Generalised self-tuning controller with pole assignment

Allidina, A. Y.; Hughes, F.M.

doi:10.1049/ip-d.1980.0003

Cited by 91 publications

(14 citation statements)

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“…Different approaches have been adopted for pole-assignment regulators, where the controllers are chosen in such a way that the closed-loop-system poles are placed at prespecified positions depending on the required system performance. Pole and/or zero assignment approach for the purpose of tuning a PID controller has been presented in [27][28][29][30][31]. Gain scheduled scheme for PI controller, as it is simple to implement and provides good performance, has been proposed in [32,33].…”

Section: E-mail Address: Mszaky78@yahoocommentioning

confidence: 99%

A self-tuning PI controller for the speed control of electrical motor drives

Zaky

2015

Electric Power Systems Research

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Section: E-mail Address: Mszaky78@yahoocommentioning

confidence: 99%

A self-tuning PI controller for the speed control of electrical motor drives

Zaky

2015

Electric Power Systems Research

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“…The generalized pole-placement STC (GPP-STC) was proposed by Allidina and Hughes [7]. It can keep closed-loop poles at preassigned locations under any changes of plant dynamics and can follow the changes of output references.…”

Section: B Generalized Pole-placement Stc (Gpp-stc)mentioning

confidence: 99%

Model-based control of strip temperature for the heating furnace in continuous annealing

Yoshitani

Hasegawa

1998

IEEE Trans. Contr. Syst. Technol.

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This paper presents the model of the heating furnace in continuous annealing processes for use in design of self-tuning control systems. A simplified mathematical model is derived from first principles. The model parameters are recursively estimated with an algorithm called recursive parameter estimation with a vector-type variable forgetting factor (REVVF). The REVVF algorithm was developed for such cases where some knowledge on parameter variability can be obtained beforehand. The control system of strip temperature presented here is hierarchical. The upper level is called "optimal preview control," which performs preset control. It previews the approaching setup change, which is the change of strip size or reference temperature, and optimizes the line speed and the strip temperature trajectory. Next, the lower level is called "temperature tracking control," which performs closed-loop control using the above trajectory as the control target. At this level, the generalized pole-placement self-tuning control was first employed; and later, the generalized predictive self-tuning control was introduced. These control methods were applied with some practical modifications and with the above mentioned REVVF. The control has been working successfully in several real plants.

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“…But although in Ref. 15 the weighting function in the error criterion of the generalized minimum variance control is designed based on the pole-assignment method, the general internal model is not mentioned except for the step reference signal. In addition, Ref.…”

Section: Introductionmentioning

confidence: 99%

“…Further, the control scheme is extended into the self-tuning scheme. Some self-tuning pole-assignment methods [15,16] considering the minimum variance have already been proposed. But although in Ref.…”

Section: Introductionmentioning

confidence: 99%

A design of self‐tuning pole‐assignment control systems based on minimizing quadratic cost functions

Ohnishi

Yamamoto

Kaneda

2003

Electrical Engineering Japan

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SUMMARYThere are numerous studies of the design of self-tuning control (STC) systems for linear nonminimum-phase systems. A wide variety of existing methods on designing STC systems are conceptually classified into two groups. One is based on minimization of an error criterion, and the other on pole assignment of the closed-loop system. On the other hand, in order to guarantee the tracking property for the reference signals, an internal model which corresponds to the reference signals must be inserted into the control system based on the internal model principle. However, the variance of the control error signal may become large on inserting the internal model, because stochastic noise may be amplified. In this paper, a design method for self-tuning pole-assignment control systems with internal models is proposed based on minimizing the linear quadratic cost function. The newly proposed control scheme has the feature that the variance of the stochastic noise in the control system is reduced owing to the minimum variance control scheme, and also the tracking property with respect to the reference signal is assured owing to the internal model. The proposed control scheme is numerically evaluated in some simulation examples.

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Generalised self-tuning controller with pole assignment

Cited by 91 publications

References 0 publications

A self-tuning PI controller for the speed control of electrical motor drives

A self-tuning PI controller for the speed control of electrical motor drives

Model-based control of strip temperature for the heating furnace in continuous annealing

A design of self‐tuning pole‐assignment control systems based on minimizing quadratic cost functions

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