Road conditions are of critical importance for motion control problems of the autonomous vehicle. In the existing studies of Model Predictive Control (MPC), road condition is generally modeled with the system dynamics, sometimes simplified as common disturbances, or even ignored based on some assumptions. For most of such MPC formulations, the cost function is usually designed as fixed function and has no relations with the time-varying road conditions. In order to comprehensively deal with the uncertain road conditions and improve the overall control performance, a new model predictive control strategy based on a mechanism of adaptive cost function is proposed in this paper. The relation between the cost function and road conditions is established based on a set of priority policies which reflect the different cost requirements under different road grades and friction coefficients. The adaptive MPC strategy is applied to solve the longitudinal control problem of autonomous vehicles. Simulation studies are conducted on the MPC method with both the fixed cost function and the adaptive cost function. The results show that the proposed adaptive MPC approach can achieve a better overall control performance under different road conditions.
- Dynamic Systems and Control Division
Road Condition Based Adaptive Model Predictive Control for Autonomous Vehicles
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Wang, X, Guo, L, & Jia, Y. "Road Condition Based Adaptive Model Predictive Control for Autonomous Vehicles." Proceedings of the ASME 2018 Dynamic Systems and Control Conference. Volume 3: Modeling and Validation; Multi-Agent and Networked Systems; Path Planning and Motion Control; Tracking Control Systems; Unmanned Aerial Vehicles (UAVs) and Application; Unmanned Ground and Aerial Vehicles; Vibration in Mechanical Systems; Vibrations and Control of Systems; Vibrations: Modeling, Analysis, and Control. Atlanta, Georgia, USA. September 30–October 3, 2018. V003T37A005. ASME. https://doi.org/10.1115/DSCC2018-9095
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