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

IF 4.9 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Qing Yang;Jing Wang;Hao Shen;Ju H. Park
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引用次数: 0

摘要

本文通过一种新的基于尺度的强化学习方案,解决了具有未知动态的马尔可夫跳跃系统(MJSs)的优化问题。首先,通过子系统变换,将mjs的最优控制器设计问题转化为求解一组并行解耦的代数Riccati方程(dare)。求解这些方程的传统学习方案要么需要初始允许的控制策略,要么收敛缓慢。为了克服这些限制,提出了一种新的基于尺度的强化学习算法。该算法具有几个显著的优点:在学习过程中不需要系统动力学,收敛速度更快,并且放宽了对初始可接受控制策略的要求。通过数学归纳法严格证明了该方案的有效性。最后,通过一个运放电路实例验证了所提方案的可行性,并通过一系列对比仿真验证了其优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
IEEE Transactions on Circuits and Systems II: Express Briefs
IEEE Transactions on Circuits and Systems II: Express Briefs 工程技术-工程:电子与电气
CiteScore
7.90
自引率
20.50%
发文量
883
审稿时长
3.0 months
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.
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