2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)最新文献_第2页

Adaptive Optimal Control for Discrete-Time Linear Systems via Hybrid Iteration 基于混合迭代的离散线性系统自适应最优控制

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10167108

Omar Qasem, Weinan Gao, Hector M. Gutierrez

{"title":"Adaptive Optimal Control for Discrete-Time Linear Systems via Hybrid Iteration","authors":"Omar Qasem, Weinan Gao, Hector M. Gutierrez","doi":"10.1109/DDCLS58216.2023.10167108","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10167108","url":null,"abstract":"In this paper, a novel dynamic programming (DP) and adaptive dynamic programming (ADP) algorithms are proposed, namely hybrid iteration (HI), for discrete-time linear systems. The proposed HI approach fill up the performance gap of two well- known DP algorithms, i.e., policy iteration (PI) and value iteration (VI). In particular, HI drops the need of the prior knowledge of an initial stabilizing control policy required in PI, and at the same time it maintains a fast quadratic convergence rate compared with VI. A data-driven adaptive optimal controller design is also proposed based on the proposed HI algorithm. Simulation results for randomly generated discrete-time linear systems with different system orders demonstrate that the proposed HI approach can significantly save CPU time and reduce the number of learning iterations to converge to the optimal solution comparing with the VI approach. The data-driven HI method is implemented to an application of turbocharged diesel engine with exhaust gas recirculation, and the simulation results illustrate the efficacy of the proposed HI method.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122761865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fault-tolerant Tracking Control for Mobile Robots Based on the Framework of Intermediate Estimator and MPC 基于中间估计器和MPC框架的移动机器人容错跟踪控制

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10166441

Liang-Huan Ying, Jun-Wei Zhu

引用次数: 0

In-distribution stability analysis for neural Markovian jump systems: A delay-feedback control method 神经马尔可夫跳变系统的分布稳定性分析:一种延迟反馈控制方法

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10167380

Xiaohang Li

引用次数: 0

Fault detection for rolling bearings by multi-sensor information fusion method with adaptive weights 基于自适应权值的多传感器信息融合滚动轴承故障检测方法

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10166660

Hao Wu, Yinghao Zhao, Xu Yang, Jian Huang, Jiarui Cui

引用次数: 0

Data-Based Approximate Optimal Control for Unknown Nonaffine Systems via Dynamic Feedback 基于数据的未知非仿射系统动态反馈近似最优控制

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10166780

Jin-Jye Lin, Bo Zhao, Derong Liu

引用次数: 0

Load Tooth Contact Analysis of Composite Cycloidal Planetary Transmission with Small Tooth Difference 小齿差复合摆线行星传动载荷齿接触分析

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10166206

S. Zhou, Zhenhua Han, Yunchen Zhou, Yunda Zhao, Hao Wang, Youwu Du

{"title":"Load Tooth Contact Analysis of Composite Cycloidal Planetary Transmission with Small Tooth Difference","authors":"S. Zhou, Zhenhua Han, Yunchen Zhou, Yunda Zhao, Hao Wang, Youwu Du","doi":"10.1109/DDCLS58216.2023.10166206","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166206","url":null,"abstract":"Based on the profile equation of composite cycloidal tooth, the coordinate system of the contact analysis model of composite cycloidal gear teeth is established. Depending on the conjugate contact conditions of gears, the vector equation of tooth contact analysis (TCA) is created. The backlash and transmission error of the composite cycloidal planetary gear pair with small tooth difference (Abbreviated as composite cycloidal gear pair) after modification are calculated by solving the equation. The loaded tooth contact analysis (LTCA) model of the composite cycloid is built using Hertzian contact theory, a moment balancing equation, and deformation compatibility condition. The composite cycloidal transmission error and contact force in the load tooth contact analysis mode are solved. The distribution of contact force under different crank shaft angles and the sensitivity of bearing transmission error and contact force to tooth profile adjustment coefficient are analyzed with examples. The results show that the transmission accuracy of composite cycloid gears under load conditions is within 1 arcmin, which meets the requirements for precision transmission performance.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128423083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Group Consensus for Second-order Linear Multi-agent Systems with Intermittent Control 二阶线性多智能体间歇控制系统的群一致性

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10166220

Zhaoqin Wu, Weixun Li, Xiangyang Du, Jing Xiao, Limin Zhang

引用次数: 0

Differential Dynamic Programming for Finite-Horizon Multi-Player Non-Zero-Sum Differential Games of Nonlinear Systems 非线性系统有限视界多参与者非零和微分对策的微分动态规划

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10166320

Yuqi Zhang, Bin Zhang

引用次数: 0

Research on Privacy Protection of Internet of Vehicles Based on Elliptic Curve Ring Signature 基于椭圆曲线环签名的车联网隐私保护研究

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10166665

Weiqi Mao, Bo Mi, Darong Huang, Haoyu Ma

引用次数: 0

Reinforcement Learning Driving Strategy based on Auxiliary Task for Multi-Scenarios Autonomous Driving 基于辅助任务的多场景自动驾驶强化学习策略

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10166271

Jingbo Sun, Xing Fang, Qichao Zhang

{"title":"Reinforcement Learning Driving Strategy based on Auxiliary Task for Multi-Scenarios Autonomous Driving","authors":"Jingbo Sun, Xing Fang, Qichao Zhang","doi":"10.1109/DDCLS58216.2023.10166271","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166271","url":null,"abstract":"Reinforcement learning (RL) has made great progress in autonomous driving applications. However, using one RL based driving policy for multi-scenarios autonomous driving is still challenging for RL in autonomous driving. There are different observations and reward measurements in different scenarios. At the same time, there is also the problem of multi-source heterogeneous observation in autonomous driving. To address the problems above, we propose a reinforcement learning framework based on the auxiliary task. Firstly, we designed a reward function to enable vehicles to learn safe and efficient strategies. Further, an auxiliary task is designed to learn the characteristics of different scenarios so that the ego agent can adopt different strategies for different scenarios. Finally, in order to handle the driving problem in multiple scenarios, we propose a representation network based on Multi-layer perceptron (MLP), Convolutional neural network (CNN), and Transformer networks to learn multi-source heterogeneous observation. The multi-source heterogeneous observation consists of the ego vehicle state, the bird's eye view (BEV) state and neighbour vehicle states. Experiments show that our method achieves a higher success rate compared to a popular reinforcement learning algorithm.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130233484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1