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Parameter Identification of Tire Model Based on Improved Particle Swarm Optimization Algorithm

机译:基于改进粒子群优化算法的轮胎模型参数识别

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Accurate parameters of vehicle motion state are very important to the active safety of a vehicle. Currently the extended Kalman filter and unscented Kalman filter are widely used in estimation of the key state parameters, such as speed. In this situation, tire model must be used. The Magic Formula Tire Model is widely used in vehicle dynamics simulation because of its high versatility and accuracy. However, it requires a large number of parameters, which make the key state parameters of a real vehicle difficult to accurately obtain. Therefore, it is limited in real-time control of a vehicle. Firstly, the original Magic Formula Tire Model is simplified in this paper; then Jin Chi's Tire Model is introduced; thirdly, parameters of both the simplified Magic Formula and Jin Chi's Tire Model are identified using PSO (Particle Swarm Optimization) algorithm. Finally, Jin Chi's Tire Model is also used in parameters identification of experimental data.
机译:车辆运动状态的准确参数对于车辆的主动安全性非常重要。目前,扩展的卡尔曼滤波器和unspented卡尔曼滤波器广泛用于估计键态参数,例如速度。在这种情况下,必须使用轮胎模型。由于其高通用性和准确性,神奇公式轮胎模型广泛用于车辆动力学模拟。然而,它需要大量参数,这使得实际车辆的关键状态参数难以准确地获得。因此,在车辆的实时控制中受到限制。首先,本文简化了原始魔术配方轮胎模型;然后介绍了金驰的轮胎模型;第三,使用PSO(粒子群优化)算法来识别简化的魔术公式和金Chi轮胎模型的参数。最后,金志的轮胎模型也用于实验数据的参数识别。

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