Jinpeng Yang

To address the limitations of existing quarter-vehicle models in capturing pitch motion and front-rear coupling effects, this paper proposes a half-vehicle mechatronic suspension system based on the electromechanical analogy. Traditional methods often overlook non-ideal effects and the dynamic interaction between the front and rear wheels. This paper constructs an equivalent electrical network model for the half-vehicle suspension system. To ensure the physical realizability of the system, parameter optimization is performed under positive-real constraints using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). This approach achieves an optimal trade-off between vertical vibration suppression and pitch control. Simulation results under random road input at a vehicle speed of 20 m/s indicate that while the unconstrained mechatronic suspension improves ride comfort, it increases the dynamic tire load by 19.18%. In contrast, the constrained mechatronic suspension reduces RMS vertical body acceleration by 19.54% and pitch angular acceleration by 2.22% compared to the standard passive suspension. Additionally, a reduction of 8.29% was observed in the suspension working space RMS, alongside a 1.26% decrease in the dynamic tire load. These results demonstrate that introducing appropriate positive-real constraints effectively balances ride comfort and road-holding performance, providing a systematic modeling and optimization framework for half-vehicle mechatronic suspensions.

To further tap into the vibration isolation potential of vehicle mechatronic ISD (Inerter–Spring–Damper) suspension systems, a positive real synthesis design method for vehicle ISD suspensions is proposed based on the fractional-order bridge networks. Firstly, a quarter-car dynamic model of the mechatronic ISD suspension system is established. Subsequently, based on the impedance solution method for bridge networks, a basic bridge network structure and its corresponding series-parallel network configuration are constructed for comparative analysis. Then, with the aim of improving the overall performance of the vehicle suspension system, the particle swarm optimization algorithm is employed to determine the optimal parameters for the mechatronic ISD suspension. Finally, simulation results under random road excitation at a vehicle speed of 20 m/s indicate that, compared to the traditional passive suspension system, the fractional-order bridge network-based ISD suspension system achieves reductions of 3.9%, 27.5%, and 11.8% in the root mean square (RMS) values of vehicle body acceleration, suspension working space, and dynamic tire load, respectively. In contrast, the integer-order bridge network-based ISD suspension system only achieves reductions of 3.0%, 24.2%, and 4.5% in these respective performance metrics. The results confirm that, with an equivalent number of components, the fractional-order bridge network exhibits superior vibration isolation performance relative to series-parallel networks, thereby providing novel methodological guidance for the design of vehicle mechatronic ISD suspension.