Combined control strategy using internal model control and adaptive inverse control for electro-hydraulic shaking table

Abstract

An electro-hydraulic shaking table is a useful experimental apparatus to real-time replicate the desired acceleration signal for evaluating the performance of the tested structural systems. The article proposes a combined control strategy to improve the tracking accuracy of the electro-hydraulic shaking table. First, the combined control strategy utilizes an adaptive inverse control as a feedforward controller for extending the acceleration frequency bandwidth of the electro-hydraulic shaking table when the estimated plant model may be a nonminimum phase system and its inverse model is an unstable system. The adaptive inverse control feedforward compensator guarantees the stability of the estimated inverse transfer function. Then, the combined control strategy employs an improved internal model control for obtaining high fidelity tracking accuracy after the modeling error between the estimated inverse transfer function using adaptive inverse control and the electro-hydraulic shaking table actual inverse system is improved by the improved internal model control. So, the proposed control strategy combines the merits of adaptive inverse control feedforward compensator and improved internal model control. The combined strategy is programmed in MATLAB/Simulink, and then is compiled to a real-time PC system with xPC target technology for implementation. The experimental results demonstrate that a better tracking performance with the proposed combined control strategy is achieved in an electro-hydraulic shaking table than with a conventional controller.

Experimental evaluation of acceleration waveform replication on electrohydraulic shaking tables

“…16 An eight-DOF control method was employed to implement independent control of the EHST. 22 Some multiaxis random vibration control methods for the EHST have been presented. [63][64][65][66] Although the coordinate controller based on transformation matrixes can eliminate the statically indeterminate problem of the redundant EHST, an internal coupling force due to each parameter inconformity and installation errors among eight actuators cannot be avoided.…”

“…80 However, the estimated discretetime transfer function model of the EHST is a nonminimum phase (NMP) system and its direct inverse model is unstable and cannot be employed as an FIMC. In order to overcome the problem, an adaptive finite impulse response was employed to design the FIMC in practical applications, 22,76 a zero-order Taylor series approximate inverse technique was employed to synthesize a feed-forward inverse model, 81,82 and a stable approximate inverse model is designed for NMP systems in the literature. 83 Chen 46 selected three different compensation methods including an improved inverse compensation method in order to minimize the effect of actuator delay for real-time testing.…”

“…Hence, how to accurately estimate position and acceleration closed-loop transfer functions of the EHST and design their inverse transfer functions is a critical issue. There are also several common identification algorithms to estimate transfer function models of the EHST such as H1, 40 a recursive least-squares (RLS) algorithm, [71][72][73] recursive extended least-squares (RELS) algorithm, 18,19,21,[35][36][37]74,75 adaptive inverse identification algorithm based on least-meansquare (LMS) algorithm, 22,76 a state-space model, [40][41][42][43] genetic algorithm, 77 adaptive robust control. 78 Ozcelik et al 79 employed an identification method based on the measured hysteresis response for a NEES-UCSD shake table mechanical system.…”

“…Due to potential advantages of the EHST in power density, large forces, high-fidelity position and acceleration tracking accuracy, [16][17][18][19] high durability and stiffness, a rapid and wide frequency output response, and more accurate vibration tests, many investigations have been carried on their academic studies and industrial applications. 2,20 However, dynamic characteristics of acceleration output responses on the EHST would not be intended in its magnitude and phase 21,22 due to inherent nonlinearities and uncertainties of the EHST, such as dynamics of servovalves and hydraulic actuators. 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.…”

“…2,20 However, dynamic characteristics of acceleration output responses on the EHST would not be intended in its magnitude and phase 21,22 due to inherent nonlinearities and uncertainties of the EHST, such as dynamics of servovalves and hydraulic actuators. 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]…”