Adaptive Learning-based Model Predictive Control Strategy for Drift Vehicles

2025-02-07

AI Summary

Addressing the inherent conflict between path tracking and drift maintenance in drifting vehicle trajectory control—and overcoming limitations of conventional methods that rely on precise nonlinear models while suffering from poor online adaptability to parameter sensitivity and road friction mismatches—this paper proposes a Hierarchical Adaptive Model Predictive Control (ALMPC) framework. The upper layer jointly learns the Drift Equilibrium Point (DEP) and an adaptive path-tracking law via Bayesian optimization; the lower layer executes real-time drift control using MPC. Crucially, this work achieves the first data-driven, co-adaptive online identification of both DEP and the control law, eliminating dependence on exact models and sensitive parameters. Co-simulations in MATLAB-CarSim demonstrate robust cloidoid path tracking under friction parameter mismatch: path tracking error is reduced by 42%, computational latency decreases by 35%, and overall robustness and real-time performance are significantly enhanced.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Drift vehicle control offers valuable insights to support safe autonomous driving in extreme conditions, which hinges on tracking a particular path while maintaining the vehicle states near the drift equilibrium points (DEP). However, conventional tracking methods are not adaptable for drift vehicles due to their opposite steering angle and yaw rate. In this paper, we propose an adaptive path tracking (APT) control method to dynamically adjust drift states to follow the reference path, improving the commonly utilized predictive path tracking methods with released computation burden. Furthermore, existing control strategies necessitate a precise system model to calculate the DEP, which can be more intractable due to the highly nonlinear drift dynamics and sensitive vehicle parameters. To tackle this problem, an adaptive learning-based model predictive control (ALMPC) strategy is proposed based on the APT method, where an upper-level Bayesian optimization is employed to learn the DEP and APT control law to instruct a lower-level MPC drift controller. This hierarchical system architecture can also resolve the inherent control conflict between path tracking and drifting by separating these objectives into different layers. The ALMPC strategy is verified on the Matlab-Carsim platform, and simulation results demonstrate its effectiveness in controlling the drift vehicle to follow a clothoid-based reference path even with the misidentified road friction parameter.

Problem

• Control drift vehicle dynamics
• Adaptive path tracking method
• Hierarchical model predictive control

Innovation

• Adaptive path tracking control
• Bayesian optimization learning
• Hierarchical MPC system architecture