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针对车辆行驶稳定性实时且精确控制的挑战，结合李雅普诺夫指数与BP神经网络对车辆稳定性进行判定，再以其判定结果辅以模型预测控制算法进行行驶稳定性控制。此方案旨在迅速评估车辆行驶状态，确保行驶过程中的稳定性。所构建的车辆行驶稳定性判定模型，运用李雅普诺夫指数法，在不同行驶工况下离线计算出稳定性指标。这些指标被用作训练数据，以优化BP神经网络模型。经过试验验证，该神经网络模型展现出了较高的预测精度。基于上述稳定性判定模型，设计模型预测控制器。该控制器以稳定性判定模型输出作为介入前提，能紧密跟踪期望的横摆角速度与质心侧偏角，从而实现后轮转角的精确调控。为验证所设计的控制策略的有效性，采用Simulink与CarSim软件的联合仿真方法。仿真结果表明，所提出的控制系统能够依据车辆稳定性状况进行及时且准确的控制介入，显著提升了车辆行驶的稳定性。
Abstract：Addressing the challenge of real-time and precise control of vehicle driving stability, this paper presents an innovative solution: a vehicle stability determination method that integrates Lyapunov exponents with BP neural networks, followed by model predictive control(MPC) for stability maintenance during driving. The aim of this approach is to rapidly assess the vehicle's driving state and ensure stability throughout the journey. The constructed vehicle driving stability determination model first employs the Lyapunov exponent method to calculate stability indices offline under various driving conditions. These indices are then used as training data to optimize the BP neural network model, which demonstrates high prediction accuracy after testing and validation. Based on this stability determination model, we further designed an MPC controller capable of closely tracking the desired yaw rate and sideslip angle, enabling precise regulation of the rear wheel steering angle. To verify the effectiveness of the designed control strategy, we adopted a co-simulation method using Simulink and CarSim software. The simulation results indicate that the proposed control system can intervene timely and accurately based on the vehicle's stability status, significantly enhancing the driving stability.
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基本信息:
DOI：10.19886/j.cnki.dhdz.2024.0282
中图分类号:TP183;U463.6
引用信息:
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