Abstract
In this paper, a computationally efficient framework for intelligent critic control design and application of continuous-time input-affine systems is established with the purpose of disturbance attenuation. The described problem is formulated as a two-player zero-sum differential game and the adaptive critic mechanism with intelligent component is employed to solve the minimax optimization problem. First, a neural identifier is developed to reconstruct the unknown dynamical information incorporating stability analysis. Next, the optimal control law and the worst-case disturbance law are designed by introducing and tuning a critic neural network. Moreover, the closed-loop system is proved to possess the uniform ultimate boundedness. At last, the present method is applied to a smart microgrid and then is further adopted to control a general nonlinear system via simulation, thereby substantiating the performance of disturbance attenuation.
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