Self-tuning controllers based on pole-zero placement

Åström, Karl Johan; Wittenmark, Björn

doi:10.1049/ip-d.1980.0018

Cited by 309 publications

(63 citation statements)

References 12 publications

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“…However, these methods assign only the poles in the closed-loop system and so their response characteristics are not as good as that can be obtained from a reference model type [3]. Although self-tuning controller based on polezero placement has been proposed to overcome this problem, yet unstable zeros of the plant cannot be canceled [4]. Using these techniques unstable zeros must also be zeros of the closed-loop system.…”

Section: Introductionmentioning

confidence: 99%

“…In these circumstances, we show that a controller can be designed such that the closed-loop system is stable and that the transfer function of the system, as a whole, is given by (4) The unstable zeros of a plant cannot be canceled by the controller, because in this situation there will ex ist unstable oscillations in the system. This means the zeros of the closed-loop transfer function of a nonmin imum phase system cannot be assigned arbitrarily, i.e.…”

Section: Problemmentioning

confidence: 99%

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A New Method for the Self-Tuning Control of Nonminimum Phase Discrete-Time Systems in the Presence of Disturbances

1997

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We present a new method for the self-tuning control (STC) of nonminimum phase discrete-time systems based on the pole-zero placement. We show that the effect of unstable zeros on the output can be canceled approximately. The long division method is used to decompose a polynomial into a stable polynomial and an unstable one. Finally, the results of computer simulation are presented to illustrate the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Section: Problemmentioning

confidence: 99%

A New Method for the Self-Tuning Control of Nonminimum Phase Discrete-Time Systems in the Presence of Disturbances

1997

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“…Parameter vector is therefore can be determined by minimizing the loss function given in equation (15) using the recursive least square (RLS) algorith m that is widely used and can be referred in [20]and [21] …”

Section: Self-tuning Pole Placementmentioning

confidence: 99%

Adaptive Steam Temperature Regulation for Essential Oil Extraction Process

Tajjudin¹,

Rahiman²,

Ishak³

et al. 2012

CONTROL

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Essential oil is volat ile and sensitive to excessive heat. Many studies had shown that temperature during extraction process had a great impact on the oil quality. Despite of that, until now there are very few research had been published on the control development of essential oil extraction system. Hence, this study was commenced part icularly on the development of a regulated essential oil extract ion system using self-tuning control. A self-tuning control was applied using pole-assignment method to regulate the steam temperature throughout the extraction process. Combination of controller poles in real and imaginary axis may influence the closed-loop response so that the steam can reach the set point faster but yet with min imal overshoot. Extensive analysis was done by simu lation in order to understand the effect of the poles location and also the selection of sampling time over the closed-loop response. Outcome fro m the simu lation was applied on the real process where the controller produced satisfactory result as expected. The controller was able to regulate the steam temperature at a desired level and maintained within ±2% output boundary.

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“…In this example work, an adaptive neural network control approach is used to enhance the performance of a flexible manipulator. Adaptive controllers, based on selfTuning method were proposed to avoid the problem of uncancellable zeros for the system transfer function [11], but the reference model of the adaptive control depends on transfer function of the plant. Due to this problem, the desired output is not independent of the plant characteristics.…”

Section: Introductionmentioning

confidence: 99%

Time Varying Back Propagating Algorithm for MIMO Adaptive Inverse Controller

Mehedi¹

2017

ijacsa

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Abstract-In the field of automatic control system design, adaptive inverse is a powerful control technique. It identifies the system model and controls automatically without having prior knowledge about the dynamics of plant. In this paper neural network based adaptive inverse controller is proposed to control a MIMO system. Multi layer perception and back propagation are combinedly used in this investigation to design the NN learning algorithm. The developed structure represents the ability to identify and control the MIMO system. Mathematical derivation and simulation results for both plant identification and control are shown in this paper. Further, to prove the superiority of the proposed technique, performances are compared with recursive least square (RLS) method for the same MIMO system. RLS based adaptive inverse scheme is discussed in this paper for plant identification and control. Also the obtained simulated results are compared for both plant parameter estimation and tracking trajectory performance.

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Self-tuning controllers based on pole-zero placement

Cited by 309 publications

References 12 publications

A New Method for the Self-Tuning Control of Nonminimum Phase Discrete-Time Systems in the Presence of Disturbances

A New Method for the Self-Tuning Control of Nonminimum Phase Discrete-Time Systems in the Presence of Disturbances

Adaptive Steam Temperature Regulation for Essential Oil Extraction Process

Time Varying Back Propagating Algorithm for MIMO Adaptive Inverse Controller

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