PID gradient algorithm for neural network based generalised nonlinear PID controller
Kongzhi Lilun Yu Yingyong/Control Theory and Applications
Gradient descent, Neural networks, Nonlinear, Optimization, PID control
An algorithm of PID gradient descent with momentum term (PIDGDM) is proposed. In this algorithm, the procedure of gradient optimization is considered as a feedback control system. Then, the convergent characteristic of the algorithm is presented. In this paper, a nonlinear PID controller is also proposed to handle some nonlinear control problems. The nonlinear PID control strategy is realized using neural networks. The PIDGDM algorithm is applied to the training of the neural nonlinear PID controller. Finally, simulation study of applying the neural nonlinear PID control strategy to a continuous-stirred- tank-reactor (CSTR) and a van de Vusse reactor is illustrated.
Tan, Yonghong; Dang, Xuanju; Van Cauwenberghe, Achiel R.; and Saif, Mehrdad. (2000). PID gradient algorithm for neural network based generalised nonlinear PID controller. Kongzhi Lilun Yu Yingyong/Control Theory and Applications, 17 (6).