Reinforcement learning approach to autonomous PID tuning

Dogru, Oguzhan; Kirubakaran, V.; Ibrahim, Fadi; Wu, Yuqi; Sundaramoorthy, Arun Senthil; Huang, Biao; Xu, Shujie; Nixon, Mark; Bell, Noel

doi:10.1016/j.compchemeng.2022.107760

Cited by 57 publications

(16 citation statements)

References 49 publications

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“…Reinforcement learning is a general learning framework that can be used to address general AI issues because intelligences interact with their environments in a manner similar to how humans do. Because of this, machine learning-based reinforcement learning is also referred to as a broad AI strategy [ 33 ].…”

Section: Models and Evaluation Methodsmentioning

confidence: 99%

Design and Application of Artificial Intelligence Technology-Driven Education and Teaching System in Universities

Zhang

2022

Computational and Mathematical Methods in Medicine

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In recent years, many colleges and universities have been experimenting and exploring the evaluation of education and teaching system and have achieved certain results. In order to understand the quality of education and teaching system in colleges and universities, to improve the school conditions, and to promote the reform of teaching management, methods and means of evaluating the quality of education and teaching system in general higher education institutions are needed. Modern university education and teaching system should realize the combination of classroom teaching and practice teaching, and education and teaching system adopts the mode of the combination of on-campus practice and off-campus practice, so the design of teaching system is the key to the quality of teaching. Aiming at the current problem that talents cultivated by colleges and universities can hardly meet social demands in terms of engineering practice ability, innovation ability, and international competitiveness, this paper proposes the evaluation and adjustment of college education and teaching system driven by algorithms based on artificial intelligence (AI). By designing the teaching system of talent cultivation, and then establishing a quantitative and controllable quality assurance system for practical teaching, a new mechanism for the design of university education system is further explored. Specifically, the framework of the instructional system is built with the aid of an actor-critic algorithm in reinforcement learning, which assists in the design of the university education system, allowing students to truly understand, master and flex their knowledge, and strengthening the correct understanding of the students’ internal learning mechanisms. The practical teaching effect shows that the AI-driven instructional designs are more popular with contemporary students and have higher evaluation scores. The numerical experiment results also show the stability of the instructional design, overcoming the drawbacks of traditional manual subjectivity in the design. AI-driven college education and teaching system is conducive to cultivating students’ solid technical theoretical foundation. Therefore, through the AI-driven teaching system to strengthen the training of practical ability, so as to comprehensively improve students' comprehensive quality and innovation ability.

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Section: Models and Evaluation Methodsmentioning

confidence: 99%

Design and Application of Artificial Intelligence Technology-Driven Education and Teaching System in Universities

Zhang

2022

Computational and Mathematical Methods in Medicine

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“…linear quadratic regulator [32,20], and other methods. RL has also been applied to PID tuning [16,31], with a notable use in [27], where a deep RL-based PID tuning method is proposed and experimented on the physical two-tank system without prior pre-training. For the risks that might arise during the RL based control process, recently, safe reinforcement learning has emerged as a new research focus, see e.g., [17,37,48].…”

Section: The Broader Context Of Learning-based and Data-driven Controlmentioning

confidence: 99%

Neural Operators for Delay-Compensating Control Of Hyperbolic Pides

Qi,

Zhang,

Krstic

2023

Preprint

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“…Pan et al 300 present a reinforcement learning control approach that can handle nonlinear stochastic optimal control problems and has the potential of meeting state constraints. Some recent advances in reinforcement learning have to do with boosting the performance of such algorithms as discussed in Zhu et al 301 and with the leverage of reinforcement learning for the tuning of PID controllers as shown in Dogrua et al 302 In Schwung et al, 303 the reinforcement learning task is speed-up by deploying programmable logic controller information. Moreover, recent reinforcement control strategies aimed to batch control can be found elsewhere (Ma et al, 304 Kim et al, 305 Yoo et al, 306 Joshi et al, 307 and Mowbray et al 308 ).…”

Section: Reinforcement Learning Algorithmsmentioning

confidence: 99%

Machine Learning Algorithms Used in PSE Environments: A Didactic Approach and Critical Perspective

Fuentes-Cortés

Flores‐Tlacuahuac

Nigam

2022

Ind. Eng. Chem. Res.

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This work addresses recent developments for solving problems in process systems engineering based on machine learning algorithms. A general description of most popular supervised and unsupervised learning algorithms is presented, as well as the applications addressed in the current literature. Because of their wide usage and potential applications, support vector machines and neural networks are addressed as special cases. The approach used is fundamentally didactic. Therefore, several of the references included are recommendations for novice readers interested in entering the area of machine learning and data science. The applications were selected considering simplicity, popularity of the application, and accessibility for inexperienced readers, but with knowledge of the process systems engineering area. Finally, a critical perspective for future development and applications is provided. Epistemological issues and modeling limitations are discussed in order to analyze the real significance of data-driven strategies as well as a questioning of academic marketing in recent years.

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Reinforcement learning approach to autonomous PID tuning

Cited by 57 publications

References 49 publications

Design and Application of Artificial Intelligence Technology-Driven Education and Teaching System in Universities

Design and Application of Artificial Intelligence Technology-Driven Education and Teaching System in Universities

Neural Operators for Delay-Compensating Control Of Hyperbolic Pides

Machine Learning Algorithms Used in PSE Environments: A Didactic Approach and Critical Perspective

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