IET Control Theory and Applications最新文献_第6页

Event-Triggered Fuzzy Predictive Control of Nonlinear Cyber-Physical System Under Stochastic Communication Protocol Scheduling 随机通信协议调度下非线性信息物理系统的事件触发模糊预测控制

IF 2.3 4区计算机科学

IET Control Theory and Applications Pub Date : 2025-04-21 DOI: 10.1049/cth2.70025

Jun Wang, Chenghong Liao, Hongguang Pan

{"title":"Event-Triggered Fuzzy Predictive Control of Nonlinear Cyber-Physical System Under Stochastic Communication Protocol Scheduling","authors":"Jun Wang, Chenghong Liao, Hongguang Pan","doi":"10.1049/cth2.70025","DOIUrl":"10.1049/cth2.70025","url":null,"abstract":"The objective of this paper is to investigate an event-triggered output feedback model predictive control (MPC) approach for the nonlinear cyber-physical system (CPS) with a stochastic communication protocol (SCP) scheduling, which is approximated by an interval type-2 Takagi–Sugeno (IT2 T-S) fuzzy model. For the objective of enhancing network communication efficiency and relieving data collision caused by limited communication resource, an SCP protocol ruled by a Markov stochastic process is favourably utilized to govern the data scheduling of network. Based on an event-triggered output feedback control law, a mode-dependent IT2 fuzzy controller is formally designed, in which the feedback gain is optimized by solving an online constrained MPC optimization problem. By the utilization of defining the mean-square quadratic boundedness (MSQB) for confining the augmented system state into a robust invariant set, both the feasibility of controller and closed-loop stochastic stability are ensured and proved with the satisfaction of physical constraint in the mean-square sense. Finally, we validate the effectiveness of the proposed method by a numerical simulation example.","PeriodicalId":50382,"journal":{"name":"IET Control Theory and Applications","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cth2.70025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fixed-Time Synergetic Control of Multi-Interior Permanent Magnet Synchronous Motor Traction System With Dynamic Adhesion 多内嵌式永磁同步电机牵引系统动态附着的定时协同控制

IF 2.3 4区计算机科学

IET Control Theory and Applications Pub Date : 2025-04-21 DOI: 10.1049/cth2.70030

Deqing Huang, Qiyuan Zhao, Ruiqi Li, Yupei Jian

{"title":"Fixed-Time Synergetic Control of Multi-Interior Permanent Magnet Synchronous Motor Traction System With Dynamic Adhesion","authors":"Deqing Huang, Qiyuan Zhao, Ruiqi Li, Yupei Jian","doi":"10.1049/cth2.70030","DOIUrl":"10.1049/cth2.70030","url":null,"abstract":"The difference in wheel speeds within a train carriage arises from variations in traction motor performance and rail adhesion conditions. This can potentially lead to uneven wheel wear and, subsequently, to imbalanced traction and unstable train operation. To tackle this issue, this paper proposes a control method based on fixed-time synergetic control theory to synchronize the linear speeds of wheels in a multi-interior permanent magnet synchronous motor (IPMSM) traction system. The method considers load differences caused by wear differences between the front and rear wheels, as well as the dynamic adhesion conditions of the rail. First, the model of the permanent magnet synchronous traction system (PMSTS) is established by combining the single-axle train model with the dynamic model of the IPMSM. Then, synergetic control theory is extended with fixed-time theory to ensure the convergence performance of the PMSTS under any adhesion condition. Furthermore, a new synergetic load torque observer is designed to estimate the motor-side load torque, with the observed information used to track maximum adhesion coefficient. Finally, the proposed method is validated for its effectiveness and advantages through a hardware-in-the-loop platform.","PeriodicalId":50382,"journal":{"name":"IET Control Theory and Applications","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cth2.70030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multivariable Control of Wastewater Treatment Process Based on Multi-Agent Deep Reinforcement Learning 基于多智能体深度强化学习的污水处理过程多变量控制

IF 2.3 4区计算机科学

IET Control Theory and Applications Pub Date : 2025-04-20 DOI: 10.1049/cth2.70021

Shengli Du, Rui Sun, Peixi Chen

{"title":"Multivariable Control of Wastewater Treatment Process Based on Multi-Agent Deep Reinforcement Learning","authors":"Shengli Du, Rui Sun, Peixi Chen","doi":"10.1049/cth2.70021","DOIUrl":"10.1049/cth2.70021","url":null,"abstract":"This paper investigates the multivariable control of wastewater treatment processes (WWTP). This paper integrates deep reinforcement learning (DRL) with PID control and proposes a multivariable adaptive PID control strategy based on multi-agent DRL (MADRL) for WWTP. The approach begins with the construction of a MADRL-PID controller structure, consisting of an agent and a PID controller module. The agent adjusts the PID controller values while the PID module calculates the control signal. To enhance the agent's ability to cooperatively tune multiple PID controllers, the algorithm's components–reward function, action space, environment, and state space–are designed according to the BSM1 simulation platform principles and the MADRL framework requirements. Additionally, to handle WWTP's non-linearities, uncertainties, and parameter coupling, the multi-agent deep deterministic policy gradient algorithm is selected as the foundation for training the agents. Experimental results demonstrate that the proposed algorithm exhibits greater adaptability than traditional PID control and achieves superior control performance.","PeriodicalId":50382,"journal":{"name":"IET Control Theory and Applications","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cth2.70021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-Based H ∞ ${H_infty }$ Optimal Tracking Control of Completely Unknown Linear Systems Under Input Constraints 输入约束条件下基于数据的完全未知线性系统的 H ∞ ${H_infty }$ 优化跟踪控制

IF 2.3 4区计算机科学

IET Control Theory and Applications Pub Date : 2025-04-20 DOI: 10.1049/cth2.70022

Peyman Ahmadi, Aref Shahmansoorian, Mehdi Rahmani

{"title":"Data-Based \u0000 \u0000 \u0000 H\u0000 ∞\u0000 \u0000 ${H_infty }$\u0000 Optimal Tracking Control of Completely Unknown Linear Systems Under Input Constraints","authors":"Peyman Ahmadi, Aref Shahmansoorian, Mehdi Rahmani","doi":"10.1049/cth2.70022","DOIUrl":"10.1049/cth2.70022","url":null,"abstract":"This paper presents an <math>\u0000 <semantics>\u0000 <msub>\u0000 <mi>H</mi>\u0000 <mi>∞</mi>\u0000 </msub>\u0000 <annotation>${H_infty }$</annotation>\u0000 </semantics></math> optimal tracking control approach for linear systems with unknown models and input constraints. The proposed method is based on data-based adaptive dynamic programming (ADP) that is computationally tractable and does not require model approximation. This study consists of two new algorithms: a model-based constrained control algorithm and a data-based algorithm for systems with completely unknown models. A lower bound for the <math>\u0000 <semantics>\u0000 <msub>\u0000 <mi>H</mi>\u0000 <mi>∞</mi>\u0000 </msub>\u0000 <annotation>${H_infty }$</annotation>\u0000 </semantics></math> attenuation coefficient is determined to ensure optimality. Additionally, the approach allows for constraints on the amplitude and frequency of the control signal, which are incorporated using the idea of inverse optimal control (IOC). The effectiveness of the proposed method is demonstrated through a simulation example, showcasing its ability to achieve robust tracking performance and satisfy input constraints.","PeriodicalId":50382,"journal":{"name":"IET Control Theory and Applications","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cth2.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Human-on-the-Loop Control in Surface Mount Technology via Deep Reinforcement Learning 基于深度强化学习的表面贴装技术中的人在环控制

IF 2.3 4区计算机科学

IET Control Theory and Applications Pub Date : 2025-04-17 DOI: 10.1049/cth2.70028

Qianqian Zhang, Pengfei Li, Yun-Bo Zhao, Yu Kang

{"title":"Human-on-the-Loop Control in Surface Mount Technology via Deep Reinforcement Learning","authors":"Qianqian Zhang, Pengfei Li, Yun-Bo Zhao, Yu Kang","doi":"10.1049/cth2.70028","DOIUrl":"10.1049/cth2.70028","url":null,"abstract":"Considering the importance of solder paste printing in the production process of surface mounted technology (SMT), as well as the decisive impact of key process parameters on the solder paste printing effect. Traditional methods, whether manual or machine tuning, suffer from significant production capacity losses due to long downtime, and machines cannot adaptively adjust parameters based on human expert knowledge, thereby affecting the qualification rate of solder paste printing and the efficiency of SMT production lines. This paper proposes a human–machine integration optimization method for key printing process parameters. By establishing a printing quality prediction model and a key process parameter strategy model, a closed-loop control system has been formed to achieve machine autonomous parameter tuning with expert knowledge. And this paper has completed the establishment of the strategy model based on deep reinforcement learning methods, enabling the SMT production line to predict and adjust key process parameters in real time based on SPI data. In addition, the optimization method described in this paper retains the final decision-making authority of human operators to ensure emergency correction of prediction bias and decision failure history in the system. The final experimental results of this paper indicate that the proposed optimization method performs well in terms of qualification rate, correction effect, SPI data prediction, etc. These demonstrate the effectiveness and value of the proposed human-on-the-loop optimization method in SMT production lines.","PeriodicalId":50382,"journal":{"name":"IET Control Theory and Applications","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cth2.70028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143846073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual-Layer Model Predictive Control for Multi-Vessels Formation With Predefined-Time and Collision-Free Strategy 采用预定时间和无碰撞策略的多船编队双层模型预测控制

IF 2.3 4区计算机科学

IET Control Theory and Applications Pub Date : 2025-04-16 DOI: 10.1049/cth2.70029

Han Xue, Kaibiao Sun

引用次数: 0

Risk-Aware Control: Integrating Worst-Case Conditional Value-At-Risk With Control Barrier Function 风险感知控制：最坏情况条件风险值与控制障碍函数的集成

IF 2.3 4区计算机科学

IET Control Theory and Applications Pub Date : 2025-04-15 DOI: 10.1049/cth2.70024

Masako Kishida

引用次数: 0

Optimal Control-Based Dominance Regions for Boundary-Guarding Games with Rotationally-Constrained Autonomous Vehicles 基于最优控制的旋转约束自动驾驶车辆边界保护博弈优势区域

IF 2.3 4区计算机科学

IET Control Theory and Applications Pub Date : 2025-04-15 DOI: 10.1049/cth2.70023

Xinxin Guo, Yucheng Zhang, Guixi Ke, Weisheng Yan, Rongxin Cui

引用次数: 0

Distributed NE Seeking for Networked Euler-Lagrange Systems With Constrained Communication 具有约束通信的网络欧拉-拉格朗日系统的分布式网元搜索

IF 2.3 4区计算机科学

IET Control Theory and Applications Pub Date : 2025-04-14 DOI: 10.1049/cth2.70020

Yuanyuan Xu, Xin Cai, Xingzhi Chen, Bingpeng Gao, Xinyuan Nan

引用次数: 0

Elevator Speed Tracking Using Event-Triggered Model Predictive Control with Learning-Based Initialisation 基于学习初始化的事件触发模型预测控制的电梯速度跟踪

IF 2.3 4区计算机科学

IET Control Theory and Applications Pub Date : 2025-04-03 DOI: 10.1049/cth2.70017

Bo Chen, Huaqing Liu, Zheming Wang, Ke Li, Yin Shen, Liang Wang

引用次数: 0