A nonlinear industrial model predictive controller using integrated PLS and neural net state-space model
Control Engineering Practice(2001)
摘要
Model predictive control (MPC) technology has been well developed and successfully applied in the refinery and petrochemical process industries over the last 20years. Recent development has been focused on nonlinear MPC and robust MPC technologies because new challenges have been encountered in the polymer and chemical industries where many processes show strong nonlinearity and uncertainty. This paper presents a nonlinear industrial model predictive controller, recently developed by Aspen Technology Inc. This MPC controller uses a nonlinear, state-space, integrated partial least-squares (PLS) and neural net model (Zhao, Guiver and Sentoni, American control conference, Philadelphia, PA, USA, 1998), and a multi-step, constrained, Newton-type optimization algorithm (Oliveira and Biegler, Automatica, 31 (2) (1995) 281–286). It results in a robust and cost-effective industrial nonlinear MPC controller. A pH reactor example and a successful industrial application in NOx emission control of a power plant are presented to demonstrate the capability of this controller.
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关键词
Nonlinear control,Predictive control,State-space model,Neural nets,Industrial control
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