{"title":"基于车辆多体动力学模型的循环神经网络和粒子群优化相结合的侧滑角估计方法","authors":"Yu Sun, Yongjun Pan, Ibna Kawsar, Gengxiang Wang, Liang Hou","doi":"10.1007/s11044-024-09973-5","DOIUrl":null,"url":null,"abstract":"<p>The active safety system of a vehicle typically relies on real-time monitoring of the sideslip angle and other critical signals, such as the yaw rate. The vehicle sideslip angle cannot be measured directly due to the high cost and impracticality of sensor networks. The vehicle sideslip can be estimated using kinematic, dynamic, or machine-learning models and available vehicle states. This paper combines recurrent neural networks and the particle-swarm optimization (PSO) algorithm to estimate the vehicle sideslip angle accurately. First, a vehicle-dynamics model is constructed to conduct dynamics simulations of vehicles under various driving conditions and road environments for data collection. Secondly, the obtained vehicle states, including velocity, acceleration, yaw rate, and steering, are used to develop machine-learning models that estimate the vehicle sideslip angle. Two machine-learning models are proposed using the long short-term memory neural network (LSTM) and the bidirectional long short-term memory neural network (BiLSTM). Thirdly, the PSO algorithm is employed to optimize the hyperparameters of the LSTM and BilLSTM models for enhanced estimation precision. The Gaussian noise is added to the datasets to evaluate the robustness of the estimation models. The results indicate that the estimation models are capable of accurately predicting the vehicle’s sideslip angle. The <span>\\(R^{2}\\)</span> values of the results are mostly greater than 0.96. The PSO algorithm can improve estimation precision, and the PSO-LSTM model performs the best.</p>","PeriodicalId":49792,"journal":{"name":"Multibody System Dynamics","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Combined recurrent neural networks and particle-swarm optimization for sideslip-angle estimation based on a vehicle multibody dynamics model\",\"authors\":\"Yu Sun, Yongjun Pan, Ibna Kawsar, Gengxiang Wang, Liang Hou\",\"doi\":\"10.1007/s11044-024-09973-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The active safety system of a vehicle typically relies on real-time monitoring of the sideslip angle and other critical signals, such as the yaw rate. The vehicle sideslip angle cannot be measured directly due to the high cost and impracticality of sensor networks. The vehicle sideslip can be estimated using kinematic, dynamic, or machine-learning models and available vehicle states. This paper combines recurrent neural networks and the particle-swarm optimization (PSO) algorithm to estimate the vehicle sideslip angle accurately. First, a vehicle-dynamics model is constructed to conduct dynamics simulations of vehicles under various driving conditions and road environments for data collection. Secondly, the obtained vehicle states, including velocity, acceleration, yaw rate, and steering, are used to develop machine-learning models that estimate the vehicle sideslip angle. Two machine-learning models are proposed using the long short-term memory neural network (LSTM) and the bidirectional long short-term memory neural network (BiLSTM). Thirdly, the PSO algorithm is employed to optimize the hyperparameters of the LSTM and BilLSTM models for enhanced estimation precision. The Gaussian noise is added to the datasets to evaluate the robustness of the estimation models. The results indicate that the estimation models are capable of accurately predicting the vehicle’s sideslip angle. The <span>\\\\(R^{2}\\\\)</span> values of the results are mostly greater than 0.96. The PSO algorithm can improve estimation precision, and the PSO-LSTM model performs the best.</p>\",\"PeriodicalId\":49792,\"journal\":{\"name\":\"Multibody System Dynamics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-02-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Multibody System Dynamics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s11044-024-09973-5\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MECHANICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Multibody System Dynamics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s11044-024-09973-5","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MECHANICS","Score":null,"Total":0}
Combined recurrent neural networks and particle-swarm optimization for sideslip-angle estimation based on a vehicle multibody dynamics model
The active safety system of a vehicle typically relies on real-time monitoring of the sideslip angle and other critical signals, such as the yaw rate. The vehicle sideslip angle cannot be measured directly due to the high cost and impracticality of sensor networks. The vehicle sideslip can be estimated using kinematic, dynamic, or machine-learning models and available vehicle states. This paper combines recurrent neural networks and the particle-swarm optimization (PSO) algorithm to estimate the vehicle sideslip angle accurately. First, a vehicle-dynamics model is constructed to conduct dynamics simulations of vehicles under various driving conditions and road environments for data collection. Secondly, the obtained vehicle states, including velocity, acceleration, yaw rate, and steering, are used to develop machine-learning models that estimate the vehicle sideslip angle. Two machine-learning models are proposed using the long short-term memory neural network (LSTM) and the bidirectional long short-term memory neural network (BiLSTM). Thirdly, the PSO algorithm is employed to optimize the hyperparameters of the LSTM and BilLSTM models for enhanced estimation precision. The Gaussian noise is added to the datasets to evaluate the robustness of the estimation models. The results indicate that the estimation models are capable of accurately predicting the vehicle’s sideslip angle. The \(R^{2}\) values of the results are mostly greater than 0.96. The PSO algorithm can improve estimation precision, and the PSO-LSTM model performs the best.
期刊介绍:
The journal Multibody System Dynamics treats theoretical and computational methods in rigid and flexible multibody systems, their application, and the experimental procedures used to validate the theoretical foundations.
The research reported addresses computational and experimental aspects and their application to classical and emerging fields in science and technology. Both development and application aspects of multibody dynamics are relevant, in particular in the fields of control, optimization, real-time simulation, parallel computation, workspace and path planning, reliability, and durability. The journal also publishes articles covering application fields such as vehicle dynamics, aerospace technology, robotics and mechatronics, machine dynamics, crashworthiness, biomechanics, artificial intelligence, and system identification if they involve or contribute to the field of Multibody System Dynamics.