Control of a non-isothermal continuous stirred tank reactor by a feedback–feedforward structure using type-2 fuzzy logic controllers

Galluzzo, M.; Cosenza, Biagio

doi:10.1016/j.ins.2011.03.023

Cited by 25 publications

(9 citation statements)

References 45 publications

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“…Fuzzy logic‐based systems have been used for feedback/feedforward/mixed control [ 70 ] as well as in different control schemes such as NMPC, [ 71,72 ] IMC, [ 48,73 ] PI control, [ 74 ] and PID control. [ 43,75 ] To implement a fuzzy system in a PID control scheme, the parameters of a tuned PID controller (gain and time constants) are used in the relation between the controller parameters and the undetermined gains of the fuzzy controller (which would replace the PID controller).…”

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%

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

Dutta

Upreti

2021

Can J Chem Eng

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“…In systems with uncertainties of the system model, the choice of type-1 fuzzy may not always be the appropriate solution for a control problem [36]. In these cases, the type-2 fuzzy logic control has been represented in many fields of chemical processes [37,38].…”

Section: Application Of Neural Network In Chemical Process Controlmentioning

confidence: 99%

Application of AI in Chemical Engineering

Hajjar¹,

Tayyebi²,

EghbalAhmadi³

2018

Artificial Intelligence - Emerging Trends and Applications

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A major shortcoming of traditional strategies is the fact that solving chemical engineering problems due to the highly nonlinear behavior of chemical processes is often impossible or very difficult. Today, artificial intelligence (AI) techniques are becoming useful due to simple implementation, easy designing, generality, robustness and flexibility. The AI includes various branches, namely, artificial neural network, fuzzy logic, genetic algorithm, expert systems and hybrid systems. They have been widely used in various applications of the chemical engineering field including modeling, process control, classification, fault detection and diagnosis. In this chapter, the capabilities of AI are investigated in various chemical engineering fields.

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“…In recent years, several works have been developed based on type-2 fuzzy systems. They are used inimage processing [13][14][15], the control of electrical machines [16][17][18][19], and the control of mobile robots [20][21][22][23]. But the disadvantage of fuzzy logic is the empirical choice of parameters, which can make the control of the system long and delicate in certain situations.…”

Section: Introductionmentioning

confidence: 99%

Type-2 fuzzy logic controller optimized by wavelet networks for mobile robot navigation

Affane

Omari

2020

IJEECS

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<p>In this work, we will use a new control strategy based on the integration of a type-2 fuzzy reasoning optimized by wavelet networks as part of a navigation system of a mobile robot. The proposed approach is able to facilitate the navigation task in an autonomous manner, in order to determine which commands must be sent at each moment to the mobile robot. This operation must take into account convergence towards a goal with the shortest possible path in the minimum delay between the starting position and the target position. Once the goal is reached, the robot stops. </p><p> </p>

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Control of a non-isothermal continuous stirred tank reactor by a feedback–feedforward structure using type-2 fuzzy logic controllers

Cited by 25 publications

References 45 publications

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

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

Application of AI in Chemical Engineering

Type-2 fuzzy logic controller optimized by wavelet networks for mobile robot navigation

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