Micro-drilling process control by using adaptive sliding controller. Issue 2 (25th February 2014)

• Record Type: Journal Article
• Authors: Chen, Hung-Yi
• Editors: Hsieh, Wen-Hsiang
• Is Part Of: Engineering computations. Volume 31:Issue 2(2014)
• Page Start: 201
• Page End: 215
• Publication Date: 2014-02-25
• Subjects: Online learning -- Adaptive sliding controller -- Functional approximation technique -- Fuzzy compensation -- Micro-drilling process; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-01-2013-0011
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: – Recently, the micro-positioning technology has become more important for achieving the requirement of precision machinery. The piezo-actuator plays a very important role in this application area. A model-free adaptive sliding controller with fuzzy compensation is proposed for a piezo-actuated micro-drilling process control in this paper. The paper aims to discuss these issues. Design/methodology/approach: – Due to the system's nonlinear and time-varying characteristics, this control strategy employs the functional approximation technique to establish the unknown function for releasing the model-based requirement of the sliding mode control. In addition, a fuzzy scheme with online learning ability is augmented to compensate for the finite approximation error and facilitate the controller design. Findings: – The Lyapunov direct method can be applied to find adaptive laws for updating coefficients in the approximating series and tuning parameter in the fuzzy compensator to guarantee the control system stability. With the addition adaptive fuzzy compensator, as less as five Fourier series functions can be used to approximate the nonlinear time-varying function for designing a sliding mode controller for micro-drilling process control. Originality/value: – The important advantages of this approach are to achieve the sliding mode controller design without the system dynamic model requirement and release the trial-and-error work of selecting approximation function.