Parameter Identification of Magic Formula Tire Model Based on Fibonacci Tree Optimization Algorithm

来源 :上海交通大学学报(英文版) | 被引量 : 0次 | 上传用户:elongyu999
下载到本地 , 更方便阅读
声明 : 本文档内容版权归属内容提供方 , 如果您对本文有版权争议 , 可与客服联系进行内容授权或下架
论文部分内容阅读
The magic formula (MF) tire model is a semi-empirical tire model that can precisely simulate tire behavior.The heuristic optimization algorithm is typically used for parameter identification of the MF tire model.To avoid the defect of the traditional heuristic optimization algorithm that can easily fall into the local optimum,a parameter identification method based on the Fibonacci tree optimization (FTO) algorithm is proposed,which is used to identify the parameters of the MF tire model.The proposed method establishes the basic structure of the Fibonacci tree alternately through global and local searches and completes optimization accordingly.The global search rule in the original FTO was modified to improve its efficiency.The results of independent repeated experiments on two typical multimodal function optimizations and the parameter identification results showed that FTO was not sensitive to the initial values.In addition,it had a better global optimization performance than genetic algorithm (GA) and particle swarm optimization (PSO).The root mean square error values optimized with FTO were 5.09%,10.22%,and 3.98% less than the GA,and 6.04%,4.47%,and 16.42% less than the PSO in pure lateral and longitudinal forces,and pure aligning torque parameter identification.The parameter identification method based on FTO was found to be effective.
其他文献
自然资源监测是各地自然资源部门日常管理中一项极端重要的工作,而传统的监测方法在高效性、便利性和准确性等方面还存在一定的问题,很难保证监测工作的体系化、监测成果的时效性.依托“互联网+”等技术,以太仓市为例,研究和探索利用实时监控视频、多源遥感、无人机等多种监测手段对自然资源进行动态监测的路径,进一步提高自然资源监测的工作效率,实现决策科学化、应用智能化.
Environmental perception is a key technology for autonomous driving.Owing to the limitations of a single sensor,multiple sensors are often used in practical applications.However,multi-sensor fusion faces some problems,such as the choice of sensors and fus
Human beings have been kept pursuing of higher ef-ficiency and better safety to move people and things around since thousands of years ago.In modern soci-ety,vehicles are therefore invented and utilized to boost the speed and enhance the safety.In recent
期刊
High-definition (HD) maps are key components that provide rich topologic and semantic information for decision-making in vehicle autonomous driving systems.A complete ground orthophoto is usually used as the base image to construct the HD map.The ground o
As an emerging visual task,vehicle re-identification refers to the identification of the same vehicle across multiple cameras.Herein,we propose a novel vehicle re-identification method that uses an improved ResNet-50 architecture and utilizes the topology
In this study,a multi-object tracking (MOT) scheme based on a light detection and ranging sensor was proposed to overcome imprecise velocity observations in object occlusion scenarios.By applying real-time velocity estimation,a modified unscented Kalman f
Analyzing a vehicle\'s abnormal behavior in surveillance videos is a challenging field,mainly due to the wide variety of anomaly cases and the complexity of surveillance videos.In this study,a novel intelligent vehicle behavior analysis framework based
Multi-object tracking is a vital problem as many applications require better tracking approaches.Although learning-based detectors are becoming extremely powerful,there are few tracking methods designed to work with them in real time.We explored an effici
Contemporary autonomous-driving technology relies on good environmental-perception systems and high-precision maps.For unknown environments or scenarios where perception fails,a human-in-the-loop remote-driving system can effectively complement common sol
In real-world scenarios,the uncertainty of measurements cannot be handled efficiently by traditional model predictive control (MPC).A stochastic MPC (SMPC) method for handling the uncertainty of states in autonomous driving lane-keeping scenarios is prese