Nonlinear Decoupling PID Control Using Neural Networks and Multiple Models
Lianfei ZHAI
Tianyou CHAI
摘要：For a class of complex industrial processes with strong nonlinearity, serious coupling and uncertainty, a nonlinear decoupling proportional-integral-differential (PID) controller is proposed, which consists of a traditional PID controller, a decoupling compensator and a feedforward compensator for the unmodeled dynamics. The parameters of such controller is selected based on the generalized minimum variance control law. The unmodeled dynamics is estimated and compensated by neural networks, a switching mechanism is introduced to improve tracking performance, then a nonlinear decoupling PID control algorithm is proposed. All signals in such switching system are globally bounded and the tracking error is convergent. Simulations show effectiveness of the algorithm.
机标关键词：Multiple ModelsNeural Networksunmodeled dynamicsgeneralized minimum variancecontrol algorithmswitching systemneural networkstracking error
分类号：TP3(计算技术、计算机技术)
资助基金：the National Foundamental Research Program of China(2002CB312201)the State Key Program of National Natural Science of China(60534010)the Funds for Creative Research Groups of China(60521003)Program for Changjiang Scholars and Innovative Research Team in University(IRT0421)
论文发表日期：2006-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：8( 62-69 )
英文信息