{"title":"Deep reinforcement learning optimizer based novel Caputo fractional order sliding mode data driven controller","authors":"Amir Veisi , Hadi Delavari","doi":"10.1016/j.engappai.2024.109725","DOIUrl":null,"url":null,"abstract":"<div><div>The design of controllers in engineering applications typically requires a model that accurately captures the dynamics of the real system. However, finding a precise model for controller design can be challenging in real engineering applications. Consequently, data-driven methods have gained widespread use in engineering systems. This paper presents a novel robust data-driven fractional-order controller optimized through deep reinforcement learning. Additionally, a new robust fractional-order observer has been introduced to improve both the robustness and speed of the system. To establish the stability of the proposed control system, a new Lyapunov stability theorem based on the Caputo fractional-order definition is provided. The proposed controller offers significant advantages, including enhanced robustness against external disturbances, increased resilience to parameter uncertainties and unmodeled nonlinear dynamics, improved accuracy, greater speed, and guaranteed optimal control coefficients. Furthermore, assured adaptability is demonstrated due to the optimization provided by deep reinforcement learning including enhanced robustness against external disturbances, uncertainties of parameters, and unmodeled nonlinear dynamics; improved accuracy; greater speed; and guaranteed optimal control coefficients. Furthermore, assured adaptability is demonstrated due to the optimization provided by deep reinforcement learning. The performance of the proposed method has been compared with that of conventional integer-order sliding mode control, highlighting the superiority of this approach. The proposed method has been evaluated under normal conditions, external disturbances, and system uncertainties. Notably, performance improvements of 15%, 30%, and 68% have been achieved under normal conditions, external disturbances, and internal uncertainties, respectively, compared to the conventional integer-order sliding mode controller.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109725"},"PeriodicalIF":7.5000,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Applications of Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0952197624018839","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
The design of controllers in engineering applications typically requires a model that accurately captures the dynamics of the real system. However, finding a precise model for controller design can be challenging in real engineering applications. Consequently, data-driven methods have gained widespread use in engineering systems. This paper presents a novel robust data-driven fractional-order controller optimized through deep reinforcement learning. Additionally, a new robust fractional-order observer has been introduced to improve both the robustness and speed of the system. To establish the stability of the proposed control system, a new Lyapunov stability theorem based on the Caputo fractional-order definition is provided. The proposed controller offers significant advantages, including enhanced robustness against external disturbances, increased resilience to parameter uncertainties and unmodeled nonlinear dynamics, improved accuracy, greater speed, and guaranteed optimal control coefficients. Furthermore, assured adaptability is demonstrated due to the optimization provided by deep reinforcement learning including enhanced robustness against external disturbances, uncertainties of parameters, and unmodeled nonlinear dynamics; improved accuracy; greater speed; and guaranteed optimal control coefficients. Furthermore, assured adaptability is demonstrated due to the optimization provided by deep reinforcement learning. The performance of the proposed method has been compared with that of conventional integer-order sliding mode control, highlighting the superiority of this approach. The proposed method has been evaluated under normal conditions, external disturbances, and system uncertainties. Notably, performance improvements of 15%, 30%, and 68% have been achieved under normal conditions, external disturbances, and internal uncertainties, respectively, compared to the conventional integer-order sliding mode controller.
期刊介绍:
Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.