Iterative Learning Control of Linear Discrete-Time Multivariable Systems

Fang, Yong; Chow, Tommy W. S.

doi:10.1016/s0005-1098(98)00091-0

Cited by 32 publications

(28 citation statements)

References 7 publications

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“…We used the adaptive inverse method in [1] to deal with this problem. We used the results of ILC design based on 2D system theory in [2,3] and [4]. Based on 2D system theory we could design the ILC controller parameters and based on adaptive inverse method we could compensate the effect of nonlinearities.…”

Section: Discussionmentioning

confidence: 99%

“…In ILC, the problem is to find an appropriate input signal iteratively such that the plant output tracks a desired reference signal over a finite interval of time. 2D system theory provides an appropriate mathematical framework to analyze and design iterative learning controllers [2,3,4].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper we use 2D system theory based iterative learning control discussed in [2] and [3], to design a discrete-time adaptive control of the ILC systems with multi-segment piece-wise linear nonlinearities. We apply the inverse method discussed in [1] to the ILC system and assume that the nonlinearity parameters are unknown.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive iterative learning control for systems with nonlinearities

Khademi

Mood

Maleki

2011

2011 19th Mediterranean Conference on Control &Amp; Automation (MED)

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In this paper, we consider the problem of ILC when it deals with systems with piecewise linear nonlinearities. Most of actuators have nonlinear characteristics such as hysteresis, backlash, dead-zone or piecewise linear nonlinearities. These nonlinearities make controller design difficult, specifically when their parameters are not known. Iterative learning control, (ILC), uses an iterative algorithm to track a fixed reference output signal in a specific period of time. We use the results of iterative learning control design based on 2D system theory and nonlinearity inverse method to compensate the effect of the actuator nonlinearity.I. Khademi was with the Department of electrical Engineering, Amirkabir University of Technology, Tehran,424, Iran. He is now with the

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive iterative learning control for systems with nonlinearities

Khademi

Mood

Maleki

2011

2011 19th Mediterranean Conference on Control &Amp; Automation (MED)

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“…Researchers have devoted a lot of efforts on ILC for discrete-time systems. A 2-D system theory based ILC has been studied for discrete linear timeinvariant systems [30] . A unified learning scheme was considered for the initial shift problem of nonlinear systems with well-defined relative degree [31] .…”

Section: Introductionmentioning

confidence: 99%

A high-order internal model based iterative learning control scheme for discrete linear time-varying systems

Zhou

Huang

2015

Int. J. Autom. Comput.

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Abstract:In this paper, an iterative learning control algorithm is proposed for discrete linear time-varying systems to track iterationvarying desired trajectories. A high-order internal model (HOIM) is utilized to describe the variation of desired trajectories in the iteration domain. In the sequel, the HOIM is incorporated into the design of learning gains. The learning convergence in the iteration axis can be guaranteed with rigorous proof. The simulation results with permanent magnet linear motors (PMLM) demonstrate that the proposed HOIM based approach yields good performance and achieves perfect tracking.

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“…Contributing by the two independent dynamic processes of the 2-D system, the 2-D model provides an excellent mathematical platform to describe both the dynamics of the control system and the behavior of the learning iteration. In the 2-D system theory based ILC techniques for linear multivariable systems, very promising results have been obtained [8], [9], [11]- [14]. Some of the work focused on the investigation of the ILC techniques for linear discrete multivariable systems [9], [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

2-D system theory based iterative learning control for linear continuous systems with time delays

Chow

2005

IEEE Trans. Circuits Syst. I

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This paper presents two-dimensional (2-D) system theory based iterative learning control (ILC) methods for linear continuous multivariable systems with time delays in state or with time delays in input. Necessary and sufficient conditions are given for convergence of the proposed ILC rules. In this paper, we demonstrate that the 2-D linear continuous-discrete Roesser's model can be applied to describe the ILC process of linear continuous time-delay systems. Three numerical examples are used to illustrate the effectiveness of the proposed ILC methods.

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Iterative Learning Control of Linear Discrete-Time Multivariable Systems

Cited by 32 publications

References 7 publications

Adaptive iterative learning control for systems with nonlinearities

Adaptive iterative learning control for systems with nonlinearities

A high-order internal model based iterative learning control scheme for discrete linear time-varying systems

2-D system theory based iterative learning control for linear continuous systems with time delays

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