Parameter-Adaptive Control of a Class of Nonlinear Processes

Lachmann, K.-H.

doi:10.1016/s1474-6670(17)63029-5

Cited by 15 publications

(4 citation statements)

References 5 publications

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“…In fact, the coefficients are uncertain, which introduces a nonlinear influence into the model. A few studies have been reported using particular classes of nonlinear models such as bilinear models (Svoronos et al, 1981;Balestrino et al, 1984) or Hammerstein models (Anbumani et al, 198 1;Lachmann, 1982).…”

Section: Linear Diflerence Equation Modelsmentioning

confidence: 99%

Adaptive control strategies for process control: A survey

1986

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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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Section: Linear Diflerence Equation Modelsmentioning

confidence: 99%

Adaptive control strategies for process control: A survey

1986

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“…University of California Santa Barbara, CA 93106 bumani et al, 1981;Lachmann, 1982;Dochain and Bastin, 1984;Svoronos et al, 1981). However, the applicability of these techniques is limited to processes with specific nonlinearities such as polynomials in the manipulated input or bilinear products of output and input.…”

Section: Department Of Chemical and Nuclear Engineeringmentioning

confidence: 99%

“…Only the real solutions that satisfy any given constraints on u are relevant, and in case of more than one relevant solution, the one closest to the previous control action, u(t -l), can be selected for u(t). In case there is no relevant solution, the set point, w ( t ) , can be altered temporarily as suggested by Lachmann (1982).…”

Section: W(t)llz + ~~Q ' ( Z -' ) U (~)~~~~mentioning

confidence: 99%

A multivariable nonlinear self‐tuning controller

Agarwal

Seborg

1987

AIChE Journal

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A new self-tuning controller (STC) has been developed for a general class of nonlinear multiinput, multioutput process models. These models can include arbitrary, known nonlinear functions of the old inputs and outputs as well as the products of these functions and any powers of the most recent inputs. A very general control algorithm has been proposed that allows different numbers of inputs and outputs, different time delays for each input-output pair, and a performance index that can include different penalties on the outputs and on the incremental changes in the outputs. Simulation results are presented for two-point composition control of a pilot-scale distillation column. The results demonstrate that the new nonlinear STC performs significantly better than conventional STC's based on linear models.Correspondence concerning this paper should be a d d r d lo bumani et al., 1981;Lachmann, 1982;Dochain and Bastin, 1984;Svoronos et al., 1981). However, the applicability of these techniques is limited to processes with specific nonlinearities such as polynomials in the manipulated input or bilinear products of output and input. This paper presents a new self-tuning controller for multipleinput, multiple-output (MIMO) processes based on a very general class of nonlinear process models. The analysis is an extension of the modified nonlinear STC for single-input, singleoutput (SISO) systems that was previously developed by the authors (Agarwal and Seborg, 1985) to MIMO systems. The new MIMO STC algorithm provides several advantages over an alternative presented in an earlier paper (Agarwal and Seborg, 1986): it is applicable to a broader class of systems, it requires estimation of fewer parameters, and it allows different penalties on the elements of the output in the performance index. Problem StatementThe problem is to derive a new self-tuning controller that minimizes a quadratic cost function objective based on a very general class of nonlinear models that can include arbitrary, known nonlinear functions of the old inputs and outputs as well as the products of these functions and any powers of the most recent inputs. It is assumed that the nonlinear MIMO process can be described adequately by a discrete-time model, which is linear in system parameters and allows for different numbers of inputs and outputs and different time delays between different input-output pairs. Such a model can be represented in the following general form:

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“…Here a nonlinear predictive control algorithm with a general nonlinear and bilinear model structure (Lachmann, 1982;Svoronos et al, 1981) is used. A nonstandard, and probably more appropriate, cost function is used here.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Predictive Control of Complex Processes

Katende

Jutan

1996

Ind. Eng. Chem. Res.

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Most predictive control algorithms, including the generalized predictive control (GPC) (Clarke et al., 1987), are based on linear dynamics. Many processes are severely nonlinear and would require high-order linear approximations. Another approach, which is presented here, is to extend the basic adaptive GPC algorithm to a nonlinear form. This provides a nonlinear predictive controller which is shown to be very effective in the control of processes with nonlinearities that can be suitably modeled using general Volterra, Hammerstein, and bilinear models. In developing this algorithm, the process dynamics are not restricted to a particular order, as is the case with the current nonlinear adaptive algorithms. Simulations are presented using a number of examples, and the steady state properties are discussed.

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Parameter-Adaptive Control of a Class of Nonlinear Processes

Cited by 15 publications

References 5 publications

Adaptive control strategies for process control: A survey

Adaptive control strategies for process control: A survey

A multivariable nonlinear self‐tuning controller

Nonlinear Predictive Control of Complex Processes

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