Neural Network-Based Optimal Fault-Tolerant Control for Interconnected Nonlinear Systems With Actuator Failures

In this study, we present a decentralized optimal fault-tolerant control (FTC) framework using neural networks (NNs) for interconnected nonlinear systems. This approach addresses challenges arising from unknown drift functions, interconnections, and multiple faults, including lock-in-place, loss of effectiveness, and float. Specifically, we propose a novel NN-based approximation scheme that utilizes a learning algorithm and a differentiator to estimate unknown information within the system. Additionally, our developed optimal control framework, in contrast to the conventional adaptive dynamic programming (ADP) approach, eliminates the need to separately design the optimal tracking controller into two parts, i.e., the steady-state controller and the feedback controller. Moreover, in the simulation section, control parameters are designed using the presented search algorithm, which demonstrates advantages in terms of both time efficiency and convenience. Finally, comparative simulations are conducted to illustrate the effectiveness of the proposed decentralized optimal fault-tolerant tracking control strategy.