Adaptive optimized consensus control for a class of nonlinear multi-agent systems with asymmetric input saturation constraints and hybrid faults

This article studies the adaptive optimized leader–follower consensus control problem for a class of discrete-time multi-agent systems with asymmetric input saturation constraints and hybrid faults based on the optimized backstepping technique. Different from the conventional saturation model, we consider an individual asymmetric saturation constraint for each actuator instead of a common upper and lower bound for all actuators. Besides, a set of hybrid faults is also considered, with the main focus on the partial fault and bias fault. To eliminate the effects of saturation and faults, a simplified smooth function is constructed to approximate the asymmetric saturation model, and designed compensation signals are used to cope with the two main types of faults to improve the fault-tolerance and system performance. Subsequently, long-term strategic utility functions and virtual control signals are approximated to the optimal levels by adopting the actor–critic neural network (NN) framework, and the actor–critic NN weights are adjusted in the light of a gradient descent method. According to the forward difference Lyapunov function approach, it is proved that the closed-loop system can be stabilized and all errors are semiglobally uniformly ultimately bounded. Finally, the validity of the proposed control scheme is demonstrated through two simulation examples.