Intelligent Critic Control With Robustness Guarantee of Disturbed Nonlinear Plants

In this paper, the author focuses on establishing an intelligent critic control framework with robustness guarantee for disturbed nonlinear systems. Combining the neural network learning ability with adaptive critic designs, a general structure of intelligent critic control is developed to address the robustness problems, which broadens the application scope of adaptive dynamic programming and the related learning control methods. First, the problem transformation is conducted for changing the robust stabilization problem into optimal control design, where a special discounted cost function is well defined. Then, a recurrent neural network is constructed to learn the unknown nominal plant with stability proof. Moreover, the critic network implementation is presented with the help of the obtained neural identifier and the adaptive learning architecture. In addition, extension discussions and several simulation examples are provided to display the robustness verification results of the intelligent critic strategy.