Adaptive Neural Network Exact-Optimal Consensus Fault Tolerant Control for Nonlinear Multiagent Systems With Actuator Faults

The adaptive neural network (NN) exact-optimal consensus fault tolerant control (FTC) problem is investigated for uncertain high-order nonlinear multiagent systems (NMASs) with intermittent actuator faults. NNs are utilized to model unknown agents, and an adaptive NN state observer with asymptotical property is established. Since the optimization point is not directly known to the agents, the optimal signal generator is formulated to estimate it. Based on the designed NN state observer and optimal signal generator, an adaptive NN exact-optimal consensus output-feedback FTC scheme is proposed by using the backstepping control technology. It is proved that the controlled NMAS is asymptotically stable, and the observer errors and the tracking errors between the outputs and optimization point asymptotically converge to zero. Finally, we apply the proposed adaptive NN exact-optimal consensus output-feedback FTC approach to multiple marine surface vehicles (MSVs), and the simulation and comparison results verify its effectiveness.