Quantum neural network-based intelligent controller design for CSTR using modified particle swarm optimization algorithm

Salahshour, Esmaeil; Malekzadeh, Milad; Gordillo, Francisco; Ghasemi, Jahan B.

doi:10.1177/0142331218764566

Cited by 24 publications

(11 citation statements)

References 39 publications

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“…By relying on the uncertainty and polymorphism of quantum, the particle can obtain any position with different probabilities, and guide the particle to explore the global optimal position more efficiently. Good results have been achieved in parameter optimization (30)(31).…”

Section: Qpso Algorithm and Its Parameter Selection Processmentioning

confidence: 94%

Incipient Fault Diagnosis Method for IGBT Drive Circuit Based on Improved SAE

Shi

et al. 2019

IEEE Access

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An incipient fault diagnosis method devised for insulated gate bipolar transistor (IGBT) drive circuit based on improved stack auto-encoder (SAE) is recommended. First, the Monte Carlo method is applied to extracting the time domain response signal of the circuit under test as sample data. Then, with SAE used to extract essential features of data, the SAE is employed to extract features of sample data. Meanwhile, multi-classification relevant vector machine (RVM) is involved for fault diagnosis of the acquired features. As the structure of the hidden layer in SAE and the learning rate could exert a significant effect on the feature extraction performance, in this paper, the quantum particle swarm optimization (QPSO) algorithm is used to optimize the above parameters. As revealed by the experimental results, the improved SAE method is effective in the extraction of the essential characteristics of the incipient faults for the IGBT drive circuit. Further with this, the incipient fault multi-classification RVM of the IGBT drive circuit is capable of achieving 100% diagnostic accuracy. INDEX TERMS IGBT drive circuit, incipient diagnosis, deep learning, stacked auto-encoder, multiclassification relevant vector machine.

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Section: Qpso Algorithm and Its Parameter Selection Processmentioning

confidence: 94%

Incipient Fault Diagnosis Method for IGBT Drive Circuit Based on Improved SAE

Shi

et al. 2019

IEEE Access

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“…An initial study trained them using PSO and reported superior performance in PID control scheme. [ 259 ]…”

Section: Miscellaneous 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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“…In which x = sin ( t∕10) . In order to have a quantitative comparison, consider the criterion Fitness = ∫ 10 0 � ∑ i e 2 i � dt [43][44][45][46][47][48][49][50][51][52]. All the controller parameters are the same as those presented in simulation 1.…”

Section: Simulation 2: Comparison Between Different Uncertainty Estimmentioning

confidence: 99%

Design of an adaptive dynamic sliding mode control approach for robotic systems via uncertainty estimators with exponential convergence rate

2020

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In this work, an adaptive dynamic sliding mode control approach is proposed for robotic systems via uncertainty estimators with exponential convergence rate. The uncertainties are estimated using various uncertainty estimators such as the Fourier series expansion, Legendre polynomials and adaptive fuzzy systems. Also, for each uncertainty estimator, the approximation error is compensated. The adaptation laws are derived using a stability analysis. Moreover, the asymptotic convergence of the tracking error and the boundedness of all closed-loop signals are guaranteed. The novelty of this paper is proposing a positive exponential function for the convergence rate of the adaptation rules to prevent from initial high voltages originated from large initial tracking errors. Another novelty of this paper is presenting a robust control term for the truncation error that improves the accuracy of the control system. Analysis of simulations reveals the effectiveness of the proposed method in terms of fast disturbance rejection and negligible tracking error.

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Quantum neural network-based intelligent controller design for CSTR using modified particle swarm optimization algorithm

Cited by 24 publications

References 39 publications

Incipient Fault Diagnosis Method for IGBT Drive Circuit Based on Improved SAE

Incipient Fault Diagnosis Method for IGBT Drive Circuit Based on Improved SAE

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

Design of an adaptive dynamic sliding mode control approach for robotic systems via uncertainty estimators with exponential convergence rate

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