The vehicle suspension system plays a vital role in ensuring a comfortable and safe ride for passengers. A well-designed suspension system can minimize the body acceleration and tire friction caused by uneven road surfaces, thereby giving passengers a smooth and stable ride. Therefore, optimizing the suspension system's control strategy is of great importance to improve the ride comfort and handling performance of the vehicle.
One approach to achieving this is to apply a linear-quadratic regulator (LQR) control strategy to the vehicle active suspension system (ASS). LQR is a well-known control design method that can be used to achieve optimal performance in terms of minimizing a cost function that captures control objectives. However, the LQR's weighting parameters need to be carefully tuned to balance conflicting control objectives, such as ride comfort and vehicle handling.
To overcome this challenge, an adaptive inertia weight particle swarm optimization (AIWPSO) algorithm has been proposed in recent research for solving the multiobjective weight optimization problem in LQR applied to the vehicle ASS. AIWPSO tunes the weighting parameters of LQR to strike a balance between competing control objectives, such as ride comfort, road handling, and suspension travel.
The AIWPSO algorithm uses an adaptive inertia weight approach to update the particle velocities based on the success rate. This approach mitigates premature convergence of the optimization process that can occur in the standard PSO algorithm. Furthermore, it balances the exploration and exploitation capabilities of the algorithm to produce better solutions.
To evaluate the efficacy of the AIWPSO-tuned LQR, a hardware-in-loop (HIL) test on a quarter-car ASS plant was conducted. The experiment simulated an uneven road surface. The experimental results showed that the AIWPSO-tuned LQR significantly reduced vehicle body acceleration due to irregular road profiles compared to conventional PSO-tuned LQR. The proposed scheme also ensured minimum tire friction for passenger safety while improving ride comfort.
The International Organization for Standardization (ISO) 2361-1 standards were used to evaluate the ride and health criteria. The experimental results showed that the proposed scheme reduced the vibration dose value by 25.34% for a bumpy road profile. Moreover, the cumulative power spectral density (CPSD) of vehicle body acceleration in both low- and high-frequency regions demonstrated a significant improvement in ride comfort.
In conclusion, the AIWPSO algorithm is an effective approach for optimizing the control strategy of the vehicle suspension system. The HIL experiment results showed that the proposed scheme significantly improved ride comfort while ensuring passenger safety. The AIWPSO-tuned LQR can be used to balance conflicting control objectives and improve the overall performance of the vehicle ASS. Therefore, it is a promising solution for future vehicle suspension system design and development.