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Applied Intelligent Control of Induction Motor Drives by Tze?Fun Chan, Keli Shi(auth.)

By Tze?Fun Chan, Keli Shi(auth.)

Induction automobiles are crucial workhorses in undefined. they're commonly used as constant-speed drives whilst fed from a voltage resource of mounted frequency. creation of complicated energy digital converters and strong electronic sign processors, although, has made attainable the improvement of excessive functionality, adjustable velocity AC motor drives.

This booklet goals to discover new components of induction motor keep watch over in line with man made intelligence (AI) concepts as a way to make the controller much less delicate to parameter alterations. chosen AI ideas are utilized for various induction motor keep an eye on suggestions. The ebook offers a pragmatic machine simulation version of the induction motor which may be used for learning quite a few induction motor force operations. The regulate ideas explored contain expert-system-based acceleration regulate, hybrid-fuzzy/PI two-stage regulate, neural-network-based direct self keep watch over, and genetic set of rules dependent prolonged Kalman clear out for rotor velocity estimation. There also are chapters on neural-network-based parameter estimation, genetic-algorithm-based optimized random PWM approach, and experimental investigations. A bankruptcy is equipped as a primer for readers to start with simulation experiences on numerous AI suggestions.

  • Presents significant synthetic intelligence ideas to induction motor drives
  • Uses a pragmatic simulation method of get readers all started on force improvement
  • Authored through skilled scientists with over twenty years of expertise within the box
  • Provides a number of examples and the newest examine effects
  • Simulation courses to be had from the book's significant other web site

This ebook may be worthy to graduate scholars and examine engineers who concentrate on electrical motor drives, electrical automobiles, and electrical send propulsion. Graduate scholars in clever keep an eye on, utilized electrical movement, and effort, in addition to engineers in business electronics, automation, and electric transportation, also will locate this booklet invaluable.

Simulation fabrics to be had for obtain at www.wiley.com/go/chanmotorContent:
Chapter 1 creation (pages 1–7):
Chapter 2 Philosophy of Induction Motor keep an eye on (pages 9–30):
Chapter three Modeling and Simulation of Induction Motor (pages 31–74):
Chapter four basics of clever keep an eye on Simulation (pages 75–108):
Chapter five Expert?System?Based Acceleration keep watch over (pages 109–132):
Chapter 6 Hybrid Fuzzy/PI Two?Stage regulate (pages 133–166):
Chapter 7 Neural?Network?based Direct Self regulate (pages 167–197):
Chapter eight Parameter Estimation utilizing Neural Networks (pages 199–241):
Chapter nine GA?Optimized prolonged Kalman clear out for velocity Estimation (pages 243–271):
Chapter 10 Optimized Random PWM innovations according to Genetic Algorithms (pages 273–311):
Chapter eleven Experimental Investigations (pages 313–371):
Chapter 12 Conclusions and destiny advancements (pages 373–379):

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Additional resources for Applied Intelligent Control of Induction Motor Drives

Example text

IEEE Transactions on Industry Applications, 30(1), 34–44. G. (1995) Fuzzy logic based efficiency optimization control of an indirect vector controlled induction motor drive. IEEE Transactions on Industrial Electronics, 42(2), 192–198. F. Y. (1994) Fuzzy logic application for intelligent control of a variable speed drive. IEEE Transactions on Energy Conversion, 9(4), 679–685. M. (1994) The Field Orientation Principle in Control of Induction Motors, Kluwer Academic Publishers, Boston. Vidyasagar, M.

9. Neural-network control algorithm"(Bose, 1997a): # ! gðtÞ x a. Input-output pair ¼ z IMðxÞ where x is the input samples, z is the outputs, g(t) denotes the generation function of input samples, and IM denotes the plant outputs. ' w x b. Training ¼ Re f b z where w is the weight of network, b is the bias of network, f denotes the activation function, and Re denotes the training algorithm. c. Implementation z0 ¼ f ðwx0 þ bÞ 0 where z denotes the output of neural-network and x0 denotes the practical inputs.

The six fifth-order equations are listed as follows: 1st equation: s s with state variables flsdm ; lsqm ; ids ; iqs ; oo g. 2nd equation: s s s s ; iqs ; idR ; iqR ; oo g. with state variables fids 3rd equation: s s ; iqR ; oo g. with state variables flsdm ; lsqm ; idR 4th equation: with state variables flsdR ; lsqR ; lsdm ; lsqm ; oo g. 5th equation: s s ; iqs ; oo g. with state variables flsdR ; lsqR ; ids 6th equation: s s ; iqR ; oo g. 14), rotor speed oo, rotor fluxes ðlsdr ; lsqr Þ, and stator currents ðids ; iqs Þ are the states, while rotor inertia J, stator and rotor inductances (Ls, Lr), mutual inductance LM, stator and rotor resistances (Rs, Rr), and the number of poles P are the parameters.

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