Path Tracking Control Based on Model Predictive Control With Adaptive Preview Characteristics and Speed-Assisted Constraint

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Abstract
As one of the research focuses in the field of intelligent driving, improving the performance of path tracking has become a goal for many scholars. Among many path tracking control algorithms, model predictive control (MPC) controllers are widely used due to their excellent control performance. However, the traditional MPC control has shortcomings because it does not consider the particularity of the driving car with preview driving characteristics, i.e., it is only directly controlling from the vehicle state. To fully retain the advantages of the MPC controller and simultaneously exert the preview characteristic of the intelligent vehicle to improve the path tracking performance, this work proposes a model predictive control with adaptive preview characteristics and an algorithm of longitudinal vehicle speed-assisted constraint for the path tracking algorithm. The algorithm mainly consists of two parts: The MPC controller with adaptive preview characteristic is proposed based on the lateral error and target curvature; The longitudinal vehicle speed-assisted constraint algorithm based on the largest ideal lateral acceleration becomes a supplementary part of the algorithm as a supplementary constraint of the MPC controller. Series of simulations based on the Simulink/CARSIM software individually verified the model predictive control with adaptive preview characteristics and an algorithm of longitudinal vehicle speed-assisted constraint for the path tracking algorithm. The proposed adaptive preview strategy is suitable for the path tracking algorithm controlled by the MPC controller and improves the path tracking performance.
Funding Information
  • National Natural Science Foundation of China (51575224, 51505178)
  • China Postdoctoral Science Foundation (2014M561289)

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