There exist two intrinsic shortcomings on model predictive control (MPC) strategy, namely the extensive online calculation burden and the complex tuning process, which prevent MPC from being applied to a wider extent. To tackle these two drawbacks, different methods were proposed with majority of them treating these two issues independently. However, parameter tuning in fact has double-sided effects on both controller performance as well as real-time computational burden. Due to the lack of theoretical tools for globally analyzing the complex conflicts among MPC parameter tuning, controller performance optimization as well as computational burden easement, a look-up table based online parameter selection method is proposed in this paper to help a vehicle track its reference path under both the stability and computational capacity constraints. Matlab-CarSim conjoint simulation shows the effectiveness of the proposed strategy.
- Dynamic Systems and Control Division
Parameter Selection of an LTV-MPC Controller for Vehicle Path Tracking Considering CPU Computational Load
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Wang, Z, Bai, Y, Wang, J, & Wang, X. "Parameter Selection of an LTV-MPC Controller for Vehicle Path Tracking Considering CPU Computational Load." Proceedings of the ASME 2018 Dynamic Systems and Control Conference. Volume 2: Control and Optimization of Connected and Automated Ground Vehicles; Dynamic Systems and Control Education; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Energy Systems; Estimation and Identification; Intelligent Transportation and Vehicles; Manufacturing; Mechatronics; Modeling and Control of IC Engines and Aftertreatment Systems; Modeling and Control of IC Engines and Powertrain Systems; Modeling and Management of Power Systems. Atlanta, Georgia, USA. September 30–October 3, 2018. V002T15A005. ASME. https://doi.org/10.1115/DSCC2018-9129
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