Adaptive-critic-based hybrid intelligent optimal tracking for a class of nonlinear discrete-time systems

In this paper, a hybrid intelligent tracking control approach is developed to address optimal tracking problems for a class of nonlinear discrete-time systems. The generalized value iteration algorithm is utilized to attain the admissible tracking control with off-line training, while the on-line near-optimal control method is established to enhance the control performance. It is emphasized that the value iteration performance is improved by introducing the acceleration factor. By collecting the input-output data of the unknown system plant, the model neural network is constructed to provide the partial derivative of the system state with respect to the control law as the approximate control matrix. A novel computational strategy is introduced to obtain the steady control of the reference trajectory. The critic and action neural networks are utilized to approximate the cost function and the tracking control, respectively. Considering approximation errors of neural networks, the stability analysis of the specific systems is provided via the Lyapunov approach. Finally, two numerical examples with industrial application backgrounds are involved for verifying the effectiveness of the proposed approach.