Full-state neural network observer-based hybrid quantum diagonal recurrent neural network adaptive tracking control

Abstract

This study introduces a neural network (NN) adaptive tracking controller-based reinforcement learning (RL) scheme for unknown nonlinear systems. First, an observer using feed-forward NN (FFNN) is performed to estimate the controlled system states. Second, an adaptive control based on actor-critic RL is developed, in which a quantum diagonal recurrent neural network (QDRNN) is proposed to represent the critic and actor parts. The critic QDRNN is applied to perform the “strategic” utility function, and it is minimized by the actor QDRNN. The proposed adaptive tracking NN control guarantees the faster convergence due to the developed updated algorithm for the controller parameters, which is derived using the Lyapunov function. Simulation and practical results indicate the robustness of the proposed observer-based adaptive control relative to other existing controllers.