基于Koopman算子的模型预测牵引力控制系统
Model predictive traction control system based on the Koopman operator
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中文总结 AI 辅助
本文针对车辆牵引力控制问题，提出基于Koopman算子与模型预测控制的系统，经仿真验证其跟踪性能与非线性控制器相当，且执行时间更短。
中文摘要 AI 辅助
牵引力控制与防抱死制动系统因重要性已成为现代车辆的标准配置，但轮胎动力学的精确模型往往难以获取且常包含非线性特性，使其在控制系统中的应用颇具挑战。本文提出一种基于模型预测控制与Koopman算子理论的牵引力控制系统，旨在通过状态空间变换将非线性系统近似为线性系统。将基于Koopman预测器的线性模型预测控制器与标准非线性模型预测控制器进行对比，在高保真车辆动力学仿真环境中的实验表明，两种控制器的参考跟踪性能相当，而所提基于Koopman算子的算法在标准PC与嵌入式硬件上的执行时间均更短。
英文摘要
Due to their importance, traction control and anti-lock braking systems have become standard equipment in modern vehicles. However, accurate models of tire dynamics are often difficult to obtain and usually include nonlinearities, making their use in control systems challenging. This paper describes a traction control system based on model predictive control and Koopman operator theory, which aims to approximate nonlinear systems with linear ones through a state space transformation. A linear model predictive controller based on the Koopman predictor is compared to a standard nonlinear model predictive controller. Experiments in a high-fidelity vehicle dynamics simulation environment show a comparable reference tracking performance of the two controllers, with a reduced execution time for the proposed Koopman operator-based algorithm, both on a standard PC and embedded hardware.
