Generalized hybrid control synthesis for affine systems using sequential adaptive networks

Nayak, Rajib; Gomes, James

doi:10.1002/jctb.2268

Cited by 1 publication

(1 citation statement)

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“…A number of strategies are also applied, for example, having membership functions as the ANN nodes, which can be created and modified, [ 238 ] adopting decoupled control loops with fuzzy logic to improve the controller performance, [ 235 ] and approximating unmeasured variables using mechanistic equations to improve convergence. [ 29,204 ]…”

Section: Common Ai‐based Process Control Technologiesmentioning

confidence: 99%

Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering

Dutta

Upreti

2021

Can J Chem Eng

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In the last three decades, artificial intelligence (AI) has been increasingly and vigorously utilized for process control in chemical, biochemical, and biomedical engineering. These disciplines frequently involve fairly sophisticated processes under risks of operational upsets, and thus have an ever increasing demand of superior control strategies. As the deployment of AI in process control pushes the limits in this regard, the research advances, which are multitudinous and varied, need to be assimilated and organized to help promote further utilization and progress in the field. To that end, we examine more than 280 relevant research publications, and systematically collate the information. The AI‐based technologies are classified, and their over‐arching control paradigm is presented. Common AI‐based control technologies are then presented, which are based on expert systems, fuzzy logic, artificial neural networks, nature‐inspired algorithms, and hybrid approaches. Their working principles, types, and implementations are summarized along with advantages, limitations, and comparisons, if available. Important applications in the above disciplines are included with the help of tables that capture important details. A discussion is also provided on advanced and newly emerging AI‐based control technologies with pertinent applications. Overall trends are analyzed, and future prospects are identified based on the survey.

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Section: Common Ai‐based Process Control Technologiesmentioning

confidence: 99%