Abstract
In most cases, a vehicle works in a complex environment, with working conditions changing frequently. For most model predictive tracking controllers, however, the impacts of some important working conditions, such as speed and road conditions, are not concerned. In this regard, an adaptive model predictive controller is proposed, which improves tracking accuracy and stability compared with general model predictive controllers. First, the proposed controller utilizes the recursive least square algorithm to estimate tire cornering stiffness and road friction coefficient online. Then, the estimated tire cornering stiffness is used to update vehicle dynamics model and the estimated road friction coefficient is used to update the road adhesion constraint. Moreover, the control parameters consist of prediction horizon, control horizon, and sampling time, all of which are set according to vehicle speed. A co-simulation based on MATLAB/Simulink and CarSim is conducted. The simulation results illustrate that the proposed controller has a great adaptive ability to changing working conditions, especially to speed and road conditions.
Highlights
With the rapid growth of city, traffic conditions are getting worse and worse, which leads to frequent traffic accidents
The estimated road friction coefficient is used to update the road adhesion constraint in model predictive control (MPC) controller, which can promote the adaptive ability to road conditions
When the road condition changes, the adaptive model predictive control (AMPC) controller A has better stability and path tracking performance than the general MPC controller B, which proves the adaptive ability of the AMPC controller
Summary
With the rapid growth of city, traffic conditions are getting worse and worse, which leads to frequent traffic accidents. Ercan et al considered the dynamics of vehicle steering system and identified the parameters of steering system model online with recursive least squares algorithm.[25] an MPC path tracking controller was designed based on the real-time updating model. Chen et al proposed an adaptive model predictive lane-keeping system based on linear timevarying model.[26] Recursive least square algorithm was used to identify tire cornering stiffness online in real time, and vehicle speed in the prediction layer was predicted by longitudinal acceleration These papers made some improvements for the MPC algorithm, the main contribution was the correction of vehicle model parameters, which often changes slightly in the process of path tracking. Fcf equations (1) and (2), the 2-DOF vehicle dynamics equations can be obtained as follows
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