Higher Order Ilc Versus Alternatives Applied to Chain Conveyor Systems

Al-Towaim, T.; Lewin, P.L.; Rogers, Eric

doi:10.3182/20020721-6-es-1901.00991

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…6 This gives Y (z) = G(z)U (z) = (h m z −m + h m+1 z −(m+1) + h m+2 z −(m+2) + · · ·)U (z), (1.22) where m is the relative degree of the system, z −1 is the delay operator in the discrete-time domain, and the parameters h i are Markov parameters of the impulse response system of the plant G(z). If we now define the following vectors: …”

Section: Motivation For Robust Interval Iterative Learning Controlmentioning

confidence: 99%

“…• Higher-order ILC [6,74,239,516,365,328]. • 2-D approach/analysis [129,123,301,99,124,385,147,348,148,135,169,145,122].…”

Section: A33 Typical Ilc Problemsmentioning

confidence: 99%

“…Hence, for the upper and lower boundary matrices of A 5 , it is enough to use 25 vertex matrices. From these vertex matrices, we calculate the upper boundary and lower boundary matrices of A 5 , A ∈ A I to be From numerous numerical tests, we have found that Lemma D.5 and Lemma D 6. are particularly effective for a stable system and a lower-order impulse response.…”

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confidence: 99%

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Iterative Learning Control

Ahn

Chen

Moore

2007

Communications and Control Engineering

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Section: Motivation For Robust Interval Iterative Learning Controlmentioning

confidence: 99%

“…• Higher-order ILC [6,74,239,516,365,328]. • 2-D approach/analysis [129,123,301,99,124,385,147,348,148,135,169,145,122].…”

Section: A33 Typical Ilc Problemsmentioning

confidence: 99%

mentioning

confidence: 99%

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Iterative Learning Control

Ahn

Chen

Moore

2007

Communications and Control Engineering

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“…Experimental applications of HO-ILC have been also reported; e.g., [5], [6]. It is also interesting to note that at the 15th IFAC World Congress on Automation and Control a special session was devoted to HO-ILC; e.g., [11]- [13]. The basic incentive for using HO-ILC is to improve the control performance by using more of the past control information.…”

Section: Introductionmentioning

confidence: 97%

Optimality of First-Order ILC Among Higher Order ILC

Saab

2006

IEEE Trans. Automat. Contr.

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Higher order iterative learning control (HO-ILC) algorithms use past system control information from more than one past iterative cycle. This class of ILC algorithms have been proposed aiming at improving the learning efficiency and performance. This paper addresses the optimality of HO-ILC in the sense of minimizing the trace of the control error covariance matrix in the presence of a class of uncorrelated random disturbances. It is shown that the optimal weighting matrices corresponding to the control information associated with more than one cycle preceding the current cycle are zero. That is, an optimal HO-ILC does not add to the optimality of standard first-order ILC in the sense of minimizing the trace of the control error covariance matrix. The system under consideration is a linear discrete-time varying systems with different relative degree between the input and each output.Index Terms-Discrete-time systems, iterative learning control (ILC), monotonic convergence, optimal control, relative degree, tracking control.

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“…Most of the proposed HO-ILC algorithms are shown to be robust and, in absence of measurement errors, drive the output error to zero for different classes of systems. It is also interesting to note that at the 15th IFAC World Congress on Automation and Control a special session was devoted to HO-ILC; e.g., [7]- [9]. The basic incentive for using HO-ILC is to improve the control performance by using more of the past control This work was supported by the Lebanese American University.…”

Section: Introductionmentioning

confidence: 98%

On Higher-Order Iterative Learning Control Algorithm in Presence of Measurement Noise

Saab

Proceedings of the 44th IEEE Conference on Decision and Control

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Higher-Order Iterative Learning Control (HO-ILC) algorithms use past system control information from more than one past iterative cycle. This class of ILC algorithms have been proposed aiming at improving the learning efficiency and performance. This paper addresses the optimality of HO-ILC in the sense of minimizing the control error covariance matrix in the presence of measurement noise. It is shown that the optimal weighting matrices corresponding to the control information associated with more than one cycle preceding the current cycle are zero. Consequently, an optimal HO-ILC is automatically reduced to an optimal first-order ILC. The system under consideration is a linear discrete-time varying systems with different relative degree between the input and each output. Furthermore, a suboptimal second-order ILC is proposed for a class of nonlinear systems. Based on a numerical example, it is shown that a compatible suboptimal first-order ILC yields better performance than the proposed suboptimal second-order ILC algorithm.

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Higher Order Ilc Versus Alternatives Applied to Chain Conveyor Systems

Cited by 5 publications

References 4 publications

Iterative Learning Control

Iterative Learning Control

Optimality of First-Order ILC Among Higher Order ILC

On Higher-Order Iterative Learning Control Algorithm in Presence of Measurement Noise

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