Fault-Tolerant Optimized Consensus Control for a Class of Second-Order Nonlinear Multi-Agent Systems Subjected to Actuator Failures

The work intends to study the optimized leader-following consensus control with fault-tolerant ability for a class of second-order nonlinear multi-agent systems (MASs) subjected to actuator failures. Since MAS is an important topic in the field of low-altitude of technology and engineering, this work can contribute to the development of this field. To realize optimized control, reinforcement learning (RL) strategy is employed for avoiding the derivation of analytical solution of Hamilton–Jacobi–Bellman (HJB) equation. Nevertheless, second-order MASs need to simultaneously regulate both position and velocity states to reach consensus, which inevitably increases the complexity of optimization algorithms. In this context, to endow the optimal control scheme with fault-tolerant capability against actuator faults, an adaptive estimation algorithm for unknown actuator fault parameters is integrated with the RL-based optimal control strategy. For making the combination smoothly, a simplified RL algorithm is obtained by taking the negative gradient of a simple positive function, which is equivalent to HJB equation. Finally, both theoretical analysis and numerical simulations verify the feasibility of the proposed control.