Disturbance observer‐based fixed‐time optimal control for multi‐input multi‐output asymmetric output error constrained nonlinear systems

Summary This paper focuses on a class of multi‐input multi‐output (MIMO) nonlinear systems with unknown external disturbances and asymmetric time‐varying output constraints, and investigates the fixed‐time optimal control problem. First, a fuzzy state observer is designed to estimate the unmeasurable states. Notably, this paper introduces a novel intermediate variable disturbance observer with time‐varying gain parameter, which can more effectively suppress the influence of external disturbances on the controlled system, thereby achieving higher control accuracy and providing a more universal expression. Subsequently, based on the disturbance observer–critic–actor (DOCA) reinforcement learning architecture, a fixed‐time optimal controller is constructed by combining fuzzy approximation and backstepping techniques. The research results show that the controller not only ensures the output error remains within the constraint range and achieves fixed‐time stability but also optimizes virtual control and actual control. Finally, the effectiveness of the proposed strategy is effectively verified through a comparison of the simulation example.