Adaptive control strategies for process control: A survey

Abstract

In recent years there has been extensive interest in feedback control systems that automatically adjust their controller settings to compensate for changes in the process or the enviornment. Such systems are referred to as adaptive controllers. This survey paper reviews the current state of the art in adaptive control from a process control perspective and describes leading design techniques. Potential operating problems associated with adaptive control schemes are considered. A survey of experimental applications of adaptive control systems to process control problems is also included. SCOPEProcess control systems inevitably include adjustable controller settings that facilitate process operation over a wide range of conditions. Typically, controller settings are tuned after the control system has been installed using time-consuming, trial-and-error procedures. If process conditions change significantly, then the controller must be retuned in order to obtain satisfactory control.In recent years, there has been extensive interest in adaptive control systems that automatically adjust the controller settings to compensate for unanticipated changes in the process or the environment. Adaptive control schemes provide systematic, flexible approaches for dealing with uncertainties, nonlinearities, and time-varying process parameters. Consequently, adaptive control systems offer significant potential benefits for difficult process control problems where the process is poorly understood andlor changes in unpredictable ways. The practical benefits of adaptive control have been documented in a wide variety of successful industrial applications.Although adaptive control has been a reputable research area for about thirty years, it is only in the last decade that it has achieved prominence as one of the

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“…(b) The sampling interval could be adapted to reduce the number of parameters to be estimated. To this end, it is suggested [54] that the sampling period be chosen so that the time delay index has a value of two or three; such slow sampling, the authors suggest, has the additional advantage of increasing the robustness of the corresponding adaptive controller. This advice may be relevant only for small values of the time delay as otherwise it may conflict with the most often quoted rule of thumb that the sampling period should be between one fifth and one fifteenth of the 95% rise time of the process step response [56].…”

“…In an attempt to reduce the computational burden associated with the overparameterisation method, the following ideas may be worth considering in future work: (a) If the time constants of the process do not change significantly, then the denominator parameters need not be estimated on-line [53]; as well as reducing the computational burden in the estimation stage, other advantages of this scheme are that excessive fluctuation of the denominator parameters is avoided and the denominator parameter estimates cannot drift into or near an undesirable region. A further suggestion [54] is that selective updating of certain model parameters be employed when the number of parameters of the process to be estimated is large; such selective updating could be achieved by only updating those parameters that give a significant improvement in the residual of the model fit. (b) The sampling interval could be adapted to reduce the number of parameters to be estimated.…”

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“…A generally used process model is a controlled auto regressive moving average model (CARMA) or auto regressive moving average exogenous (ARMAX) model [18][19][20].…”

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“…Finally, conclusions to the paper are drawn. Other reviews are recommended to the interested reader [19]- [35].…”