Supervision of adaptive control algorithms

Hägglund, Tore; íStröM, K. J.

doi:10.1016/s0005-1098(00)00026-1

Cited by 56 publications

(10 citation statements)

References 23 publications

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“…It must be noted that the parameter estimation is biased if disturbances occur whose mean is not zero. As this is the typical case for the used example (room temperature control), the problem is solved by using a high pass filter in the parameter estimator for both controlled and manipulated variable [23], [24]. Although so the estimated parameters converge slower, the results are very much better than without this filter.…”

Section: A Simulated Controllersmentioning

confidence: 99%

Adaptive controllers for level-crossing sampling: Conditions and comparison

Hensel

Kabitzsch

2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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When wireless sensors are used for control purposes (e. g. room temperature control), energy efficiency of the sensors is of great interest, because the energy must come from a (limited) battery or from energy harvesting. Since message transmission is the most critical point regarding energy efficiency, event-based sampling is a typical method for reducing the message rate. Levelcrossing sampling, also known as send-on-delta sampling, is the most popular type of event-based sampling.Some technical processes are time-variant. The controller parameters should then be updated regularly to reach good control performance. Adaptive controllers are able to estimate the current process parameters and optimize the controller parameters automatically.Adaptive control and event-based sampling have hardly been examined together. One focus of this paper is the analysis of preconditions which must be fulfilled to apply adaptive controllers in conjunction with level-crossing (or event-based) sampling sampling. Further, performance measures are discussed which are the basis for a qualitative comparison of such controllers. This paper finally compares two adaptive control strategies with respect to level-crossing sampling.

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Section: A Simulated Controllersmentioning

confidence: 99%

Adaptive controllers for level-crossing sampling: Conditions and comparison

Hensel

Kabitzsch

2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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“…To avoid this difficulty, a supervisor for linear adaptive controllers is proposed by Hagglund and Astrom [9], and developed to switching multiple-model controllers (SMMC) by Peymani et al [10]. The supervisor compares f u and f y , and after detecting a disturbance, it interrupts the adaptation of linear model of RLS in APPC for the first phase.…”

Section: Disturbance Supervisor Designmentioning

confidence: 99%

“…Furthermore, a disturbance rejection supervisor firstly introduced in [9] and developed by authors in [10] is used to enhance the performance of the adaptive controllers in the presence of unmeasured disturbances. A laboratory-scale CSTR pH neutralization plant is chosen as an application example in which unlike many other test processes there is no buffer stream so that the process preserves severe nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

An experimental comparison of adaptive controllers on a pH neutralization pilot plant

Peymani

Fatehi

Bashivan

et al. 2008

2008 Annual IEEE India Conference

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Inherent nonlinearity of pH processes causes that they are recognized as an appropriate test bench for evaluation of advanced controllers. Because of special characteristics of them, it is evident that adaptive controllers outperform others. This paper presents a comparison between a conventional adaptive controller and a switching multiple-model adaptive one in both regulation and disturbance rejection points of view. A disturbance rejection supervisor is designed to improve the performance of the adaptive controllers in the presence of unmeasured disturbances. A laboratory scale pH process is used as an application example.

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“…To avoid this, it might be advantageous to calculate the Idle index only during periods when there are abrupt load changes. This can be accomplished using load detection procedures, see Ha¨gglund and Å stro¨m (2000).…”

Section: The Idle Indexmentioning

confidence: 99%