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35
A survey of industrial model predictive control technology
, 2003
"... This paper provides an overview of commercially available model predictive control (MPC) technology, both linear and nonlinear, based primarily on data provided by MPC vendors. A brief history of industrial MPC technology is presented first, followed by results of our vendor survey of MPC control an ..."
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Cited by 199 (5 self)
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This paper provides an overview of commercially available model predictive control (MPC) technology, both linear and nonlinear, based primarily on data provided by MPC vendors. A brief history of industrial MPC technology is presented first, followed by results of our vendor survey of MPC control and identification technology. A general MPC control algorithm is presented, and approaches taken by each vendor for the different aspects of the calculation are described. Identification technology is reviewed to determine similarities and differences between the various approaches. MPC applications performed by each vendor are summarized by application area. The final section presents a vision of the next generation of MPC technology, with an emphasis on potential business and research opportunities.
Model Predictive Control: Past, Present and Future
 Computers and Chemical Engineering
, 1997
"... More than 15 years after Model Predictive Control (MPC) appeared in industry as an effective means to deal with multivariable constrained control problems, a theoretical basis for this technique has started to emerge. The issues of feasibility of the online optimization, stability and performance a ..."
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Cited by 96 (4 self)
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More than 15 years after Model Predictive Control (MPC) appeared in industry as an effective means to deal with multivariable constrained control problems, a theoretical basis for this technique has started to emerge. The issues of feasibility of the online optimization, stability and performance are largely understood for systems described by linear models. Much progress has been made on these issues for nonlinear systems but for practical applications many questions remain, including the reliability and efficiency of the online computation scheme. To deal with model uncertainty "rigorously" an involved dynamic programming problem must be solved. The approximation techniques proposed for this purpose are largely at a conceptual stage. Among the broader research needs the following areas are identified: multivariable system identification, performance monitoring and diagnostics, nonlinear state estimation, and batch system control. Many practical problems like control objective prior...
Robust Constrained Model Predictive Control using Linear Matrix Inequalities
, 1996
"... The primary disadvantage of current design techniques for model predictive control (MPC) is their inability to deal explicitly with plant model uncertainty. In this paper, we present a new approach for robust MPC synthesis which allows explicit incorporation of the description of plant uncertainty i ..."
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Cited by 78 (4 self)
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The primary disadvantage of current design techniques for model predictive control (MPC) is their inability to deal explicitly with plant model uncertainty. In this paper, we present a new approach for robust MPC synthesis which allows explicit incorporation of the description of plant uncertainty in the problem formulation. The uncertainty is expressed both in the time domain and the frequency domain. The goal is to design, at each time step, a statefeedback control law which minimizes a "worstcase" infinite horizon objective function, subject to constraints on the control input and plant output. Using standard techniques, the problem of minimizing an upper bound on the "worstcase" objective function, subject to input and output constraints, is reduced to a convex optimization involving linear matrix inequalities (LMIs). It is shown that the feasible receding horizon statefeedback control design robustly stabilizes the set of uncertain plants under consideration. Several extensions...
Refining PID Controllers Using Neural Networks
 ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS
, 1992
"... The KBANN approach uses neural networks to refine knowledge that can be written in the form of simple propositional rules. We extend this idea further by presenting the MANNCON algorithm by which the mathematical equations governing a PID controller determine the topology and initial weights of ..."
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Cited by 13 (5 self)
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The KBANN approach uses neural networks to refine knowledge that can be written in the form of simple propositional rules. We extend this idea further by presenting the MANNCON algorithm by which the mathematical equations governing a PID controller determine the topology and initial weights of a network, which is further trained using backpropagation. We apply this method to the task of controlling the outflow and temperature of a water tank, producing statisticallysignificant gains in accuracy over both a standard neural network approach and a nonlearning PID controller. Furthermore,
Control Performance Monitoring  A Review and Assessment
 Computers and Chemical Engineering
, 1998
"... In this paper we present an overview of current status of control performance monitoring using minimum variance principles. Extensions to PIDachievable performance assessment, tradeoff between performance and robustness, and tradeoff between deterministic and stochastic performance objectives are ..."
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Cited by 12 (2 self)
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In this paper we present an overview of current status of control performance monitoring using minimum variance principles. Extensions to PIDachievable performance assessment, tradeoff between performance and robustness, and tradeoff between deterministic and stochastic performance objectives are discussed. Future directions are pointed out for research and practice with regard to rootcause diagnosis, plantwide performance assessment, multivariable assessment, adequacy assessment of existing control strategies, performance assessment of model predictive control, and the use of intelligent field devices and artificial intelligence to form a systematic diagnostic methodology. A brief tutorial on performance assessment is given in the appendix with an industrial process example. 1 Introduction There is a recent resurgence of interest in control loop performance assessment and diagnosis due to the work of Harris (1989). In his work Harris proposed the use of closedloop data to eva...
Model Predictive Control: Multivariable Control Technique of Choice in the 1990s?
 In Advances in Modelbased Predictive Control
, 1990
"... The state space and input/output formulations of model predictive control are compared and preference is given to the former because of the industrial interest in multivariable constrained problems. Recently, by abandoning the assumption of a finite output horizon several researchers have derived ..."
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Cited by 8 (0 self)
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The state space and input/output formulations of model predictive control are compared and preference is given to the former because of the industrial interest in multivariable constrained problems. Recently, by abandoning the assumption of a finite output horizon several researchers have derived powerful stability results for linear and nonlinear systems with and without constraints, for the nominal case and in the presence of model uncertainty. Some of these results are reviewed. Optimistic speculations about the future of MPC conclude the paper. 1 Introduction The objective of this paper is to review some major trends in model predictive control (MPC) research with emphasis on recent developments in North America. We will focus on the spirit rather than the details, i.e. we do not attempt to provide a complete list of all the relevant papers published during the last few years. 1 We will try to contrast the motivations driving the research in the different camps. There is...
Predictive control by local linearization of a TakagiSugeno fuzzy model
 In FUZZIEEE
, 1998
"... Linear model based predictive control (MBPC) has many advantages but also drawbacks over nonlinear MBPC. In this paper a possibility of using Linear MBPC to control nonlinear systems is investigated. TakagiSugeno fuzzy models are chosen as the model structure. Local linear models can be derived fro ..."
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Cited by 7 (5 self)
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Linear model based predictive control (MBPC) has many advantages but also drawbacks over nonlinear MBPC. In this paper a possibility of using Linear MBPC to control nonlinear systems is investigated. TakagiSugeno fuzzy models are chosen as the model structure. Local linear models can be derived from the linear rule consequents in a straightforward way. Each sample time a local linear model is calculated and used to calculate the next incremental control action using Linear MBPC. This receding horizon controller is used in the IMC scheme to correct for model mismatch. Two simulation examples are given: a SISO liquid level process and a MIMO liquid level process with two inputs and four outputs. Keywords: Predictive control, multivariable (MIMO) systems, TakagiSugeno fuzzy model. 1 Introduction The term fuzzy model based predictive control (FMBPC) is used with several different meanings in the literature. First, a fuzzy model can be used as a predictor in MBPC [1, 2, 3], second, the ...
Stochastic inequality constrained closedloop model predictive control  with application to chemical process operation
, 2004
"... ..."
Incorporating Prior Knowledge in Fuzzy Model Identification
, 2000
"... This paper presents an algorithm for incorporating a priori knowledge into datadriven identification of dynamic fuzzy models of the TakagiSugeno type. Knowledge about the modelled process such as its stability, minimal or maximal static gain, or the settling time of its step response can be transl ..."
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Cited by 6 (3 self)
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This paper presents an algorithm for incorporating a priori knowledge into datadriven identification of dynamic fuzzy models of the TakagiSugeno type. Knowledge about the modelled process such as its stability, minimal or maximal static gain, or the settling time of its step response can be translated into inequality constraints on the consequent parameters. By using inputoutput data, optimal parameter values are then found by means of quadratic programming. The proposed approach has been applied to the identification of a laboratory liquid level process. The obtained fuzzy model has been used in modelbased predictive control. Realtime control results show that when the proposed identification algorithm is applied, not only physically justified models are obtained, but also the performance of the modelbased controller improves with regard to the case where no prior knowledge is involved. 1
Identification and Control of Nonlinear Systems Using Fuzzy Hammerstein Models
 Industrial and Engineering Chemistry Research
, 2000
"... This paper addresses the identification and control of nonlinear systems by means of Fuzzy Hammerstein (FH) models, which consist of a static fuzzy model connected in series with a linear dynamic model. For the identification of nonlinear dynamic systems with the proposed FH models, two methods a ..."
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Cited by 4 (1 self)
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This paper addresses the identification and control of nonlinear systems by means of Fuzzy Hammerstein (FH) models, which consist of a static fuzzy model connected in series with a linear dynamic model. For the identification of nonlinear dynamic systems with the proposed FH models, two methods are proposed. The first one is an alternating optimization algorithm that iteratively refines the estimate of the linear dynamics and the parameters of the static fuzzy model. The second method estimates the parameters of the nonlinear static model and of the linear dynamic model simultaneously by using a constrained recursive leastsquares algorithm. The obtained FH model is incorporated in a modelbased predictive control scheme and a new constrainthandling method is presented. A simulated waterheater process is used as an illustrative example. A comparison with an affine neural network and a linear model is given. Simulation results show that the proposed FH modeling approach is useful for modular parsimonious modeling and modelbased control of nonlinear systems.