Observer-Based Optimal Backstepping Security Control for Nonlinear Systems Using Reinforcement Learning Strategy

This article considers an observer-based optimal backstepping security control for nonlinear systems using reinforcement learning (RL) strategy. The main challenge faced is the design of optimal contoller under the deception attacks. Therefore, this article introduces an improved security RL algorithm based on neural network technology under the design framework of critic-actor to resist attacks and optimize the entire system. Second, compared with some existing results, how to relax the general assumption about deception attack is also a difficult research topic. In this article, an unusual observer that uses the attacked system output is designed to estimate the real unavailable states caused by deception attacks, so that the impact of deception attacks is eliminated and the output feedback control is also achieved. By selecting the virtual controllers and the real controller as corresponding optimized controllers within the framework of the RL algorithm, the control strategy can ensure that all signals in the closed-loop system are semi-globally ultimately bounded. Finally, two simulation experiments will be run to demonstrate the effectiveness of the strategy.