| Intelligent Vehicle Adaptive Robust Path Tracking Control Strategy Based on MPC and ADRC |
| Zhendong Zhu, Qiangqiang Yao, Yiheng Shi |
| School of Mechanical Engineering, Qinghai University, Xining, 810016, China |
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Received: June 25, 2025; Revised: August 5, 2025 Accepted: August 6, 2025. Published online: September 4, 2025. |
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| ABSTRACT |
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A composite path tracking strategy that integrates Adaptive Model Predictive Control (AMPC) and Improved Active Disturbance Rejection Control (IADRC) is proposed to improve the path tracking accuracy and anti-interference performance of autonomous vehicles under complex working conditions and unknown disturbances. The strategy first employs a Linear Time-Varying Model Predictive Controller (LTV-MPC) with dynamic constraints, including front wheel angle and side deflection angle limits, to ensure the feasibility of the optimization problem. At the same time, an adaptive trajectory tracking controller (AMPC) is designed to update the predicted time domain in real-time, balancing control performance and computational complexity. To address tracking errors caused by model inaccuracies and external disturbances, an Immune Genetic Algorithm (IGA) is used to optimize the IADRC parameter tuning. A smoothing nonlinear function is introduced to create an objective function that couples lateral deviation with swing angle deviation, allowing for accurate compensation. Simulation results show that the proposed AMP-IADRC controller reduces the maximum lateral deviation by approximately 30.63% on average under low adhesion conditions (μ = 0.4). The controller also demonstrates strong robustness, improving tracking accuracy and driving stability under adverse road conditions. |
| Key Words:
Intelligent vehicle · Adaptive model predictive control · Immunogenetic algorithm · Improved active disturbance rejection control · Path tracking |
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Corresponding Author.
Qiangqiang Yao , Email. 
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