Observer-Based Optimal Backstepping Security Control for Nonlinear Systems Using Reinforcement Learning Strategy
针对非线性系统在欺骗攻击下的最优控制问题，提出一种基于强化学习的改进安全算法，通过设计特殊观测器估计被攻击破坏的状态，实现输出反馈控制并保证系统有界。
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.