Two-degree-of-freedom inversion-based iterative learning control for an electro-hydraulic shaking table

Electro-hydraulic shaking tables with multiple degrees of freedom are widely used to simulate vibration environments and to assess product reliability. Traditional three-variable control faces challenges in reducing the influence of disturbances and compensating for the coupling between the degrees of freedom in the multi-degree-of-freedom electro-hydraulic shaking table system. Although existing iterative learning control can be used to reduce the influence of disturbances, it is difficult to design iterative learning control for the multi-degree-of-freedom electro-hydraulic shaking table system. Moreover, iterative learning control fails to account for the transient learning behavior, which may lead to non-monotonic convergence of the error signal. An inversion-based iterative learning control was designed in this study for the multi-degree-of-freedom electro-hydraulic shaking table system to improve the tracking accuracy of random signals. The inverse model was designed based on the finite impulse response filters using the iteratively reweighted least squares algorithm, which was used to compensate for the coupling between the degrees of freedom and reduce the fluctuations in the acceleration frequency characteristic. Iterative learning control was developed in the outer loop using the monotonic convergence condition of the iterative learning error to achieve monotonic error convergence. The experimental results of the 2-degree-of-freedom electro-hydraulic shaking table demonstrate that inversion-based iterative learning control enhanced the tracking accuracy of random acceleration periodic signals.