Fixed-Time Neuro-Optimal Adaptive Control With Input Saturation for Uncertain Robots

This paper presents a neural optimization-based fixed-time adaptive control scheme for robot systems with unknown dynamics and input saturation. During the process of information exploration, security and control efficiency issues always exist due to the complexity of the system. In this regard, a performance index function is constructed to optimize control performance, and a nonlinear auxiliary compensation system is developed to solve the saturation effect of the actuator. By solving the Hamilton–Jacobi–Bellman (HJB) equation and utilizing fixed-time theory, a fixed-time optimization control scheme is designed within the framework of adaptive dynamic programming. The objective of this scheme is to achieve both optimal performance and rapid convergence. Secondly, universal approximators, namely neural network (NNs), are employed to handle unknown uncertainties through the actor-critic-identifier structure. Among them, the critic network evaluates system performance, the actor network implements control actions, and the identifier network estimates unknown dynamics. Additionally, under the Lyapunov stability criterion and optimization theory, a stability analysis is conducted to demonstrate the feasibility of the devised neuro-optimal fixed-time control scheme and guarantee the convergence of all signals within a fixed-time. Finally, simulations are performed to further validate the effectiveness of the developed control method.