David M. Prett

Model predictive control: theory and practice—a survey

Abstract We refer to Model Predictive Control (MPC) as that family of controllers in which there is a direct use of an explicit and separately identifiable model. Control design methods based on the MPC concept have found wide acceptance in industrial applications and have been studied by academia. The reason for such popularity is the ability of MPC designs to yield high performance control systems capable of operating without expert intervention for long periods of time. In this paper the issues of importance that any control system should address are stated. MPC techniques are then reviewed in the light of these issues in order to point out their advantages in design and implementation. A number of design techniques emanating from MPC, namely Dynamic Matrix Control, Model Algorithmic Control, Inferential Control and Internal Model Control, are put in perspective with respect to each other and the relation to more traditional methods like Linear Quadratic Control is examined. The flexible constraint handling capabilities of MPC are shown to be a significant advantage in the context of the overall operating objectives of the process industries and the 1-, 2-, and ∞-norm formulations of the performance objective are discussed. The application of MPC to non-linear systems is examined and it is shown that its main attractions carry over. Finally, it is explained that though MPC is not inherently more or less robust than classical feedback, it can be adjusted more easily for robustness.

Model predictive control: Theory and practice

Abstract We refer to Model Predictive Control (MPC) as that family of controllers in which there is a direct use of an explicit and separately identifiable model. Control design methods based on the MPC concept have found wide acceptance in industrial applications and have been studied by academia. The reason for such popularity is the ability of MPC designs to yield high performance control systems capable of operating without expert intervention for long periods of time. In this paper the issues of importance that any control system should address are stated. MPC techniques are then reviewed in the light of these issues in order to point out their advantages in design and implementation. A number of design techniques emanating from MPC, namely Dynamic Matrix Control, Model Algorithmic Control, Inferential Control and Internal Model Control, are put in perspective with respect to each other and the relation to more traditional methods like Linear Quadratic Control is examined. The flexible constraint handling capabilities of MPC are shown to be a significant advantage in the context of the overall operating objectives of the process industries and the 1–, 2–, and ∞ system norm formulations of the performance objective are discussed. The application of MPC to nonlinear systems is not covered for brevity. Finally, it is explained that though MPC is not inherently more or less robust than classical feedback, it can be adjusted more easily for robustness.

Design Methodology Based On the Fundamental Control Problem Formulation

After more than ten years of advanced process control developments at Shell, we have come to the realization that a Unified Approach to control design is needed. Two main reasons for this need exist. On the one hand, the costs associated with treating each control problem as a separate and unique case study are becoming increasingly higher. On the other hand, with the increase in the number of control applications, it is becoming prohibitively costly to have expert manpower maintaining loops. Our own experience dictates that a unification of approach is only possible after there is a complete understanding of the Fundamental Control Problem. This involves a recognition that every control system attempts to meet certain Performance Criteria given a Process Representation. Therefore, a design methodology that recognizes all elements of this Fundamental Control Problem up front and allows for intelligent introduction of necessary assumptions and compromises by the designer should provide the desired unification. In this paper we outline a design methodology based on this Fundamental Control Problem definition indicating the future research efforts needed in order to realize it.

Design of Robust Process Controllers

Abstract The task of designing a robust process controller consists of determining the control algorithm that meets the system performance requirements across a broad range of operating conditions while recognizing the compromises demanded by the available implementation vehicles. This design task generally involves an iterative procedure wherein the compromises forced on the designer and the performance demanded represent an infeasible set that must be negotiated upon until a resolution is achieved. In the chemical and refining industries this task is particularly challenging for two reasons. On the one hand, there is little freedom to change the basic process design in order to achieve feasibility. On the other hand, large levels of research and/or engineering effort to mathematically represent the process is normally not justifiable because of the fact that the number of processes of a given genre is small and so the cost is not distributed across a large number. The needs of our industry have forced a unified approach to control theory wherein the economy is incorporated in the fact that a single design procedure is utilized. In this paper we describe our work in developing such a unified approach to process control. Because of its generality, it is proposed as a potential solution to many of the current control problems encountered not only in our industry but across a broad class of industrial needs. In addition, we outline our current research efforts which will lead to the development of highly versatile and robust controllers whose structure changes to meet the performance requirements on-line in real time.

The Role of Emerging Technologies in Real-Time Manufacturing Computer Systems

This report contains a number of important conclusions concerning the role of emerging technologies in real time manufacturing computer systems. It is divided into three sections. The first section describes the difference between efficient and effective solutions for manufacturing computer systems. The second section describes the roles of symbolic and subsymbolic information processing in creating efficient and effective solutions for process management. In the final section, we describe subsymbolic information processing with neural networks which we believe will provide a new source of effective solutions for manufacturing.

MULTICOMPONENT DISTILLATION CALCULATIONS USING SIMPLIFIED TECHNIQUES

Short-cut techniques for simulation of simple distillation columns, and complex columns incorporating sidedraw products and interstage heat exchange are presented. The advantageous features of these techniques over rigorous tray-by-tray calculations are the short computer execution times and low core storage requirements. These features, in conjunction with the high accuracy attained when compared to rigorous solution techniques make the presented models ideal for simulation of distillation columns on small real-time computers, where they can be used as part of online optimization and closed-loop control systems. The models are also particularly useful in generating overall and component mass balances as a starting guess for more rigorous tray-by-tray calculations. The robustness of the models is such that they converge to a mass balanced solution in excess of 99.9% of the time.