Time waveform replication for electro-hydraulic shaking table incorporating off-line iterative learning control and modified internal model control. (October 2014)

• Record Type: Journal Article
• Authors: Tang, Yu; Shen, Gang; Zhu, Zhen-Cai; Li, Xiang; Yang, Chi-Fu
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 228:Number 9(2014)
• Page Start: 722
• Page End: 733
• Publication Date: 2014-10
• Subjects: Electro-hydraulic shaking table -- iterative learning control -- internal model control -- system identification -- real-time system; Mechanical engineering -- Periodicals; Automatic control -- Periodicals; Systems engineering -- Periodicals; 621.3
• DOI: 10.1177/0959651814536553
• Languages: English
• ISSNs: 0959-6518

Abstract

In this article, a combined control strategy incorporating off-line iterative learning control and modified internal model control is proposed for improving the time waveform replication performance of electro-hydraulic shaking table. To reduce the modeling error between the estimated inverse model and the actual system, a modified internal model control strategy is first utilized to cope with the modeling error by back absorbing the nominal model and the inverse controller into a direct through block. Due to the non-minimum phase property of the nominal model estimated by the recursive extended least square algorithm, the zero magnitude error tracking controller is exploited to obtain a stable inverse controller. Then, an off-line iterative learning control strategy involving a real-time feedback controller is conducted on the compensated system to further enhance the replication performance. Therefore, the proposed algorithm combines the merits of modified internal model control and off-line iterative learning control and simplifies the conventional iterative control process by eliminating consecutive computation of Fourier and inverse Fourier transforms. The combined strategy is first programmed in MATLAB/Simulink and then compiled to a real-time personal computer with xPC target technology for implementation. Experiment results demonstrate that a better tracking accuracy and a faster convergence rate are achieved with the proposed algorithm than conventional pure iterative learning controller.