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 .

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.

Compared with other components, actuator fault has a higher probability of occurrence in semi-active suspension with magneto-rheological (MR) damper, which will lead to the safety and reliability of the system. Hence, the fault diagnosis and fault-tolerant methods of semi-active vehicle suspension system with MR damper are investigated in this paper to deal with the fault of MR damper. Firstly, the quarter-vehicle suspension system model is established. Secondly, an unknown input observer (UIO) with strong robustness and simple structure is employed to detect the fault of MR damper; meanwhile, the correlation coefficient method based on the system residuals is used to isolate the fault of MR damper. Lastly, the skyhook fault-tolerant controller (FTC) is designed to compensate the system with fault application. The simulation results under sine excitation, random excitation and bump excitation show that the performance of the proposed FTC always outweigh that of without fault-tolerant when MR damper occurs fault.

In the intelligent control system of magnetorheological (MR) suspension of all-terrain vehicle(ATV), the low-frequency disturbance(LFD) in the measured feedback signal (acceleration) makes the controller unable to calculate the theoretical control force accurately. Especially, when the frequency of the LFD signal is close to that of the actual acceleration signal, the LFD can’t be filtered by designing a traditional filter. Based on the above questions, this paper proposes an incremental proportion integration differentiation (IPID) strategy to address the issue of LFD in the measured feedback acceleration signal of the MR suspension system of ATV. First of all, the model of 1/4 vehicle suspension in consideration of LFD is established, the source of LFD is analyzed which is due to the transformation of Coriolis acceleration under the condition of vehicle body pitch and roll. Next, a semi-active IPID controller is designed by utilizing differential derivation of discrete PID and semi-active principle to eliminate the LFD component mixed in the acceleration signal by making a difference. The particle swarm optimization (PSO) algorithm is utilized to optimize the parameters of the controller, which is then verified through numerical simulation. Subsequently, a real vehicle control experiment is carried out based on a 4x4 ATV equipped with the MR suspension system and implemented by DSP controller with the designed IPID algorithm. The effectiveness of the prosed method is evaluated under the speed 10km/h and E road. And the designed method is compared with the traditional PID control algorithm through simulation and experimentation to demonstrate the superiority and rationality of "filtering out" LFD signal and improving control effectiveness.

No abstract