This paper addresses the problem of micro-vibration control of a precision vibration isolation system with a magnetorheological elastomer (MRE) isolator and fuzzy control strategy. Firstly, a polyurethane matrix MRE isolator working in the shear-compression mixed mode is introduced. The dynamic characteristic is experimentally tested, and the range of the frequency shift and the model parameters of the MRE isolator are obtained from experimental results. Secondly, a new semi-active control law is proposed, which uses isolation structure displacement and relative displacement between the isolation structure and base as the inputs. Considering the nonlinearity of the MRE isolator and the excitation uncertainty of an isolation system, the designed semi-active fuzzy logic controller (FLC) is independent of a system model and is robust. Finally, the numerical simulations and experiments are conducted to evaluate the performance of the FLC with single-frequency and multiple-frequency excitation, respectively, and the experimental results show that the acceleration transmissibility is reduced by 54.04% at most, which verifies the effectiveness of the designed semi-active FLC. Moreover, the advantages of the approach are demonstrated in comparison to the passive control and ON-OFF control.

During driving process, the impact from the road, such as pothole or bump can deteriorate the performance of the vehicle, and increase the driving dangerousness and health risk of drivers/passengers due to high energy and short time. The magneto-rheological (MR) intelligent suspension plays an important role in improving the ride comfort of vehicle, which can reduce the impact transmitted from the road. In consideration of different impact application induced by different driving speed, an adaptive skyhook control (ASC) based on improved Genetic Algorithm (GA) is proposed in this paper. Vibration dose value (VDV) of vertical acceleration of floor at driver’s position is selected as the object function for improved GA to obtain the optimal skyhook gain coefficients under different driving speeds. And then the adaptive law is formulated by using table lookup method. The road test for the B segment car installed with MR dampers is undertaken to verify the effectiveness of the designed controller. The experimental results demonstrate that the reduction of VDV and peak value of vertical acceleration with ASC is always more than that with traditional skyhook controller. Results indicate that the energy spectrum density amplitude of using ASC algorithm is lower than that with skyhook controller, when the driving speed is less than or equal to 30 km h ⁻¹ .

Due to the controllability of the stiffness and damping under the applied magnetic field, magnetorheological elastomer isolator has been proved effective in the field of vibration control. For the realization of vibration control application, an accurate MRE isolator model is a non-trivial task. However, the existing parametric modeling methods are required to identify too many parameters, which are difficult to implement. Moreover, the corresponding inverse dynamic model of the isolator cannot even be obtained by the identified model inversion. Therefore, this paper proposes a nonparametric neural network approach to approximate the dynamic behaviors of magnetorheological elastomer isolator with the characteristics of nonlinearity and hysteresis. Firstly, the dynamic characteristics of the isolator in shear-compression mixed mode are experimentally tested under different loading conditions. Secondly, based on the experimental data, a NARX neural network with three-layer structure is developed to approximate the functional relationship between inputs (displacement, velocity and current) and output (force) of magnetorheological elastomer isolator. Thirdly, the effectiveness of the network model is validated by comparing the predicted force and experimental force. Finally, considering the common occurrence of inputs with noise disturbance in real application, the robustness of the network is also verified for displacement and current inputs with noise disturbance, respectively. The results of the network generalization for experimental data show that the proposed NARX network is more robust and optimal than BP network.

Magnetorheological elastomer (MRE) vibration isolation method has attracted more attention in vibration engineering fields. Time delay in real time control system has an important influence on the control effect, especially for the vibration system with periodic excitations. For the phase difference between the response signal and controlled signal caused by time delay will reduce the vibration control effect or even make the vibration more intense. Therefore, the main purpose in this paper is to improve the vibration attenuation of the MRE vibration control system by analyzing the cause of the time delay and designing the time-delay compensator (TDC) based on the traditional fuzzy controller. The time delay in MRE vibration control system, may be generated by controller, actuator, filter and current driver, was analyzed in theory, and experimental test was carried out to estimate the value. Then a constant TDC was designed for the various vibration amplitude and frequency (single frequency and multiple frequencies). Finally, the real-time control experiments were conducted to verify the effectiveness of the proposed TDC, and the results illustrated that the acceleration RMS with TDC has been attenuated more greatly than that without TDC. Meanwhile, the lower energy consumption is another benefit.

In this article, a robust state-feedback H_∞ control for semi-active scissors linkage seat suspension with magnetorheological damper is investigated to reduce low-frequency and high-amplitude vibration, leading to health disorders in drivers or passengers. First, the stiffness and damping characteristics of the semi-active scissors linkage seat suspension are analyzed and a simplified model of the semi-active scissors linkage seat suspension is introduced. Then, the forward and inverse models of magnetorheological damper are described by the neural network method. Furthermore, the robust state-feedback H_∞ control is established by considering the system uncertainties. The proposed approach is finally validated by experiment on a test rig under different sinusoidal excitations and load masses. Experimental results show that the human vibration is reduced up to 47.66% compared with the uncontrolled system.