Variable Separation-Based Fuzzy Optimal Control for Multiagent Systems in Nonstrict-Feedback Form

This article studies the optimized backstepping consensus control problem for uncertain nonstrict-feedback nonlinear multiagent systems with intermittent actuator faults and input quantization. A fuzzy approximation-based optimal consensus control scheme is proposed via the reinforcement learning algorithm. Meanwhile, a time-varying constraint function is embedded into the optimized backstepping consensus design to guarantee the consensus errors converge to a preassigned residual set within the prescribed time. To overcome the difficulty of the virtual optimized controller design caused by unknown virtual control coefficients, a class of intermediate variables is designed. A modified variable separation technique is proposed to circumvent the problem of algebraic loop caused by the construction of virtual optimized controllers. Then, based on the reconstruction of unknown bounds, a fault-tolerant controller under the quantization action is developed to compensate for the impact of unknown actuator faults automatically, and achieve semiglobally ultimately uniformly bounded results. In the end, a practical example of a group of single-link manipulators is given to illustrate the validity of the presented method.