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- Title
Active suspension control strategy for vehicles based on road surface recognition.
- Authors
Yang, Taiping; Li, Peiqing; Li, Qipeng; Li, Zhuoran
- Abstract
An adaptive model predictive control (AMPC) algorithm based on pavement identification was proposed to determine the influence of different pavement inputs on a vehicle suspension system. First, a vehicle dynamics model was established, and a discrete leveling index-based pavement comfort assessment method was proposed based on the international leveling index to quantify the comfort level by calculating the maximum instantaneous vibration index based on the vertical acceleration of the driver's seat. Second, an augmented Kalman filter algorithm with a forgetting factor is proposed to track the pavement time-varying parameters and estimate pavement leveling. Finally, the control of the quarter-vehicle active suspension system is transformed into solving the decay of hard constraints, designing the AMPC strategy, parameterizing the cost function of AMPC with the global cost of the performance index as the evaluation function, and using Bayesian optimization to predict the time domain and weight of the cost function to achieve global optimal performance. While satisfying the dynamic constraints, passenger comfort is improved by reducing the disturbance of the road pavements. Experimental results indicate that the proposed AMPC algorithm reduces the acceleration, suspension displacement and tire displacement by 26.4%, 10.3% and 8.0%, respectively, compared to the passive suspension when the vehicle speed is 30 km/h. The proposed algorithm reduces 5.6%, 2.7% and 4.2%, respectively, compared to the B-MPC algorithm accordingly. At the speed of 60 km/h, the acceleration of the sprung mass, suspension displacement and tire displacement is reduced by 23.4%, 13.1% and 10.3%, respectively, compared to the passive suspension. The proposed algorithm reduces by 7.8%, 3.0% and 3.7%, respectively, compared to the B-MPC algorithm accordingly. In real vehicle experiment, the acceleration of the sprung mass is reduced by 12.80% and 34.2%, respectively, and the tire displacement is reduced by 3.7% and 7.1%, respectively, compared to the B-MPC and MPC controller, improving the smoothness of the suspension and driver comfort. The effectiveness of the proposed control algorithm for different road pavements was verified.
- Subjects
MOTOR vehicle springs &; suspension; PAVEMENTS; COST functions; ACCELERATION (Mechanics); KALMAN filtering; VEHICLE models
- Publication
Nonlinear Dynamics, 2024, Vol 112, Issue 13, p11043
- ISSN
0924-090X
- Publication type
Article
- DOI
10.1007/s11071-024-09391-4