Fuzzy logic control of AC induction motors

Cleland, John G.F.; Turner, Wayne C.; Wang, P.; Espy, T.; Chappell, Philip; Spiegel, R.J.; Bose, B.K.

doi:10.1109/fuzzy.1992.258768

Cited by 23 publications

(5 citation statements)

References 3 publications

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“…However, at the same time, FL products have been aggressively built in Europe and Japan (EET, 1991;Smith, 1993). Still, the United States EPA (Environmental Protection Agency) has investigated fuzzy control for energy-efficient motors (Cleland et al, 1992). NASA (National Aeronautics and Space Administration) has studied fuzzy control for automated space docking (Ortega and Giron-Sierra, 1995).…”

Section: Fuzzy Logicmentioning

confidence: 99%

Development of soft computing and applications in agricultural and biological engineering

Huang

Lan

Thomson

et al. 2010

Computers and Electronics in Agriculture

258

126

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Soft computing is a set of "inexact" computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed.

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Section: Fuzzy Logicmentioning

confidence: 99%

Development of soft computing and applications in agricultural and biological engineering

Huang

Lan

Thomson

et al. 2010

Computers and Electronics in Agriculture

258

126

View full text Add to dashboard Cite

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“…2) Fuzzy Sets Determination: For simplicity, we have chosen triangular and trapezoidal shapes with a zero symmetry and a 50% overlapping rate for fuzzy linguistic sets as proposed by Bose et al [17] and Bassily et al [18]. Fig.…”

Section: ) Scaling Factors Determinationmentioning

confidence: 99%

Fuzzy logic applied to speed control of a stepping motor drive

Bétin

Pinchon

Capolino

2000

IEEE Trans. Ind. Electron.

117

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Nowadays, thanks to the development of microprocessors, stepping motors are widely used in robotics and in the numerical control of machine tools where they have to perform high-precision positioning operations. Nevertheless, the variations of the mechanical configuration of the drive, which are common to these two applications, can lead to a loss of synchronism for high stepping rates. Moreover, the classical open-loop speed control is weak and a closed-loop control becomes necessary. In this paper, the fuzzy logic principle is applied to control the speed of a stepping motor drive with feedback. An advanced test bed is used in order to evaluate the tracking properties and the robustness capacities of the fuzzy logic controller when variations of the mechanical configuration occur. The experiment has been performed using a low-cost 16-b microcontroller in order to verify the design performance.

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“…When the speed command or load torque is changed, the system can easily transition to the fast transient response mode when the rated flux is established and the torque is directly controlled by the speed loop. The above fuzzy control can easily be translated to open-loop volt-per-hertz controlled drive [29] where the flux is controlled by the voltage and the torque is controlled by the feedforward slip signal estimated from the machine terminal voltages and currents.…”

Section: Then the Excitation Increment ( D Z D S )mentioning

confidence: 99%

Expert system, fuzzy logic, and neural network applications in power electronics and motion control

Bose

1994

Proc. IEEE

344

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Artificial intelligence (AI) tools, such as expert system, fuzzy logic, and neural network are expected to usher a new era in power electronics and motion control in the coming decades. Although these technologies have advanced significantly in recent years and have found wide applications, they have hardly touched the power electronics and mackine drives area. The paper describes theseAi tools and their application in the area of power electronics and motion control. The body of the paper is subdivided into three sections which describe, respectively, the principles and applications of expert system, fuzzy logic, and neural network. The theoretical portion of each topic is of direct relevance to the application of power electronics. The example applications in the paper are taken from the published literature. Hopefully, the readers will be able to formulate new applications from these examples.

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Fuzzy logic control of AC induction motors

Cited by 23 publications

References 3 publications

Development of soft computing and applications in agricultural and biological engineering

Development of soft computing and applications in agricultural and biological engineering

Fuzzy logic applied to speed control of a stepping motor drive

Expert system, fuzzy logic, and neural network applications in power electronics and motion control

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