Shaking table testing is a common experimental method in earthquake engineering for performance assessment of structures subjected to dynamic excitations. As most shaking tables are driven by servo hydraulic actuators to meet the potentially significant force stroke demand, the review is restricted to hydraulic shaking tables. The purpose of the control systems of hydraulic shaking tables is to reproduce reference signals with low distortion. Accurate control of actuators is vital to the effectiveness of such apparatus. However, the system dynamics of a shaking table and the specimens to be tested on the shaking table are usually very complex and nonlinear. Achieving the control goal can prove to be challenging. A variety of closed- and open-loop control algorithms has been developed to solve different control problems. With the focus placed on the control schemes for hydraulic shaking tables, the paper reviews algorithms that are currently used in the testing industry, as well as those which are the subject of academic and industrial research. It is by no means a complete survey but provides key reference for further development.

The Savonius-type hydraulic turbine, mainly known for its good self-starting properties and simple structure, not only has energy capturing characteristics but also has a certain effect on flow velocity reduction. Aside from ensuring energy capture efficiency, studying the effects of parameters on the flow velocity reduction capacity is of great significance for the protection of mariculture, as it can reduce the damage to cages and fishes. In this study, a computational fluid dynamics method was carried out to investigate the hydrodynamic characteristics and variations in the wake of a turbine. The accuracy of the simulation results was verified by experimental comparison. Firstly, the velocity contours and vectors were studied in detail to reveal the mechanism of the flow velocity reduction effect. Secondly, the velocity attenuation coefficient and relative attenuation length were formulated by the variation rule of the velocity field to evaluate the turbine reduction strength and range. Finally, the power coefficient was considered to predict the performance of a turbine under different tip speed ratios, overlap ratios, blade curvatures, and blade numbers. The results showed that the turbine had an obvious flow velocity reduction effect in the rear “sword”-shaped area, where the velocity field distribution had a certain regularity. In addition, by comprehensively comparing the simulation data, it was found that the respective effect trend of tip speed ratio, blade number, overlap ratio, and curvature on the turbine’s energy capture and flow velocity reduction characteristics was basically the same. Considering the effect of reducing flow velocity, a two-bladed turbine with a blade curvature of 0.8 and an overlap ratio of 0.15 is the optimal configuration.

This paper focuses on an electro-hydraulic servo system, which is derived from a shaking table. It proposes a control scheme based on a back propagation (BP) neural network, whose weights are trained by the particle swarm optimization (PSO) according to the fitness, which is determined by the input and the feedback signals. Each particle of PSO includes weights and thresholds of BP. The movement of each particle is adjusted by its local best-known position and the global best-known position in the searching space. With the update, a satisfactory solution can be achieved. In order to show the performance of the proposed control scheme, the designed network is also trained and tested by BP only. The comparisons between the PSO-BP and BP networks demonstrate that the PSO-BP one has better performance than that of BP, both in convergence speed and in convergence accuracy.

Electro-hydraulic servo shaking table usually requires good control performance for acceleration replication. The poles of the electro-hydraulic servo shaking table are placed by three-variable control method using pole placement theory. The system frequency band is thus extended and the system stability is also enhanced. The phase delay and amplitude attenuation phenomenon occurs in electro-hydraulic servo shaking table corresponding to an acceleration sinusoidal input. The method for phase delay and amplitude attenuation elimination based on LMS adaptive filtering algorithm is proposed here. The task is accomplished by adjusting the weights using LMS adaptive filtering algorithm when there exits phase delay and amplitude attenuation between the input and its corresponding acceleration response. The reference input is weighted in such a way that it makes the system output track the input efficiently. The weighted input signal is inputted to the control system such that the output phase delay and amplitude attenuation are all cancelled. The above concept is used as a basis for the development of amplitude-phase regulation (APR) algorithm. The method does not need to estimate the system model and has good real-time performance. Experimental results demonstrate the efficiency and validity of the proposed APR control scheme.

Based on adaptive inverse control theory, combined with neural network, neural network adaptive inverse controller is developed and applied to an electro-hydraulic servo system. The system inverse model identifier is constructed by neural network. The task is accomplished by generating a tracking error between the input command signal and the system response. The weights of the neural network are updated by the error signal in such a way that the error is minimized in the sense of mean square using (LMS) algorithm and the neural network is close to the system inverse model. The above steps make the gain of the serial connection system close to unity, realizing waveform replication function in real-time. To enhance its convergence and robustness, the normalized LMS algorithm is applied. Simulation in which nonlinear dead-zone is considered and experimental results demonstrate that the proposed control scheme is capable of tracking desired signals with high accuracy and it has good real-time performance.

This work is focused on an underwater manipulator. To solve its nonlinear dynamics and hydrodynamics when it operates underwater, model reference adaptive control (MRAC) is applied. The output of the reference model represents the expected transient response. Adaptive controller parameters are adjusted on-line in real time according to the error between the reference model output and the system output and the outputs of the state filters, using an adaptive parameter regulator. The system control input is thus achieved such that the system output can track the reference model output. Simulation results demonstrate that the proposed control scheme has accurate tracking ability and can greatly improve the dynamic and static properties of the system. The MRAC for the underwater manipulator is also fairly robust to load disturbance and system parameter variation.