RESUMO
In this article, a data-based feedback relearning (FR) algorithm is developed for the uncertain nonlinear systems with control channel disturbances and actuator faults. Uncertain problems will influence the accuracy of collected data episodes, and in turn affect the convergence and optimality of the data-based reinforcement learning (RL) algorithm. The proposed FR algorithm can update the strategy online by relearning from the empirical data. The strategy can continuously approach the optimal solution, which improves the convergence and optimality of the algorithm. Moreover, based on the experience replay technology, a data processing method is designed to further improve the data utilization efficiency and the algorithm convergence. A neural network (NN)-based fault observer is used to achieve the model-free fault compensation. The polynomial activation function is redesigned by using the sigmoid function/hyperbolic tangent activation function, to reduce the difficulty of NNs design for an unknown nonlinear system and improve the generalization. In the face of disturbances and actuator faults, the control performance, algorithm convergence, and optimality of the proposed strategy can be well guaranteed through comparative simulation.
