Performance improvements of self-tuning controllers by multistep horizons: The MUSMAR approach

Greco, Cosimo; Menga, Giuseppe; Mosca, E.; Zappa, G.

doi:10.1016/0005-1098(84)90018-9

Cited by 109 publications

(32 citation statements)

References 16 publications

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“…2, the closed-loop transfer function is given by (53) where the expectation is conditional upon data up to time t . Related approaches have been considered by Greco et al (1984), de Keyser and van Cauwenberghe (1981, Ydstie ( , 1984a, Samson (1982), Lee and Lee (1985), and van Cauwenberghe and de Keyser (1985).…”

Section: Design Methods Based On Pole Placementmentioning

confidence: 97%

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: Design Methods Based On Pole Placementmentioning

confidence: 97%

Adaptive control strategies for process control: A survey

1986

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“…An alternative approach is to base the controller design on transfer-function models, which are applicable to both stable and unstable plants, and lead to lower-order representations. Examples of transfer-functionbased predictive control are the well-known generalized predictive control (GPC) technique (Clarke et al, 1987) and the MUSMAR approach (Greco et al, 1984). A third approach is the use of state-space methods for predictive control design, a practice that has an early representative in the work of Kwon and Pearson (1977), and that has recently gained popularity through multiple advocates, such as the work of Muske and Rawlings (1993a,b).…”

Section: Introductionmentioning

confidence: 99%

Robust unconstrained predictive control design with guranteed nominal performance

1996

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“…In this approach the model is not used recurrently, and the prediction error is not propagated. Yet another option is to use a multi-model (Greco et al, 1984;Liu et al, 1999;Rossiter and Kouvaritakis, 2001). For each sampling instant within the prediction horizon one independent submodel is used, and the prediction error is not propagated.…”

Section: Introductionmentioning

confidence: 99%

“…For each sampling instant within the prediction horizon one independent submodel is used, and the prediction error is not propagated. Conceptually, the idea is not new-the multi-model is used in the MUSMAR algorithm (Greco et al, 1984). In all cited publications linear multi-models are discussed, although, as shown in (Rossiter and Kouvaritakis, 2001), for some nonlinear processes they give much better prediction accuracy in comparison with a single linear model used recurrently.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear predictive control based on neural multi-models

Ławryńczuk¹,

Tatjewski²

2010

International Journal of Applied Mathematics and Computer Science

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This paper discusses neural multi-models based on Multi Layer Perceptron (MLP) networks and a computationally efficient nonlinear Model Predictive Control (MPC) algorithm which uses such models. Thanks to the nature of the model it calculates future predictions without using previous predictions. This means that, unlike the classical Nonlinear Auto Regressive with eXternal input (NARX) model, the multi-model is not used recurrently in MPC, and the prediction error is not propagated. In order to avoid nonlinear optimisation, in the discussed suboptimal MPC algorithm the neural multi-model is linearised on-line and, as a result, the future control policy is found by solving of a quadratic programming problem.

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Performance improvements of self-tuning controllers by multistep horizons: The MUSMAR approach

Cited by 109 publications

References 16 publications

Adaptive control strategies for process control: A survey

Adaptive control strategies for process control: A survey

Robust unconstrained predictive control design with guranteed nominal performance

Nonlinear predictive control based on neural multi-models

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