A Neural Network Parallel Adaptive Controller for Dynamic System Control

Kamalasadan, Sukumar; Ghandakly, A.A.

doi:10.1109/tim.2007.895674

Cited by 48 publications

(19 citation statements)

References 15 publications

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“…On other hand, much research effort has been put into the design of artificial neural network and fuzzy logic-based controllers as they reduce the complexity and allow a faster computation of the command [20][21][22][23][24][25][26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

A Fast Adaptive Artificial Neural Network Controller for Flexible Link Manipulators

Maouche¹,

Meddahi²

2016

ijacsa

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Section: Introductionmentioning

confidence: 99%

A Fast Adaptive Artificial Neural Network Controller for Flexible Link Manipulators

Maouche¹,

Meddahi²

2016

ijacsa

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“…A constrained predictive control algorithm based on feedback linearization employed to a coupled tank apparatus has described in [4]. Intelligent controls including fuzzy logic (FL) [5][6], neural network (NN) control [7][8], and genetic algorithms (GA) [9] have also been applied to the coupled tanks system. Zumberge and Passino [10] have reported the comparison between conventional control and intelligent control applied to the process control.…”

Section: Introductionmentioning

confidence: 99%

A novel fuzzy based adaptive control of the four tank system

Prusty

Pati

Mahapatra

2015

Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT)

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The paper has analyzed the four tank system (FTS) from the mechanism modeling and has also established the nonlinear and linear mathematical model. The FTS is a typical control system with nonlinear, coupling and time delay characteristics which can be used to test the applications of different control algorithms on complex systems. The aim of the process is to keep the liquid level in the tanks at the desired values. Here, Fuzzy Modified Model Reference Adaptive Control (FMMRAC) is proposed and is applied to the tank system to test its effectiveness. The response of the FMMRAC controller is verified and is compared with other control algorithms through simulation. The performance of the closed loop system is simulated using LabVIEW software. The method used here is validated using the simulated results.

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“…Over the years, fuzzy control has evolved as an extremely popular and viable control alternative for the purpose of modeling and control in a variety of applications, ranging from robotics and mechanical systems, to electrical drives, in process control of highly non-linear chemical processes and in other fields of engineering [30][31][32][33][34]. Similarly stochastic optimization techniques, specially biologically-inspired optimization algorithms in particular, have also been employed successfully to solve the adaptive control problems and other related problems such as robotic navigation problems, communication resource allocation problems, power systems control, power electronics and drives related problems [35][36][37][38], etc.…”

Section: Introductionmentioning

confidence: 99%