Nonlinear Decoupling PID Control Using Neural
Networks and Multiple Models
佚名
【期刊名称】《控制理论与应用（英文版）》 【年(卷),期】2006(004)001
【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. 【总页数】8页(62-69)
【关键词】Nonlinear;Decoupling control;PID;Neural networks;Multiple models;Generalized minimum variance 【作者】佚名
【作者单位】Key Laboratory of Process Industry Automation, Ministry of
Nonlinear Decoupling PID Control Using Neural Networks and Multiple Models
