Fuzzy-Based Adaptive Optimization of Unknown Discrete-Time Nonlinear Markov Jump Systems With Off-Policy Reinforcement Learning

Fang, Haiyang; Tu, Yidong; Wang, Hai; He, Shuping; Liu, Fei; Ding, Zhengtao; Cheng, Shing Shin

doi:10.1109/tfuzz.2022.3171844

Cited by 23 publications

(4 citation statements)

References 32 publications

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“…Machine Learning Applications in Predictive Maintenance Implementing machine learning in predictive maintenance has significantly transformed operational strategies across various industries, including manufacturing and aviation. These advancements are pivotal in enhancing efficiency and minimizing unforeseen operational interruptions [15]. A notable application is found in predictive manufacturing systems within the framework of Industry 4.0.…”

Section: 3mentioning

confidence: 99%

Intelligent Electric Grid Maintenance via an Adaptive Predictive Maintenance Optimization Algorithm

Kiran Thatikonda

2024

jes

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This study presents a novel framework for the predictive maintenance of electric grid infrastructure, leveraging an Adaptive Predictive Maintenance Optimization (APMO) algorithm. Our comprehensive system architecture integrates IoT sensors and drone surveillance, harnessing grid computing for data processing and machine learning for analytics. The APMO algorithm, underpinned by reinforcement learning, dynamically refines maintenance schedules, enhancing operational efficiency. Results from simulated sensor data exhibit a negligible correlation, indicating a multi-faceted approach to predictive analytics. Specifically, the Health Index distribution showed a wide range of 0.7 to 1.0, while the electrical current maintained a stable mean at 10 A. Further, the APMO algorithm demonstrated a promising improvement in maintenance efficiency from 40% to 75% over a year. This research introduces a scalable, robust system for grid management, paving the way for smarter infrastructure maintenance.

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Section: 3mentioning

confidence: 99%

Intelligent Electric Grid Maintenance via an Adaptive Predictive Maintenance Optimization Algorithm

Kiran Thatikonda

2024

jes

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“…Expanded the work of [31], a decentralized fuzzy tracking learning control method was designed in [32] for interconnected systems. In [33], Fang et al developed an off-policy learning control method for discrete-time Markov jump fuzzy systems. To our knowledge, there are little attention that focus on designing a learning controller for fuzzy MJSPSs, which stimulates our work.…”

Section: B Related Workmentioning

confidence: 99%

A Fuzzy-Model-Based Approach to Optimal Control for Nonlinear Markov Jump Singularly Perturbed Systems: A Novel Integral Reinforcement Learning Scheme

Shen

Wang

et al. 2023

IEEE Trans. Fuzzy Syst.

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A fuzzy-model-based approach is developed to investigate the reinforcement learning-based optimization for nonlinear Markov jump singularly perturbed systems. As the first attempt, an offline parallel iteration learning algorithm is presented to solve the coupled algebraic Riccati equations with singular perturbation and jumping parameters. Furthermore, based on the integral reinforcement learning approach, a novel online parallel learning algorithm is proposed by employing the slow and fast sampled data simultaneously, where the impacts of stochastic jumping and ill-conditioned numerical problems are avoided. Meanwhile, the convergence of the proposed learning algorithms is proved. Finally, we present a tunnel diode circuit model to demonstrate the efficacy of the proposed methods.

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“…Because of the nonlinear spring and damping characteristics of the suspension system, model uncertainty, time-varying system parameters, and high parameter uncertainty caused by pavement interference and other factors, it is crucial to design a controller to ensure robust performance VOLUME 11, 2023 and system stability [3]. Compared to other control schemes, neural networks [4]- [6], fuzzy logic algorithms [7], [8], and sliding mode control [9], [10] have emerged as the preferred methods for dealing with the problems with parameter uncertainty and nonlinear active suspension systems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Estimation and Control of Nonlinear Suspension Systems With Natural Logarithm Sliding Mode Control

Wijaya,

Yakub,

Abdullah

et al. 2024

IEEE Access

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This article presents an adaptive logarithm-based sliding mode control (ALSMC) for an active suspension system to enhance ride comfort while maintaining the road holding and suspension space limits. The main objective of the control design is to devise a controller that does not require precise and accurate model parameters while ensuring resilience to model uncertainty and external disturbance. To achieve this objective, a natural logarithm sliding mode control (lnSMC) approach is adopted. Unlike the conventional sliding mode control that utilizes a linear sliding surface, the lnSMC is designed based on a natural logarithmic function, which creates the boundary layer of the controlled states. Thus, the selection of the controller parameter will be more practical as its value is dependent on the desired amplitude of the controlled state. This prevents trial and error when determining the controller parameter. In this study, the system's dynamics are represented by a nonlinear quarter-car suspension model (e.g., nonlinear springs and piecewise dampers) with unknown model parameters. An adaptive law is incorporated to estimate all unknown model parameters, which is subsequently used for updating the control law. A simulation study is carried out to illustrate the effectiveness of the proposed control law under various types of disturbances, including sinusoidal road profile, bump road profile, and random road profile disturbance.

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Fuzzy-Based Adaptive Optimization of Unknown Discrete-Time Nonlinear Markov Jump Systems With Off-Policy Reinforcement Learning

Cited by 23 publications

References 32 publications

Intelligent Electric Grid Maintenance via an Adaptive Predictive Maintenance Optimization Algorithm

Intelligent Electric Grid Maintenance via an Adaptive Predictive Maintenance Optimization Algorithm

A Fuzzy-Model-Based Approach to Optimal Control for Nonlinear Markov Jump Singularly Perturbed Systems: A Novel Integral Reinforcement Learning Scheme

Adaptive Estimation and Control of Nonlinear Suspension Systems With Natural Logarithm Sliding Mode Control

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