A new optimal adaptive backstepping control approach for nonlinear systems under deception attacks via reinforcement learning
Wendi Chen, Qinglai Wei
Abstract
In this paper, a new optimal adaptive backstepping control approach for nonlinear systems under deception attacks via reinforcement learning is presented in this paper. The existence of nonlinear terms in the studied system makes it very difficult to design the optimal controller using traditional methods. To achieve optimal control, RL algorithm based on critic-actor architecture is considered for the nonlinear system. Due to the significant security risks of network transmission, the system is vulnerable to deception attacks, which can make all the system state unavailable. By using the attacked states to design coordinate transformation, the harm brought by unknown deception attacks has been overcome. The presented control strategy can ensure that all signals in the closed-loop system are semi-globally ultimately bounded. Finally, the simulation experiment is shown to prove the effectiveness of the strategy.