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Book ChapterDOI

ILC-PIV Design for Improved Trajectory Tracking of Magnetic Levitation System

TL;DR: The hybrid control algorithm is put forward, which integrates the iterative learning control (ILC) scheme with proportional integral velocity (PIV) control, for improved trajectory tracking of magnetic levitation system.
Abstract: This paper puts forward the hybrid control algorithm, which integrates the iterative learning control (ILC) scheme with proportional integral velocity (PIV) control, for improved trajectory tracking of magnetic levitation system. ILC is a type of model-free controller, which is used for systems that perform repetitive tasks. Adjusting the control inputs based on the error information obtained during previous iterations, ILC tries to enhance the transient response of the closed-loop system. One of the striking features of ILC is that even without the full dynamic model of the plant, it can yield perfect trajectory tracking by learning the plant dynamics through iterations. Adopting this learning control feature of ILC, this paper aims to synthesize ILC with PIV for both improved tracking and better robustness compared to conventional PIV. The efficacy of the proposed ILC-PIV controller framework is assessed through a simulation study on the magnetic levitation plant for reference following application.
References
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Journal ArticleDOI
TL;DR: In this paper, the authors proposed a novel current cycle feedback (CCF) iterative learning control (ILC) approach to achieve high-speed imaging on atomic force microscope (AFM).
Abstract: In this paper, we proposed a novel current cycle feedback (CCF) iterative learning control (ILC) approach to achieve high-speed imaging on atomic force microscope (AFM). AFM imaging requires precision positioning of the AFM probe relative to the sample in 3-D (x- y-z). It has been demonstrated that, with advanced control techniques such as the inversion-based iterative control (IIC), precision positioning of the AFM probe in the lateral (x- y) scanning can be successfully achieved. Precision positioning of the probe in the vertical z-axis direction, however, is still challenging because the issues such as the sample topography are unknown, in general; the probe-sample interaction is complicated, and the probe-sample position is sensitive to the probe-sample interaction. The main contribution of this paper is the development of the CCF-ILC approach for the AFM z-axis control, which decouples the robustness of the feedback control from the precision tracking of the feedforward control. Particularly, the proposed CCF-ILC controller design utilizes the recently developed robust inversion technique to minimize the model uncertainty effect on the feedforward control and to remove the causality constraints in other CCF-ILC approaches. It is shown that the iterative law converges and attains a bounded tracking error upon noise and disturbances. The proposed method is illustrated through experimental implementation, and the experimental results show an increase of eight times faster imaging speed for contact-mode imaging.

25 citations

Journal ArticleDOI
TL;DR: In this paper, a robust iterative learning control (ILC) for the purpose of output tracking using continuous sliding mode technique is presented. But the main feature of the design is that the controller signal is continuous due to the use of integral and employment of second-order sliding mode techniques.

25 citations

Journal ArticleDOI
TL;DR: This paper attempts to adopt the benefits of ILC to improve the trajectory tracking performance of spatially interconnected systems by utilizing the ILC update law along the iteration domain repetitively to ensure a perfect reference trajectory tracking.

14 citations