{"title":"基于学习的马尔可夫跳变系统标度方案及其在运放电路中的应用","authors":"Qing Yang;Jing Wang;Hao Shen;Ju H. Park","doi":"10.1109/TCSII.2025.3590998","DOIUrl":null,"url":null,"abstract":"This brief addresses the optimization problem for Markov jump systems (MJSs) with unknown dynamics via a novel scaling-based reinforcement learning scheme. First, by employing subsystem transformation, the optimal controller design problem for MJSs is reformulated into solving a set of parallel and decoupled algebraic Riccati equations (DAREs). Traditional learning schemes for solving these equations either require initially admissible control policies or suffer from slow convergence. To overcome these limitations, a novel scaling-based reinforcement learning algorithm is proposed. Several notable advantages are exhibited by the proposed algorithm: it eliminates the need for system dynamics during the learning process, achieves faster convergence, and relaxes the requirement for an initially admissible control policy. The effectiveness of the proposed scheme is rigorously proven through a mathematical induction method. Finally, the feasibility of the proposed scheme is verified using an operational amplifier circuit example, and its superiority is demonstrated through a series of comparative simulations.","PeriodicalId":13101,"journal":{"name":"IEEE Transactions on Circuits and Systems II: Express Briefs","volume":"72 9","pages":"1238-1242"},"PeriodicalIF":4.9000,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Learning-Based Scaling Scheme for Markov Jump Systems and Its Application in Operational Amplifier Circuit\",\"authors\":\"Qing Yang;Jing Wang;Hao Shen;Ju H. Park\",\"doi\":\"10.1109/TCSII.2025.3590998\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This brief addresses the optimization problem for Markov jump systems (MJSs) with unknown dynamics via a novel scaling-based reinforcement learning scheme. First, by employing subsystem transformation, the optimal controller design problem for MJSs is reformulated into solving a set of parallel and decoupled algebraic Riccati equations (DAREs). Traditional learning schemes for solving these equations either require initially admissible control policies or suffer from slow convergence. To overcome these limitations, a novel scaling-based reinforcement learning algorithm is proposed. Several notable advantages are exhibited by the proposed algorithm: it eliminates the need for system dynamics during the learning process, achieves faster convergence, and relaxes the requirement for an initially admissible control policy. The effectiveness of the proposed scheme is rigorously proven through a mathematical induction method. Finally, the feasibility of the proposed scheme is verified using an operational amplifier circuit example, and its superiority is demonstrated through a series of comparative simulations.\",\"PeriodicalId\":13101,\"journal\":{\"name\":\"IEEE Transactions on Circuits and Systems II: Express Briefs\",\"volume\":\"72 9\",\"pages\":\"1238-1242\"},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2025-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Circuits and Systems II: Express Briefs\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11087414/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Circuits and Systems II: Express Briefs","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11087414/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Learning-Based Scaling Scheme for Markov Jump Systems and Its Application in Operational Amplifier Circuit
This brief addresses the optimization problem for Markov jump systems (MJSs) with unknown dynamics via a novel scaling-based reinforcement learning scheme. First, by employing subsystem transformation, the optimal controller design problem for MJSs is reformulated into solving a set of parallel and decoupled algebraic Riccati equations (DAREs). Traditional learning schemes for solving these equations either require initially admissible control policies or suffer from slow convergence. To overcome these limitations, a novel scaling-based reinforcement learning algorithm is proposed. Several notable advantages are exhibited by the proposed algorithm: it eliminates the need for system dynamics during the learning process, achieves faster convergence, and relaxes the requirement for an initially admissible control policy. The effectiveness of the proposed scheme is rigorously proven through a mathematical induction method. Finally, the feasibility of the proposed scheme is verified using an operational amplifier circuit example, and its superiority is demonstrated through a series of comparative simulations.
期刊介绍:
TCAS II publishes brief papers in the field specified by the theory, analysis, design, and practical implementations of circuits, and the application of circuit techniques to systems and to signal processing. Included is the whole spectrum from basic scientific theory to industrial applications. The field of interest covered includes:
Circuits: Analog, Digital and Mixed Signal Circuits and Systems
Nonlinear Circuits and Systems, Integrated Sensors, MEMS and Systems on Chip, Nanoscale Circuits and Systems, Optoelectronic
Circuits and Systems, Power Electronics and Systems
Software for Analog-and-Logic Circuits and Systems
Control aspects of Circuits and Systems.