Long-range Predictive Control Research Articles

Long-range Predictive Control of Nonlinear Systems given by Volterra Series

The unknown nonlinear process is approximated by a Volterra series model which is a nonparametric description. The parameters of the Volterra series can be approximated recursively. Because of the large number of the parameters the usage of scalar convergence coefficients is recommended. The control algorithm is an extension of Brujin's predictive control algorithm, based on the weighting function series of linear systems, to the Volterra weighting function series model. The control signal is assumed constant during the control horizon and the calculation is repeated in every control interval. The above strategy results in a very robust algorithm

Application of generalized predictive control to industrial processes

A novel algorithm called generalized predictive control (GPC) is shown to be particularly effective for the self-tuning control of industrial processes. The method uses long-range predictive control ideas with a carefully chosen controlled autoregressive and integrated moving average (CARMA) plant model and various horizons that allow for a rich variety of control objectives. The procedure can adapt to process dead time and model order, and a multivariable version gives tight control of complex plants without prior knowledge of the interactor matrix. Applications of GPC to a cement mill, a spray-drying tower, and a compliant robot arm give performance better than that of fully tuned proportional-integral-derivative regulators. >

A comparative study of self-adaptive long-range predictive control methods

During the last years a number of interesting control methods were published all having the same common idea: the control action is based on a long-range prediction of the process output. Four of these methods are compared in this paper. At least for three of them industrial applications are known, indicating that long-range predictive control methods are likely to be a useful tool for real-life applications. These adaptive control methods are compared by means of a simulation study. Special attention is paid to their robustness w.r.t. unmodelled dynamics, parameter variations, process noise and changing dead-time. Also dealt with are the number of tuning parameters that remain, as well as their efficiency and interpretation to the user.

Self-Tuning Long-Range Predictive Controllers

A new class of control methods, called long-range predictive control methods, has been recently introduced. Among these methods, Generalized Predictive Control (GPC) appears very promising for applications. In this paper some improvements of this method are presented and experimental results obtained with a real tracking system are reported.

Long-Range Predictive Control with Pole Placement and Double Serie-Parallel Reference Model of a Kuhni Column

This paper describes the application of the long-range predictive control with pole placement and double serie-parallel reference model to mecanically agitated Kühni columns. In previous works (Carrier, 1981) it has been stated that the flooding conditions of a pulsed column for liquid-liquid extraction might be controlled by measuring the conductivity of the liquid medium at a place located between the dispersed phase inlet and the continuous phase outlet. The pulsing intensity was the command variable. In this paper, it is demonstrated that the same control strategy may be similarly applied to a very different type of agitated column, the Kühni contactor which is a major representative of the rotary mixed columns. The controlled variable remains the conductivity measured at a similar location in the column, whereas the pulsing frequency has been replaced by the rotation speed of the central shaft which wears the mixing turbines. The same single input-output linear discrete model with time varying parameters has been adopted in order to model the even more complicated dynamic behaviour of the column. Good results have been obtained, allowing to conclude that the proposed strategy might be of valuable interest whatever the kind of mechanically agitated contactor involved.

Long-range predictive control using weighting-sequence models

Long-range predictive control appears to be a better foundation for self-tuning compared with k-step ahead or model-reference approaches. Various methods have been proposed in the literature based on weighting-sequence models, and the paper unifies their development. By assuming a noise structure which involves Brownian motion, natural integrating action is achieved as opposed to the ad hoc approaches previously used. Simulation studies using truncated models show that large numbers of parameters are necessary using weighting sequences, although a parallel method using a CARIMA model is entirely satisfactory. When used with nonminimum-phase plant, the dynamic matrix control method works best.

A Comparative Study of Self-Adaptive Long-Range Predictive Control Methods

Abstract During the last years a number of interesting control methods were published all having the same common idea : the control action is based on a long-range prediction of the process output. Four of these methods are compared in this paper. At least for three of them industrial applications are known, indicating that long-range predictive control methods are likely to be a useful tool for real-life applications. These adaptive control methods are compared by means of a simulation study. Special attention is paid to their robustness w.r.t. unmodelled dynamics, parameter variations, process noise and changing dead-time. Also the number of tuning parameters that remain, as well as their efficiency and interpretation to the user are dealt with.