Fault tolerant system based on non-integers order observers: Application in a heat exchanger

Alegría-Zamudio, M.; Escobar-Jiménez, R.F.; Gómez-Aguilar, J. F.

doi:10.1016/j.isatra.2018.06.007

Cited by 15 publications

(12 citation statements)

References 19 publications

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“…Recently, several industrial applications of observers and Kalman filters for fractional‐order systems have been reported . The theory of observer‐based control for fractional‐order systems has already emerged .…”

Section: Discussionmentioning

confidence: 99%

“…The above result suggests that the full-order LQG/LTR procedure → ∞ can be used as a reduced-order LQG/LTR procedure to recover the target with the sensitivity matrix (30). The target includes the reduced-order Kalman filter gain matrix K r through L r defined in (25).…”

Section: Proposition 3 Assume That the Plantmentioning

confidence: 95%

“…The target includes the reduced-order Kalman filter gain matrix K r through L r defined in (25). Unfortunately, the practical meaning of the target based on systems theory is not readily evident from expression (30).…”

Section: Proposition 3 Assume That the Plantmentioning

confidence: 99%

“…Proposition 4. The matrix r (s) given by (30) can be interpreted as the sensitivity matrix at the output of the partial output injection system shown in Figure 1 with the high-gain frequency-shaped matrix M f (s, ) ( ↓ 0).…”

Section: High-gain Partial Output Injectionmentioning

confidence: 99%

“…where r (s) and f (s, ) are given by (30) and (34), respectively. By replacing f (s, ) in (37) with expression (36) in Lemma 3, we can conclude that r (s) coincides with the sensitivity matrix at the output of the partial output injection system with the high-gain frequency-shaped gain matrix M f (s, ) ( ↓ 0).…”

Section: High-gain Partial Output Injectionmentioning

confidence: 99%

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Reduced‐order LQG/LTR procedure for the plant output side

Ishihara

Zheng

2019

Optim Control Appl Methods

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Summary It is commonly believed that reduced‐order observers, including reduced‐order Kalman filters, cannot be used in the loop transfer recovery (LTR) design of the plant output side. In contrast to common understanding, we show that, at least for nonminimum‐phase plants, the reduced‐order Kalman filter can be used in the linear‐quadratic‐Gaussian (LQG)/LTR design of the plant output side with clear meaning in systems theory. The key concept is to regard a reduced‐order Kalman filter as a high‐gain full‐order Kalman filter. For the reduced‐order LQG controller, we examine the asymptotic property achieved by applying the recovery procedure used in the full‐order LQG/LTR design. Using the equivalent full‐order Kalman filter, we find that the sensitivity property of the reduced‐order LQG controller is asymptotically equivalent to that of a high‐gain partial output injection system. Motivated by this result, we propose the reduced‐order LQG/LTR procedure taking the high‐gain partial output injection system as a target. Some target properties are discussed to clarify the difference from the full‐order design. A multivariable design example is presented to show that the procedure provides a systematic design of a reduced‐order controller with optimality consideration.

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Section: Discussionmentioning

confidence: 99%

Section: Proposition 3 Assume That the Plantmentioning

confidence: 95%

Section: Proposition 3 Assume That the Plantmentioning

confidence: 99%

Section: High-gain Partial Output Injectionmentioning

confidence: 99%

Section: High-gain Partial Output Injectionmentioning

confidence: 99%

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Reduced‐order LQG/LTR procedure for the plant output side

Ishihara

Zheng

2019

Optim Control Appl Methods

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An observer‐based fault tolerance control system with a static filter and its application to high‐rise buildings

Chen

Teng

et al. 2020

Engineering Reports

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This article proposes a fault tolerance control (FTC) system that composes of a state observer and a static filter, which can eliminate the adverse effects of sensor failures on the active mass damper control system of high‐rise buildings and realize fault detection and isolation. First, the accurate acceleration responses are obtained through a static filter that is used for minimizing the differences between the estimated and actual feedback signals. The key point is transformed into a gain optimization problem solved by a linear matrix inequality approach. Then, a state observer uses the detected and isolated acceleration responses as the feedback signals to estimate the whole states, which are used to calculate the control forces. Finally, a new observer‐based FTC system is accomplished for high‐rise buildings. To verify its effectiveness, the proposed methodology is applied to a numerical example and an experimental system. The results demonstrate that the fault tolerance controller has a good performance and stable control parameters, which provides good potential for structural vibration control of high‐rise buildings.

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