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Applying dynamic matrix control in the process industries

TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND control 1996年第1期18卷 32-41页

作者： Austin, PC Bozin, AS [o_1]Cambridge Control Ltd Cowley Rd Cambridge CB4 4WZ UK [o_2]Cambridge Control Ltd Cowley Rd Cambridge CB4 4WZ UK

Advanced multivariable control system designs had little impact in the process industries until the development of model-based predictive control (MBPC) systems. This paper records some aspects of the application of one particular MBPC technique, dynamic matrix control (DMC), to three different plants in the process industries. The paper begins by recalling the essential principles of MBPC systems, by briefly outlining the origins of their use in the process industries, and by recording some of the theoretical issues associated with these systems, that are currently being examined. It proceeds with a particular focus on DMC. Its use in the control of process plants is discussed by reference to a simulated paper machine. Subject to the constraints imposed by commercial confidentiality, studies are then presented of application to a fluidised catalytic cracker in the petrochemical industry and to paper fibre recycle plants in the paper industry. The paper closes with a discussion of the benefit plant-wide optimisation systems can offer whole process plants, once MBPC systems have been implemented on the individual units making up the process.

关键词： dynamic matrix control predictive control model based process industries

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Data-driven plant-model mismatch detection for dynamic matrix control systems using sum-of-norms regularization

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COMPUTERS & CHEMICAL ENGINEERING 2024年 190卷

作者： Shi, Yimiao Xu, Xiaodong Yuan, Yuan Dubljevic, Stevan Cent South Univ Changsha 410083 Peoples R China Univ Alberta Edmonton AB Canada

This article addresses the plant-model mismatch detection problem for linear multiple-input and multiple- output systems operating under the constrained dynamic matrix control (DMC) with the assumption of unknown noise models. An autocovariance-based mismatch detection method that uses sum-of-norms regularization is proposed, aiming to detect parameter jumps and estimate the noise model separately. The intention of introducing regularization is not only to be able to segment the mismatch so that the mismatch is piece- wise constant in time, but also to make the method robust to colored noise. Moreover, a method to alleviate mis-detection caused by unknown operating conditions is proposed. We show that the method can detect significant jumps in parameters and thus provide a priori knowledge for system re-identification and timing of updating the model. Finally, the feasibility of the proposed method under closed-loop conditions is analyzed from a stochastic perspective and demonstrated with illustrative examples.

关键词： Plant-model mismatch detection Autocovariance-based method dynamic matrix control Sum-of-norms regularization Noise model parameter estimation

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On-line learning Fuzzy Relational Model based dynamic matrix control of an openloop unstable process

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CHEMICAL ENGINEERING RESEARCH & DESIGN 1999年第A5期77卷 421-428页

作者： Demircan, M Çamurdan, MC Postlethwaite, BE Bogazici Univ Dept Chem Engn TR-80815 Istanbul Turkey Univ Strathclyde Chem & Proc Engn Dept Glasgow Lanark Scotland

Fuzzy Relational Models (FRM) are used when implementing a dynamic matrix control (DMC) algorithm on a nonlinear process which exhibits multiplicity of steady state solutions. Following Ozkan(1), Ozkan and Camurdan(2), the openloop unstable equilibrium point of the process is first stabilized by an analog proportional only controller. DMC is then used to control this stabilized system. Both servo and regulatory control are investigated. Locally and globally obtained fixed and globally obtained variable FRMs are used. In some runs the recursive least squares (RLS) algorithm is also used to update the relational array so as to provide on-line learning. It is shown by simulations that FRMs that are identified in a local region performed as well as those that are identified in the whole operating region. On the other hand the performance of the system was found to be poor if a variable model is used.

关键词： dynamic matrix control fuzzy relational models adaptive control

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Data-driven plant-model mismatch estimation for dynamic matrix control systems

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2020年第17期30卷 7103-7129页

作者： Xu, Xiaodong Simkoff, Jodie M. Baldea, Michael Chiang, Leo H. Castillo, Ivan Bindlish, Rahul Ashcraft, Brian Univ Texas Austin McKetta Dept Chem Engn Austin TX 78712 USA Dow Chem Co USA Freeport TX USA

This article addresses the plant-model mismatch estimation problem for linear multiple-input and multiple-output systems operating under the dynamic matrix control (DMC) implementation of model predictive control. An autocovariance-based method is proposed, aiming to identify parameter values that minimize the discrepancy between the theoretical autocovariance matrices derived from implementing the (explicit) DMC control law and the sampled autocovariance matrices calculated from operating data. We provide proof that the method results in unbiased estimates. A means for dealing with potential overfitting issues caused by the finite step response models used in DMC in practice is proposed. Several examples are presented to illustrated the theoretical developments.

关键词： autocovariance-based method dynamic matrix control model predictive control noise model parameter estimation plant model mismatch estimation

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Truncation error correction by designing input-adjustment-output coefficients for dynamic matrix control based on cubic spline function

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SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL 2024年第8期100卷 813-822页

作者： Chen, Gaige Cao, Yugang Qing, Didi Wang, Youming Univ Posts & Telecommun Sch Commun & Informat Engn Xian Peoples R China Xian Univ Posts & Telecommun Sch Artificial Intelligence Xian Peoples R China Xian Univ Posts & Telecommun Shaanxi Union Res Ctr Univ & Enterprise 5G Ind Int Xian Peoples R China Univ Posts & Telecommun Sch Automat Xian Peoples R China Xian Univ Posts & Telecommun Sch Commun & Informat Engn Changan West St Xian 710061 Peoples R China Xian Univ Posts & Telecommun Sch Artificial Intelligence Changan West St Xian 710061 Peoples R China

The truncation error is an important problem in the dynamic matrix control (DMC) algorithm. Generally, the dynamic information of a plant for the traditional DMC algorithm is determined by the step response. However, the step response of the plant is limited in practice, which leads to the truncation error of the modeling horizon in the feedback correction of DMC algorithm. A truncation error correction method based on cubic spline function is proposed for DMC algorithm, which is realized by designing coefficients and tracking the dynamic response of the system. In the feedback correction of DMC algorithm, the relationship between the correction parameter and the correction difference is constructed by the fitting of cubic spline functions on the intervals. A truncation error correction procedure is presented to calculate the correction parameters by inputting of the correction difference into the relationship. Then, the control increment and predicted model output are computed based on the correction parameters and updated shift matrix. Finally, numerical experiments demonstrate that the control performance of the proposed DMC algorithm has been much improved compared to that of traditional DMC algorithm.

关键词： Truncation error dynamic matrix control input-adjustment-output cubic spline function

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Repetitive dynamic matrix control for systems with periodic specifications

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ISA TRANSACTIONS 2024年 150卷 107-120页

作者： Santos, Tito L. M. Lima, Daniel M. Normey-Rico, Julio E. Univ Fed Bahia UFBA Dept Engn Elect & Comp DEEC Rua Aristides Novis 02 Salvador BA Brazil Univ Fed Santa Catarina UFSC Dept Controle Automacao & Comp Blumenau SC Brazil Univ Fed Santa Catarina UFSC Dept Automacao Sistemas DAS Caixa Postal 476 Florianopolis SC Brazil

This paper proposes a Repetitive dynamic matrix control (RDMC) for systems with periodic specifications. The new algorithm is able to track periodic references and reject repetitive disturbances with a known period based on a modified prediction error. A repetitive version of the Generalized DMC (GDMC) is also proposed such that it can be applied to control open -loop unstable systems. Only the step -response coefficients are required to describe the dynamical system such that the RDMC preserves the modeling simplicity of the dynamic matrix control (DMC). The proposed solution can be interpreted as an extension of the DMC for repetitive control applications. A data -driven filter design is proposed in order to ensure null prediction steady-state error in the presence of periodic disturbances even for unstable open -loop systems. Two case studies are presented to show the usefulness of the proposed strategy for control systems with periodic specification and to illustrate the typical advantages and drawbacks of the proposed repetitive control extension of the DMC algorithm.

关键词： dynamic matrix control Repetitive control Constrained systems Data-driven control

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Practical dynamic matrix control for thermal power plant coordinated control

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control ENGINEERING PRACTICE 2018年 71卷 154-163页

作者： Moon, Un-Chul Lee, Youngjun Lee, Kwang Y. Chung Ang Univ Sch Elect & Elect Engn 84 Huksuk Ro Seoul 06974 South Korea Baylor Univ Dept Elect & Comp Engn Waco TX 76798 USA

This paper proposes three practical strategies for the coordinated control (CC) of a thermal power plant using dynamic matrix control (DMC) that can be directly applied to industrial power plants. The three strategies are the replacement of conventional CC using DMC, the inclusion of disturbance variables, and a supplementary reference correction of the conventional CC. The performance during wide range operation of the three DMC-CCs is compared and discussed with the simulation results of a large-scale power plant model. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Coordinated control Boiler-turbine system Power plant control Thermal power plant dynamic matrix control

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Design of dynamic matrix control based PID for residual oil outlet temperature in a coke furnace

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2014年 134卷 110-117页

作者： Wu, Sheng Zhang, Ridong Lu, Renquan Gao, Furong Hangzhou Dianzi Univ Informat & Control Inst Hangzhou 310018 Zhejiang Peoples R China Hong Kong Univ Sci & Technol Dept Chem & Biomol Engn Kowloon Hong Kong Peoples R China

The application of proportional-integral-derivative (PID) controllers to chemical processes may not achieve the desired effect due to large time delay, model/plant mismatches, etc, which causes performance deterioration. In view of this, the paper first proposes a new PID controller design based on dynamic matrix control (DMC) optimization and then tests it on the residual oil outlet temperature in an industrial coke furnace. The resulting PID controller shows that it has the superior character of DMC algorithm and, at the same time, the simple structure as a traditional PID controller. Since model predictive control is effective in dealing with long time delays and model/plant mismatches, the control performance under the proposed PID is improved compared with typical PID controllers. (C) 2014 Elsevier B.V. All rights reserved.

关键词： PID control dynamic matrix control Chemical processes Industrial coke furnace

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Nonlinear dynamic matrix control using Local Models

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND control 1998年第1期20卷 47-56页

作者： Townsend, S Lightbody, G Brown, MD Irwin, GW Queens Univ Belfast Dept Elect & Elect Engn Control Engn Res Ctr Belfast BT9 5AH Antrim North Ireland

This paper proposes using a Local Model (LM) network representation of a nonlinear chemical process as a basis for nonlinear dynamic matrix control (DMC). The LM network is composed of local linear ARX models and is trained using a hybrid learning approach. The nonlinear DMC uses step responses for different process operating points extracted from the LM network, rather than the single-step response model of linear DMC. Simulation studies of a pH neutralisation process indicate improvements in both set point tracking and disturbance rejection.

关键词： dynamic matrix control Local Model networks nonlinear predictive control

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A new design of fractional-order dynamic matrix control with proportional-integral-derivative-type structure

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MEASUREMENT & control 2019年第5-6期52卷 567-576页

作者： Wang, Dawei Zou, Hongbo Tao, Jili Hangzhou Dianzi Univ Sch Automat Hangzhou Zhejiang Peoples R China Zhejiang Univ Ningbo Inst Technol Ningbo 315100 Zhejiang Peoples R China

Most dynamic systems in practice are of fractional order and often the models using fractional-order equations can grasp their intrinsic properties with more accuracy compared with conventional differential equations. In this paper, a fractional-order modeling-based proportional-integral-derivative-type dynamic matrix control is developed and tested on a typical industrial heating furnace system with fractional-order dynamics. The Oustaloup approximation method is first adopted to obtain the model approximation of the processes, which paves the way for the application of integer order dynamic matrix control to the fractional-order systems. Meanwhile, a set of proportional-integral-derivative-type operators are introduced in the cost function to further optimize the dynamic matrix control in terms of tracking and disturbance-rejection performance. The resulting controller bears both the merits of the dynamic matrix control and the proportional-integral-derivative, and thus improved control performance is obtained. In addition, an industrial heating furnace process system is given to test the performance of the proposed method in comparison with traditional integer order model-based dynamic matrix control, and results show that the proposed method gives improved system performance.

关键词： Fractional-order system dynamic matrix control proportional-integral-derivative control industrial processes

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