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.

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 on-line 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 on-line 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...

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 "worst-case" 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 "worst-case" 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 state-feedback control design robustly stabilizes the set of uncertain plants under consideration. Several extensions...

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 statistically-significant gains in accuracy over both a standard neural network approach and a non-learning PID controller. Furthermore,

In this paper we present an overview of current status of control performance monitoring using minimum variance principles. Extensions to PID-achievable performance assessment, trade-off between performance and robustness, and trade-off between deterministic and stochastic performance objectives are discussed. Future directions are pointed out for research and practice with regard to root-cause diagnosis, plant-wide 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 closed-loop data to eva...

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. Takagi-Sugeno 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, Takagi-Sugeno 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 ...

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...

This paper presents an algorithm for incorporating a priori knowledge into data-driven identification of dynamic fuzzy models of the Takagi-Sugeno 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 input-output 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 model-based predictive control. Real-time control results show that when the proposed identification algorithm is applied, not only physically justified models are obtained, but also the performance of the model-based controller improves with regard to the case where no prior knowledge is involved. 1

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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 least-squares algorithm. The obtained FH model is incorporated in a model-based predictive control scheme and a new constraint-handling method is presented. A simulated water-heater 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 model-based control of nonlinear systems.