Adaptive Neural Network Exact-Optimal Consensus Fault Tolerant Control for Nonlinear Multiagent Systems With Actuator Faults
针对存在间歇性执行器故障的不确定高阶非线性多智能体系统，提出一种自适应神经网络精确最优一致性容错控制方案，利用神经网络建模未知智能体并设计状态观测器，通过反步法实现输出反馈控制，使系统渐近稳定且跟踪误差收敛到零。
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.