Neural Network PID Algorithm for a Class of Discrete-Time Nonlinear Systems

Huifang Kong, Yao Fang


The control of nonlinear system is the hotspot in the control field. The paper proposes an algorithm to solve the tracking and robustness problem for the discrete-time nonlinear system. The completed control algorithm contains three parts. First, the dynamic linearization model of nonlinear system is designed based on Model Free Adaptive Control, whose model parameters are calculated by the input and output data of system. Second, the model error is estimated using the Quasi-sliding mode control algorithm, hence, the whole model of system is estimated. Finally, the neural network PID controller is designed to get the optimal control law. The convergence and BIBO stability of the control system is proved by the Lyapunov function. The simulation results in the linear and nonlinear system validate the effectiveness and robustness of the algorithm. The robustness effort of Quasi-sliding mode control algorithm in nonlinear system is also verified in the paper.


dynamic linearization model; quasi-sliding mode control; nonlinear system; neural network;

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International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
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