Data-Driven Hybrid Intelligent Optimal Tracking Design with Industrial Applications

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Data-Driven Hybrid Intelligent Optimal Tracking Design with Industrial Applications

Wang, Ding / Ha, Mingming / Zhao, Mingming

In this chapter, a hybrid intelligent tracking controlTracking control approach is developed to address optimal tracking problems for a class of nonlinear discrete-time systems. The generalized value iterationGeneralized value iteration algorithm is utilized to attain the admissible tracking controlTracking control with offline training, while the online near-optimal controlNear-optimal control method is established to enhance the control performance. It is emphasized that the value iterationValue 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 controlSteady control of the reference trajectoryReference trajectory. The critic and action neural networksNeural networks are utilized to approximate the cost functionCost function and the tracking controlTracking control policy, respectively. Considering approximation errors of neural networksNeural 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.