Neural Network Tracking Control of Unknown Servo System with Approximate Dynamic Programming

Although the adaptive dynamic programming (ADP) scheme has been widely researched on the optimal problem in recent years, which has not been applied to the servo system. In this paper, a simplified reinforcement learning (RL) based (ADP) scheme is developed to obtain the optimal tracking control of the servo system, where the unknown system dynamics are approximated with a three-layer neural network (NN) identifier. First, the servo system model is constructed and a three-layer NN identifier is used to approximate the unknown servo system. The NN weights of both the hidden layer and output layer are synchronously tuned with an adaptive gradient law. An RL-based critic NN is then used to learn the optimal cost function, and NN weights are updated by minimizing the squared Hamilton-Jacobi-Bellman (HJB) error. The optimal tracking control of the servomechanism is obtained based on the three-layer NN identifier and RL scheme, which can make the motor speed track the predefined command. Moreover, the convergence of the identifier and NN weights is proved. Finally, a servomechanism model is provided, which can illustrate the proposed methods.