadratic Lyapunov function. The simulation results show the superiority of the control method proposed in this paper by comparing them with results for the traditional sliding-mode control method and the improved sliding-mode control method.