A Reinforcement Learning-Based Control Approach for Tracking Problem of a Class of Nonlinear Systems: Applied to a Single-Link Manipulator

This paper introduces a reinforcement learning-based tracking control approach for a class of nonlinear systems using neural networks in the presence of adversarial attacks. This approach incorporates a simultaneous tracking and optimization process. It is necessary to be able to solve the Hamilton-Jacobi-Bellman equation (HJB) in order to obtain optimal control input, but this is difficult due to the strong nonlinearity terms in the equation. In order to find the solution to the HJB equation, we used a reinforcement learning approach. In this online adaptive learning approach, three neural networks are simultaneously adapted: the critic neural network, the actor neural network, and the adversary neural network. Ultimately, simulation results are presented to demonstrate the effectiveness of the introduced method on a manipulator.