Adaptive Fixed-time tracking control for large-scale nonlinear systems based on improved simplified optimized backstepping strategy

This paper investigates the optimal fixed-time tracking control problem for a class of nonstrict-feedback large-scale nonlinear systems with prescribed performance. In the process of optimal control design, the new critic and actor neural network updating laws are proposed by adopting the fixed-time technique and the simplified reinforcement learning algorithm, which both guarantee the simplified optimal control algorithm and accelerate the convergence rate. Furthermore, the prescribed performance method is contemplated simultaneously, which ensures tracking errors can converge within the prescribed performance bounds in fixed time. The minimum parameter method is utilized to reduce the number of parameters designed in the adaptive laws for large-scale systems. Meanwhile, the proposed control strategy can guarantee that all closed-loop signals are bounded within a fixed time interval. Finally, simulation examples are provided to validate the effectiveness of the proposed control strategy.