In order to break the bottleneck of the integer-order transfer function in vehicle ISD (inerter-spring-damper) suspension design, a positive real synthesis design method of vehicle mechatronic ISD suspension based on the fractional-order biquadratic transfer function is proposed. The emergence of the fractional-order components disrupts the equivalence relationship between the passivity of components and the positive realness of integer-order transfer functions in traditional networks. In this paper, the positive real condition of the fractional-order biquadratic transfer function is given. Then, a quarter dynamic model of the vehicle mechatronic ISD suspension is established, and the parameters of the fractional-order biquadratic transfer function and vehicle suspension are obtained by an NSGA-II multi-objective genetic algorithm. Moreover, the structure of the external circuit and the parameters of the electrical components are obtained by the fractional-order passive network synthesis theory. The simulation results show that under the condition of random road input and vehicle speed of 20 m/s, the root-mean-square (RMS) value of the vehicle body acceleration and the dynamic tire load of the fractional-order ISD suspension are reduced by 7.98% and 18.75% compared with the traditional passive suspension, while under the same condition, the integer-order ISD suspension can only reduce by 5.34% and 16.07%, respectively. The results show that employing a fractional-order biquadratic electrical network in the vehicle mechatronic ISD suspension enhances vibration isolation performance compared with the suspension using an integer-order biquadratic electrical network.

To explore an innovative approach for enhancing the vibration isolation performance of vehicle air suspension systems, this study introduces the inerter element into the vehicle air suspension and investigates the dynamic behavior of the semi-active vehicle air ISD (inerter-spring-damper) suspension. Initially, a dynamic model of the quarter semi-active vehicle air ISD suspension is established, followed by conducting multi-objective optimization of the core parameters using the genetic algorithm. Subsequently, the dynamic performance of the semi-active vehicle air ISD suspension is thoroughly examined through simulations and analyses in both the time and frequency domains. The results demonstrate notable improvements in various aspects: the root-mean-square (RMS) value of the vehicle body acceleration in the semi-active vehicle air ISD suspension is reduced by 12.8%, the RMS value of the suspension working space is decreased by 37.3%, and the RMS value of the dynamic tire load experiences an 8.9% reduction. The findings of this paper indicate that the proposed semi-active vehicle air ISD suspension outperforms both the passive vehicle air suspension and the semi-active vehicle air suspension without an inerter, significantly enhancing the vehicle’s ride comfort, handling stability, and driving safety.

This paper proposes an adaptive path tracking control method tailored for intelligent vehicles, aiming to enhance accuracy and stability. Initially, based on the traditional model predictive control (MPC) theory, the lateral speed stability boundary concerning the vehicle yaw rate is derived to establish the constraint conditions. Subsequently, optimal time domain parameters are determined across 100 typical curve conditions using a genetic algorithm. To achieve condition-adaptive path tracking control, speed and road curvature feedback are integrated into the MPC controller, enabling real-time adjustment of optimal control parameters. The simulation results from CarSim and Simulink co-simulation, as well as hardware-in-the-loop (HIL) experiments, demonstrate that the proposed method significantly improves path tracking accuracy for intelligent vehicles under varying curvature path conditions, outperforming both traditional MPC and higher-order sliding mode control (HOSMC) controllers.