Iterative learning control of permanent magnet linear motor with relay automatic tuning

Lee, T.H.; Tan, Kok Kiong; Lim, Si Ying; Dou, H.F.

doi:10.1016/s0957-4158(99)00074-4

Cited by 74 publications

(29 citation statements)

References 8 publications

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“…This is particularly important for piezo-systems because the model can change over time, say due to aging effects. Furthermore, as in any feedforward control approach, ILC can be augmented with feedback control for additional improvement in the positioning performance, say to account for external perturbations, or to stabilize the positioning system [15,16]. This method can also be used in conjunction with other approaches to reduce hysteresis in piezos, such as charge control methods [17].…”

Section: Introductionmentioning

confidence: 97%

Design of hysteresis-compensating iterative learning control for piezo-positioners: Application to atomic force microscopes

2006

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Section: Introductionmentioning

confidence: 97%

Design of hysteresis-compensating iterative learning control for piezo-positioners: Application to atomic force microscopes

2006

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“…The existence of time delay, which is inevitable and frequently encountered in various engineering systems, may cause a system to be unstable or inferior in its control performance (Hsiao and Yeh 1993;Lee et al 2000). Therefore, it is of great significance to study time-delay systems in theory and practice.…”

Section: Introductionmentioning

confidence: 99%

High-Order Ilc With Initial State Learning for Discrete-Time Delayed Systems

Duan

2012

Cybernetics and Systems

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This article addresses an iterative learning control (ILC) design for a class of linear discrete-time systems with multiple time delays. In order to improve the tracking performance, we introduce a P-type high-order iterative learning algorithm that makes use of information from several previous iterations. An initial state learning scheme is proposed to eliminate the effect of the initialization error on the final tracking error. Furthermore, we establish a sufficient condition to ensure asymptotic convergence. A simulation example is also provided to illustrate the effectiveness of the proposed result.

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“…It just requires less a priori knowledge about the controlled systems and relies on less online-calculation burden. In the recent two decades, ILC has been extensively studied by experts and scholars with significant progress in both theory and application [1][2][3][4]. It is well known that time delays including control delays and state delays, are unavoidable in most industrial processes.…”

Section: Introductionmentioning

confidence: 99%

Iterative learning control for nonlinear systems with uncertain state delay and arbitrary initial error

Hong-bin

Wang

2011

J. Control Theory Appl.

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Most of the existing iterative learning control algorithms proposed for time-delay systems are based on the condition that the time-delay is precisely available, and the initial state is reset to the desired one or a fixed value at the start of each operation, which makes great limitation on the practical application of corresponding results. In this paper, a new iterative learning control algorithm is studied for a class of nonlinear system with uncertain state delay and arbitrary initial error. This algorithm needs to know only the boundary estimation of the state delay, and the initial state is updated, while the convergence of the system is guaranteed. Without state disturbance and output measurement noise, the system output will strictly track the desired trajectory after successive iteration. Furthermore, in the presence of state disturbance and measurement noise, the tracking error will be bounded uniformly. The convergence is strictly proved mathematically, and sufficient conditions are obtained. A numerical example is shown to demonstrate the effectiveness of the proposed approach.

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Iterative learning control of permanent magnet linear motor with relay automatic tuning

Cited by 74 publications

References 8 publications

Design of hysteresis-compensating iterative learning control for piezo-positioners: Application to atomic force microscopes

Design of hysteresis-compensating iterative learning control for piezo-positioners: Application to atomic force microscopes

High-Order Ilc With Initial State Learning for Discrete-Time Delayed Systems

Iterative learning control for nonlinear systems with uncertain state delay and arbitrary initial error

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