Trajectory-Tracking Control of Unmanned Vehicles Based on Adaptive Variable Parameter MPC
Source abstract
Abstract
Aiming at the problems of the poor trajectory-tracking performance and low control accuracy of unmanned vehicles under complex working conditions, we first estimate the lateral force of tires using the square root cubature Kalman filter (SRCKF) in order to correct the lateral stiffness of the tires online, which reduces the model bias caused by constant lateral stiffness, and then adopt a Gaussian function-based adaptive time-domain model predictive control method to improve the trajectory-tracking control accuracy of unmanned vehicles under complex working conditions. Finally, the proposed control algorithm is validated via Carsim and MATLAB/Simulink joint simulation. The results show that compared with the classical model predictive control (MPC) algorithm, the proposed control algorithm reduces the average lateral tracking error by 73.07% and the peak beta and the peak yaw rate by 50.89% and 47.51%, respectively, so that the unmanned vehicle is able to maintain good tracking performance and control accuracy.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Vehicle Dynamics and Control Systems · Soil Mechanics and Vehicle Dynamics · Hydraulic and Pneumatic Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Automation, Beijing Information Science & Technology University, Beijing 100192, China
Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing Information Science & Technology University, Beijing 100192, China
Funding
Beijing Municipal Education Commission Research Project Funding · Beijing Natural Science Foundation Project · Beijing Key Laboratory of High Dynamic Navigation Technology
References (32)
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