Robust Optimal Parallel Tracking Control Based on Adaptive Dynamic Programming

This article focuses on a novel robust optimal parallel tracking control method for continuous-time (CT) nonlinear systems subject to uncertainties. First, the designed virtual controller facilitates the transformation of the original nonlinear system into an affine system with an augmented state vector, which promotes the introduction of the optimal parallel tracking control problem. Then, this article generates fresh insight into counteracting the effects of uncertainty by developing a novel parallel control system that invokes the formulated virtual control law and an auxiliary variable obtained from the relationship between the solutions of the optimal control problems for the uncertain system and the nominal one. Next, critic neural networks (NNs) approximate the Hamilton-Jacobi-Bellman (HJB) equations' solution to implement the proposed robust optimal control method via adaptive dynamic programming (ADP). Finally, simulation experiments demonstrate the proposed method's remarkable effectiveness.