Time waveform replication for electro-hydraulic shaking table incorporating off-line iterative learning control and modified internal model control

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.

Experimental evaluation of acceleration waveform replication on electrohydraulic shaking tables

“…Controllers based on variable feedback compensation are widely termed as a TVC. 2,[18][19][20][30][31][32][33][34][35][36] A block diagram of the TVC is shown in Figure 4, 35 where K vf , K df , and K af are three feedback parameters, and K dr , K vr , and K ar are three feed-forward parameters, respectively. Displacement is measured by an LVDT and acceleration is measured by an accelerometer mounted on the table, and velocity is synthesized from the measured displacement and acceleration.…”

“…17,[23][24][25][26] Besides, disturbed reaction forces generated by a specimen deteriorate, 27,28 dynamics of the base support, 29 and internal coupling force in the EHST, 16,17,22 and so on, affect acceleration tracking control performances. Many compensation approaches for acceleration waveform replication on the EHST have been employed to minimize nonlinear behaviors of electrohydraulic servo systems, such as off-line feed-forward compensation controllers including a three-variable controller (TVC), 2,[18][19][20][30][31][32][33][34][35][36] a feed-forward inverse model controller (FIMC), 2,18,19,22,[34][35][36][37][38][39] and an off-line iterative controller (OIC) 2,20,34,36,[40][41][42][43][44] and online adaptive controllers including adaptive inverse control (AIC), 18,21,22,33,…”

Optimal Design and Hybrid Control for the Electro-Hydraulic Dual-Shaking Table System

“…It is because the control precision directly determines the reality of earthquake replication and in turn influences the feasibility of the performance research of the specimen, which is explained in [7,8]. However, due to the eccentricity of the load mass, cross-coupled characteristic, system nonlinearity and disturbance, the control system has a poor tracking and synchronization precision [9,10].…”

Model-based adaptive feedforward compensation for disturbance caused by overturning moment in 2-dimensional shaking table systems

“…In addition, additional compensators are practically implemented using the basic control system to improve the disturbance compensation capability for dealing with various types of shaking tests. The reference generation through the iterative tests is one of the major approaches to compensate for the many uncertainties in the system; the approach is advantageous for specimens showing linearity in the test (Yasuda et al, 1991) (Tang et al, 2014). The reaction force feedback detected using the force sensor or disturbance observer can compensate for the reaction force because the resonant vibration of the specimen and/or uncertainties is in the low frequency range (Dozono et al, 2004) (Iwasaki et al, 2005).…”