Adaptive Linear Dynamic Matrix Control applied to an integrating process

Guiamba, Isabel R. F.; Mulholland, M.

doi:10.1016/j.compchemeng.2004.07.026

Cited by 14 publications

(11 citation statements)

References 8 publications

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“…Secondly, a MPC (Cutler and Ramaker, 1980) rather than a PID controller is employed to serve as the prefilter of the compound control structure. It should be pointed out that MPC is one of the most popular advanced algorithm used in chemical industrial processes (Berber and Coskun, 1996;Dougherty and Cooper, 2003;Guiamba and Mulholland, 2004;Chen et al, 2008;Labbidi et al, 2004) due to its remarkable superiority in dealing with time delays (Ramasamy et al, 2005). Finally, the analysis and design method of Zhu et al (2005) is only for low-order systems since the control analysis there is constrained to a PID structure, while our design method is also suitable for high-order systems.…”

Section: Introductionmentioning

confidence: 94%

Disturbance rejection of dead-time processes using disturbance observer and model predictive control

Yang

Chen

et al. 2011

Chemical Engineering Research and Design

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Section: Introductionmentioning

confidence: 94%

Disturbance rejection of dead-time processes using disturbance observer and model predictive control

Yang

Chen

et al. 2011

Chemical Engineering Research and Design

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“…Although DMC algorithm can be made adaptive, when it is used for control of plants containing integrating process units, steady-state offsets occur for sustained changes. In order to solve the problem, Guiamba [2] introduced an adaptive linear dynamic matrix control algorithm (ALDMC) with integrating compensation. Zhijun Li [3] proposed a simple modified DMC algorithm, which used a modified IMC structure to removal the offset based on the fact that DMC algorithm is equivalent to an IMC structure.…”

Section: Introductionmentioning

confidence: 99%

Multivariable model predictive control for integrating processes with input constraints

Zhu

Zhou

2009

2009 Chinese Control and Decision Conference

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The model predictive control (MPC) strategy with input constraints may lead to infeasibility of the control algorithm in short term and degradation of the control performance. For the control of integrating processes, the system input constraints will be possible to come into conflict with the constraints caused by zeroing the integrating modes of the system at the end of the control horizon. In order to deal with this problem and increase the feasibility, the effect of setpoint on feasibility is studied for multivariable model predictive control of integrating processes in this paper. An improved algorithm is proposed by recalculating the setpoints according to the hard constraints before calculating the manipulated variable. The simulation results verify the efficiency and feasibility. 0 0 1 i NY J J N J J

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“…The method combines the output of multiple linear DMC controllers and does not introduce additional computational complexity in relation to non‐adaptive DMC. Guiamba and Mulholland11 developed and implemented an Adaptive Linear DMC (ALDMC) algorithm in a two‐input/two‐output pump‐tank system with an integrating behavior in the form of an off‐line step response convolution model. ALDMC showed better performance than the non‐adaptive Linear DMC (LDMC) in the case of plant/model mismatch.…”

Section: Introductionmentioning

confidence: 99%

Modeling and predictive control using fuzzy logic: Application for a polymerization system

et al. 2009

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In this study, a predictive control system based on type Takagi-Sugeno fuzzy models was developed for a polymerization process. Such processes typically have a highly nonlinear dynamic behavior causing the performance of controllers based on conventional internal models to be poor or to require considerable effort in controller tuning. The copolymerization of methyl methacrylate with vinyl acetate was considered for analysis of the performance of the proposed control system. A nonlinear mathematical model which describes the reaction plant was used for data generation and implementation of the controller. The modeling using the fuzzy approach showed an excellent capacity for output prediction as a function of dynamic data input. The performance of the projected control system and dynamic matrix control for regulatory and servo problems were compared and the obtained results showed that the control system design is robust, of simple implementation and provides a better response than conventional predictive control. V V C 2009 American Institute of Chemical Engineers AIChE J, 56: 965-978, 2010

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Adaptive Linear Dynamic Matrix Control applied to an integrating process

Cited by 14 publications

References 8 publications

Disturbance rejection of dead-time processes using disturbance observer and model predictive control

Disturbance rejection of dead-time processes using disturbance observer and model predictive control

Multivariable model predictive control for integrating processes with input constraints

Modeling and predictive control using fuzzy logic: Application for a polymerization system

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