State-Space Interpretation of Model Predictive Control

A model predictive control technique based on a step response model is developed using state estimation techniques. The standard step response model is extended so that integrating systems can be treated within the same framework. Based on the modified step response model, it is shown how the state estimation techniques from stochastic optimal control can be used to construct the optimal prediction vector without introducing significant additional numerical complexity. In the case of integrated or double integrated white noise disturbances filtered through general first-order dynamics and white measurement noise, the optimal filter gain is parametrized explicitly in terms of a single parameter between 0 and 1, thus removing the requirement for solving a Riccati equation and equipping the control system with useful on-line tuning parameters. Parallels are drawn to the existing MPC techniques such as Dynamic Matrix Control (DMC), Internal Model Control (IMC) and Generalized Predictive Control (GPC)

Applications of Model Predictive Controllers in a Sugar Factory

INTERNATIONAL SYMPOSIUM ON ADVANCED CONTROL OF CHEMICAL PROCESSThis paper presents two applications of Model Predictive Control in a sugar factory: temperature control in the diffusion process and density control in the wastewater treatment plant. The implementation is done using a Generalized Predictive Controller (GPC) designed for a wide class of industrial process, with the same computational requirements as a PID routine and embedded in the existing control system. The processes have in common the existence of long and uncertain dead times, therefore the original GPC algorithm is improved by the use of the T polynomial, which increases the stability robustness by filtering the predictions.Comisión Interministerial de Ciencia y Tecnología (CICYT) TAP 96-0884Comisión Interministerial de Ciencia y Tecnología (CICYT) TAP 98-0541Comisión Interministerial de Ciencia y Tecnología (CICYT) 1FD97-083

Multivariable predictive PID control for quadruple tank

In this paper multivariable predictive PID controller has been implemented on a multi-inputs multi-outputs control problem i.e., quadruple tank system, in comparison with a simple multiloop PI controller. One of the salient feature of this system is an adjustable transmission zero which can be adjust to operate in both minimum and non-minimum phase configuration, through the flow distribution to upper and lower tanks in quadruple tank system. Stability and performance analysis has also been carried out for this highly interactive two input two output system, both in minimum and non-minimum phases. Simulations of control system revealed that better performance are obtained in predictive PID design

State-space approach to nonlinear predictive generalized minimum variance control

A Nonlinear Predictive Generalized Minimum Variance (NPGMV) control algorithm is introduced for the control of nonlinear discrete-time multivariable systems. The plant model is represented by the combination of a very general nonlinear operator and also a linear subsystem which can be open-loop unstable and is represented in state-space model form. The multi-step predictive control cost index to be minimised involves both weighted error and control signal costing terms. The solution for the control law is derived in the time-domain using a general operator representation of the process. The controller includes an internal model of the nonlinear process but because of the assumed structure of the system the state observer is only required to be linear. In the asymptotic case, where the plant is linear, the controller reduces to a state-space version of the well known GPC controller

Application of simple cascade GPC with robust behaviour to a sugar refinery

This paper presents the application of a Generalized Pre dictive Controller (GPC) to sludge density control in a sugar factory. The loop is controlled by a cascade strategy where both the master and the slave controllers are predic tive ones. The control law is extremely simple to compute and the tuning is straightforward since a method to im plement GPC previously developed by the authors which is very simple to implement and tune has been used. The controllers are embedded in the existing control system needing the same computational requirements as pid rou tines. The original GPC algorithm is improved by the use of the socalled T polynomial which increases the stabil ity robustness by ltering the predictions in order to cope with model uncertainties and di erent process dynamics caused by changes in the process operating pointsMinisterio de Ciencia y Tecnología s TAP 96-884 (CICYT)Ministerio de Ciencia y Tecnología TAP 95-370 (CICYT

Model Predictive Control: Multivariable Control Technique of Choice in the 1990s?

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

Sliding mode predictive control for chemical proces with time delay

16th IFAC World Congress Praga (República Checa), 03/07/2005A design of a novel model predictive controller is presented. The proposed Sliding Mode Predictive Control (SMPC) algorithm combines the design technique of Sliding-Mode Control (SMC) with Model based Predictive Control (MPC). The SMPC showed a considerable robustness improvement with respect to MPC in the presence of time delay, and showed an enhanced ability to handle set point changes in a nonlinear process. Its robustness was evaluated using a robustness plot, its performance was judged using a single input single output nonlinear mixing tank process with variable time delay

Robust multivariable predictive control: how can it be applied to industrial test stands ?

To cope with recent technological evolutions of air conditioning systems for aircraft, the French Aeronautical Test Center built a new test stand for certification at ground level. The constraints specified by the industrial users of the process seemed antagonistic for many reasons. First, the controller had to be implemented on an industrial automaton, not adaptable to modern algorithms. Then the specified dynamic performances were very demanding, especially taking into account the wide operating ranges of the process. Finally, the proposed controller had to be easy for nonspecialist users to handle. Thus, the control design and implementation steps had to be conducted considering both theoretical and technical aspects. This finally led to the development of a new multivariable predictive controller, called alpha-MPC, whose main characteristic is the introduction of an extra tuning parameter alpha that has enhanced the overall control robustness. In particular, the H1-norm of the sensitivity functions can be significantly reduced by tuning this single new parameter. It turns out to be a simple but efficient way to improve the robustness of the initial algorithm. The other classical tuning parameters are still physically meaningful, as is usual with predictive techniques. The initial results are very promising and this controller has already been adopted by the industrial users as the basis of the control part for future developments of the test stand