Generalized Predictive Control

Early applications of MPC took place in the 1970s, mainly in industrial contexts, but only later MPC became a research topic. One of the first solid theoretic formulations of MPC is due to Richalet et al. [53], who proposed the so-called Model Predictive Heuristic Control (MPHC). MPHC uses a linear model, based on the impulse response and, in the presence of constraints, computes the process input via a heuristic iterative algorithm. In [23], the Dynamic Matrix Control (DMC) was introduced, which had a wide success in chemical process control both impulse and step models are used in DMC, while the process is described via a matrix of constant coefficients. In later formulations of DMC, constraints have been included in the optimization problem. Starting from the late 1980s, MPC algorithms using state-space models have been developed [38, 43], In parallel, Clarke et al. used transfer functions to formulate the so-called Generalized Predictive Control (GPC) [19-21] that turned out to be very popular in chemical process control. In the last two decades, a number of nonlinear MPC techniques has been developed [34,46, 57],... 

D.W. Clarke and C. Mohtadi. Properties of generalized predictive control. Automatica, 25 859-875, 1989. 

D.W. Clarke, C. Mohtadi, and P.S. Tuffs. Generalized predictive control—Part I. The basic algorithm. Automatica, 23 137-148, 1987. 

Clarke, D. W., Mohtadi, C., and Tuffs, P. S., Generalized predictive control. Part 1 The basic algorithms, Automatica 23, 2, 137-148 (1987a). 

Generalized Predictive Control and Bioengineering, by M. Mahfouf and D. A. Linkens Sliding Mode Control theory and applications, by C. Edwards and S. K. Spurgeon Neural Network Control of Robotic Manipulators and Nonlinear Systems, by F. L. Lewis, S. Jagannathan and A. Yesildirek. 

Normey-Ricoa, J. E., Gomez-Ortegab, J., Camachob, E. F. (1999). ASmith-predictor-based generalized predictive controller for mobile robot path-tracking. Control Engineering Practice, 7(6), 729-740. doi 10.1016/ S0967-0661(99)00025-8... 

Introduction The model-based contfol strategy that has been most widely applied in the process industries is model predictive control (MFC). It is a general method that is especially well-suited for difficult multiinput, multioutput (MIMO) control problems where there are significant interactions between the manipulated inputs and the controlled outputs. Unlike other model-based control strategies, MFC can easily accommodate inequahty constraints on input and output variables such as upper and lower limits or rate-of-change limits. 

When available, fundamental process models are preferred. For many complex processes such as composite manufacturing in general and autoclave curing in particular, however, these models are often not available. This lack of availability is due to an inadequate understanding of the complex events that take place during the process. A fundamental process model is occasionally available, but it is still unsuitable for on-line model predictive control application due to the extensive computing time required to solve the model s equations. This lack of... 

With only a qualitative understanding of the experiments described in Section 5.1.1, we saw that we could predict the general shapes of the responses. However, we are ultimately interested in obtaining quantitative information about electrode processes from these current-time or current-potential curves, and doing so requires the creation of a theory that can predict, quantitatively, the response functions in terms of the experimental parameters of time, potential, concentration, mass-transfer coefficients, kinetic parameters, and so on. In general, a controlled-potential experiment carried out for the electrode reaction... 

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