The vehicle-mounted equipment is easy to be disturbed by external vibration excitations during transportation, which is harmful to the measurement accuracy and performance of the equipment. Aiming at the vibration isolation of the vehicle-mounted equipment, a semiactively controlled quasi-zero stiffness (QZS) vibration isolator with positive and negative stiffness is proposed. The vertical spring is paralleled with a magnetorheological (MR) damper, and the semiactive on-off control scheme is adopted to control the vibration. The analytical expression of the isolator’s displacement transmissibility is derived via the averaging method. Then, the vibration isolation performance under different road excitations and different driving speeds is simulated and compared with the uncontrolled passive QZS vibration isolator. In addition, the mechanical structure of the semiactive QZS isolator is designed and manufactured, and the test system is built by LabVIEW software and PXI embedded system. The isolation effect of the semiactive QZS isolator is verified through test data. It is found that the proposed semiactive QZS isolator shows excellent vibration isolation performance under various road excitations, while the passive QZS isolator is effective only under harmonic excitations. The vertical acceleration of vehicle-mounted device can be decreased over 70% after isolation, and the vibration isolation effect is remarkable. The design idea and research results of the semiactive QZS isolator may provide theoretical guidance and engineering reference for vibration isolation.

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The road friction coefficient and the forces between the tire and the road have a significant impact on the stability and precise control of the vehicle. For four-wheel independent drive electric vehicles, an adaptive tire force calculation method based on the improved Levenberg–Marquarelt multi-module and self-organizing feedforward neural networks (LM-MMSOFNN) was proposed to estimate the three-directional tire forces of four wheels. The input data was provided by common sensors amounted on the autonomous vehicle, including the inertial measurement unit (IMU) and the wheel speed/rotation angle sensors (WSS, WAS). The road type was recognized through the road friction coefficient based on the vehicle dynamics model and Dugoff tire model, and then the tire force was calculated by the neural network. The computational complexity and storage space of the system were also reduced by the improved LM learning algorithm and self-organizing neurons. The estimation accuracy was further improved by using the Extended Kalman Filter (EKF) and Moving Average (MA). The performance of the proposed LM-MMSOFNN was verified through simulations and experiments. The results confirmed that the proposed method was capable of extracting important information from the sensors to estimate three-directional tire forces and accurately adapt to different road surfaces.

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