Nonlinear decoupling PID control using neural networks and multiple models. (English) Zbl 1171.93329

Zhai, Lianfei; Chai, Tianyou

J. Control Theory Appl. 4, No. 1, 62-69 (2006).

Summary: 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.

MSC:

93B51	Design techniques (robust design, computer-aided design, etc.)
92B20	Neural networks for/in biological studies, artificial life and related topics
93B12	Variable structure systems

Keywords:

nonlinear; decoupling control; PID; neural networks; multiple models; generalized minimum variance

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