Adaptive PID Control System Design Based on ASPR Property of Systems

Mizumoto, Ikuro; Iwai, Zenta

doi:10.5772/21552

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Defining the output error by e(t) = y(t) − y m (t), the error system from u(t) to e(t) shown in Figure 2 can be represented as follows by taking into account the fact that G IM (s) is proper and the system has relative degree of γ [11]:ė…”

Section: Error System With An Internal Model Filtermentioning

confidence: 99%

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Adaptive output regulation based on internal model principle for systems, with uncertain disturbances and reference signals

Yao

Akaike

Mizumoto

2021

SICE Journal of Control, Measurement, and System Integration

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An output regulation problem under the existence of disturbances is one of the main objectives to design the control system for mechanical systems, and the strategy based on the internal model principle (IMP) is one of the attractive ways to suppress the disturbances. In this paper, we propose an almost strictly positive real (ASPR) based adaptive output feedback control with adaptive internal model for output regulation for systems with uncertain disturbances and reference signals. The stability of the obtained adaptive control system is analysed, and the effectiveness of the proposed method is confirmed through numerical simulations.

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Section: Error System With An Internal Model Filtermentioning

confidence: 99%

“…where e z (t) ∈ R γ , η e (t) ∈ R n+m−γ , and A e , b e , C e , A ηe , C ηe are appropriate matrices. Thanks to the internal model filter G IM , we can obtain the error system without disturbances as in ( 7) if the exosystem is known [11].…”

Section: Error System With An Internal Model Filtermentioning

confidence: 99%

Adaptive output regulation based on internal model principle for systems, with uncertain disturbances and reference signals

Yao

Akaike

Mizumoto

2021

SICE Journal of Control, Measurement, and System Integration

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“…However, it is not easy to tune these parameters because most real systems generally have high-order lag factors and uncertainty. Various methods have been proposed for tuning PID parameters, based on robust control [4], adaptive control theory [5], or self-tuning control [6]. Although most conventional tuning methods require system parameters in a control object, methods of computing control parameters using only operating data have been proposed, including virtual reference feedback tuning (VRFT) and fictitious reference iterative tuning (FRIT), and these methods have attracted much attention in recent years.…”

Section: Introductionmentioning

confidence: 99%

Design of an Implicit GMV‐PID Controller Using Closed Loop Data

Wakitani

Hosokawa

Yamamoto

2014

Electrical Engineering Japan

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SUMMARYIn industrial processes, PID control has been applied in many real systems. The control performance strongly depends on the PID parameters. Although some schemes for tuning PID parameters have been proposed, these schemes need system parameters which are estimated by system identification in order to calculate the PID parameters. On the other hand, model-free controller design schemes represented by virtual reference feedback tuning (VRFT) or FRIT have received much attention in the last few years. These methods can calculate control parameters using closed loop data and are expected to reduce computational costs. In this paper, a type of implicit PID controller using closed loop data is proposed. In the proposed method, the PID parameters are calculated on the basis of the implicit generalized minimum variance control. The control performance can be suitably adjusted by means of only a user-specified parameter. The effectiveness of the proposed method is numerically and experimentally evaluated.

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