A fixed-time trajectory tracking control method for uncertain robotic manipulators with input saturation based on reinforcement learning (RL) is studied. The designed reinforcement learning control algorithm is implemented by radial basis function (RBF) neural network, in which the actor neural network is used to generate the control strategy and the critic neural network is used to evaluate the execution cost. A new non-singular fast terminal sliding mode technique is used to ensure the convergence of tracking error in fixed time, and the upper bound of convergence time is estimated. To solve the saturation problem of an actuator, a nonlinear anti-windup compensator is designed to compensate for the saturation effect of the joint torque actuator in real time. Finally, the stability of the closed-loop system based on Lyapunov candidate is analyzed, and the timing convergence of the closed-loop system is proved. Simulation and experimental results show the effectiveness and superiority of the proposed control law.
In this paper, the PD Backstepping control problem is investigated for two-wheeled inverted pendulum. A nonlinear model with two states is given which is proposed to keep stability at upright position and track the specified position. Backstepping controller is used to generate the law with measured states and ensures the convergence towards zero of the angle tracking error. Furthermore, PD controller is used to control the movement of system towards the set position. The co-simulation results demonstrate the effectiveness and correction of the proposed control method in this paper.
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