Design and Experiments of Anticipatory Learning Control: Frequency-Domain Approach

Wang, Dongliang; Ye, Yongqiang

doi:10.1109/tmech.2005.848297

Cited by 73 publications

(48 citation statements)

References 17 publications

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“…It is possible to select U1(nω) and Y d (nω) such that |E1(n0ω)| > 0, which contradicts to the postulate (12). This completes the proof of necessity.…”

Section: Proof Sufficiencymentioning

confidence: 71%

Discrete-frequency convergence of iterative learning control for linear time-invariant systems with higher-order relative degree

Ruan

Bien

2015

Int. J. Autom. Comput.

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Abstract:In this paper, a discrete-frequency technique is developed for analyzing sufficiency and necessity of monotone convergence of a proportional higher-order-derivative iterative learning control scheme for a class of linear time-invariant systems with higher-order relative degree. The technique composes of two steps. The first step is to expand the iterative control signals, its driven outputs and the relevant signals as complex-form Fourier series and then to deduce the properties of the Fourier coefficients. The second step is to analyze the sufficiency and necessity of monotone convergence of the proposed proportional higher-order-derivative iterative learning control scheme by assessing the tracking errors in the forms of Paserval s energy modes. Numerical simulations are illustrated to exhibit the validity and the effectiveness.

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“…It is possible to select U1(nω) and Y d (nω) such that |E1(n0ω)| > 0, which contradicts to the postulate (12). This completes the proof of necessity.…”

Section: Proof Sufficiencymentioning

confidence: 71%

Discrete-frequency convergence of iterative learning control for linear time-invariant systems with higher-order relative degree

Ruan

Bien

2015

Int. J. Autom. Comput.

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“…At time t, the output of this controller is (e k (·))(t + ) which uses the anticipatory knowledge at time t + . This is formally similar to the A-type iterative learning controller proposed for LTI systems in [5,6]. Now, the idea of ILC with anticipation in time is generalized to TDS (see, e.g., [10][11][12]).…”

Section: Ilc Law Descriptionmentioning

confidence: 92%

“…Hence, using the approach as used in [6], one can select the lead time and learning gain to design the ILC law (16). Furthermore, the design parameters and can also be selected to show robustness with respect to model uncertainties.…”

Section: Is Iteration-invariantmentioning

confidence: 99%

Anticipatory approach to design robust iterative learning control for uncertain time‐delay systems

Meng¹,

Jia²

2010

Asian Journal of Control

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This paper is devoted to robust iterative learning control (ILC) design for time-delay systems (TDS) with uncertainties in both model plant and delay time. An ILC law is considered by using anticipation in time to compensate for the effects of delay, which has three design parameters: the weighting function, the lead time and the learning gain. For iteration-invariant TDS, it is shown that a necessary and sufficient condition can be obtained for the tracking error to converge for all admissible plant uncertainties. If the uncertainty is considered to be varying randomly from iteration to iteration, resulting in iteration-varying TDS, then a necessary and sufficient condition can be derived to ensure the convergence of the expected tracking error. For TDS in both cases, it is also shown that the convergence is monotonic in the sense of the L 2 -norm, and the estimated delay is sufficiently anticipatory for the selection of the lead time to achieve the robust ILC design. Simulation results are included to verify the theoretical study.

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“…The reason behind this phenomenon was studied in many papers (Huang and Longman 1996;Longman 2000). Recently, phase lead compensation ILC has been proposed, attracting many research efforts (Wirkander and Longman 1999;Wang 2000;Wang and Ye 2005). But even with phase lead compensation, a low-pass filter is still necessary to cut off those frequency components, noise and uncertainties that cause bad transience in learning.…”

Section: Introductionmentioning

confidence: 99%

On learning transient, auto-tunings of learnable bandwidth and lead step in iterative learning control

Zhang

Wang

2010

International Journal of Systems Science

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Design and Experiments of Anticipatory Learning Control: Frequency-Domain Approach

Cited by 73 publications

References 17 publications

Discrete-frequency convergence of iterative learning control for linear time-invariant systems with higher-order relative degree

Discrete-frequency convergence of iterative learning control for linear time-invariant systems with higher-order relative degree

Anticipatory approach to design robust iterative learning control for uncertain time‐delay systems

On learning transient, auto-tunings of learnable bandwidth and lead step in iterative learning control

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