Active Suspension Systems (ASS) with control are gaining traction as researchers strive for optimal system performance. They are significant in diverse commercial vehicle applications, catering to user demands. This study employs the advanced Model Predictive Control (MPC) technique to enhance the smoothness and safety of a half-car model. The simulation results showed the prowess of MPC controllers under varied control force signal constraints, demonstrating superiority in curtailing vehicle chassis rotation angle and speed by up to 46.93% and 43.34%, respectively. The controller was compared with an artificial neural network controller utilizing only two state signals of the system, trained from MPC data, demonstrating high accuracy with R2 reaching 0.97024 and mean squared error at 7.3557×10-5. This study contributes to the refinement of ASS by focusing on practical implementation and performance enhancement.

This article presents the in-depth research results concerning the actively controlled suspension system following the Hybrid Active Suspension System (HASS) in vehicles. Initially, detailed introductions are provided for the controller and actuator of this suspension system model. Afterward, the HASS model is proposed for application to the Active Suspension System with integrating Skyhook and Groundhook control methods, wherein a coefficient α = 0.3,0.6,0.9 is utilized to adjust the correlation between these two models. Through the amplitude transfer function, the transition in enhancing ride comfort and road holding criteria in the HASS is demonstrated. Subsequently, the coefficients of the HASS are optimized through the Genetic Algorithm optimization with the parameter β = 0.1,0.5,0.9, aiming to enhance ride comfort and road holding corresponding to each value of α. The simulation and evaluation results utilizing the HASS in both frequency and time domains demonstrate significant improvements in ride comfort and road holding criteria compared to passive suspension systems. Specifically, the ride comfort has improved from 60% to 80%, while the road holding has improved from 10% to 15%. These findings facilitate the development of applications for the controlled suspension system, allowing adaptation to diverse real-world conditions at varying levels.

Controlled suspension systems, particularly active in-wheel suspension systems, are increasingly adopted in electric and autonomous vehicles due to their compact design and adaptability to various operating conditions. This study proposes the implementation of Linear Quadratic Regulator (LQR) controllers to improve vehicle smoothness and safety criteria. Genetic Algorithms (GA) are employed to optimize the weighting parameter values in the objective function in LQR controller, which allow them to adapt to the vehicle's condition. The simulation results demonstrate that the proposed controller model enhances system performance by up to 14% in comparison with conventional models. These findings suggest that the proposed system significantly enhances the feasibility of meeting user requirements in modern vehicle applications.

The antiroll bar (ARB) plays an important role in reducing the rollover phenomenon and uneven load distribution between the wheels on an axle, thus enhancing the safety and roll stability of vehicles. This study proposes a method for optimizing the torsional stiffness of the ARB on trucks, with three main objectives: (1) building a general model of a two‐axle truck equipped with an ARB on both axles; (2) establishing a multiobjective optimization problem for torsional stiffness using a genetic algorithm; (3) optimizing the ARB stiffness using the Gaussian distribution method through the analysis of 120 different operating scenarios, including double‐lane‐change (DLC) obstacle avoidance and DLC obstacle overtake, as well as steady‐state cornering, at different speeds. The simulation results determined that the optimal torsional stiffness of the ARB on the front axle is 48,002.9798 Nm/rad and on the rear axle is 25,901.801 Nm/rad for a 15‐ton truck. This torsional stiffness value helps reduce the dynamic tire forces acting on the road surface by 6.4% and reduces the load transfer ratio coefficient by up to 20%. These results confirm the effectiveness of the proposed method in improving roll stability, reducing the risk of rollover, and optimizing the torsional stiffness design of the ARB for trucks under various operating conditions.