Application of Learning Methodologies in Control of Power Electronics Drives
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Application of Learning Methodologies in Control of Power Electronics Drives J. L. da Silva Neto, L.G. Rolim , W. I. S uemitsu, L. O. A. P. H enriques , P.J. C osta Branco, M . G. S imões. Presentation. Introduction Artificial Intelligence Fuzzy Control of Synchronous Motors

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Application of Learning Methodologies in Control of Power Electronics Drives

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Application of learning methodologies in control of power electronics drives

Application of Learning Methodologies in Control of Power Electronics Drives

J. L. da Silva Neto, L.G. Rolim, W. I. Suemitsu,

L. O. A. P. Henriques, P.J. Costa Branco,

M. G. Simões


Presentation

Presentation

  • Introduction

    • Artificial Intelligence

  • Fuzzy Control of Synchronous Motors

  • Learning controller for torque ripple reduction in SR drives

  • Conclusions


Introduction

Introduction

Learning methodologies:

  • Expert Systems: knowledge represented at a symbolic level.

  • Genetic Algorithms: Computational models based on the theory of evolution (fitness, mutation and reproduction)

  • Fuzzy Logic Systems: linguistic technique

  • Neural Networks: mathematical model of artificial neurons


Fuzzy logic and or neural networks

Fuzzy Logic and/or Neural Networks

  • Some characteristics:

    • Capability to model unclearly correlated information

    • Parameter estimation (e.g torque and flux)

    • In last ten years, appliance drives have be increasely use a Fuzzy and Neural systems.

    • Replacement of classical controllers by controllers with learning methodologies.


Electrical drive control

Electrical Drive Control

  • Evolution of digital signal processors, and circuit integration, make possible the implementation of complex control

  • Several types of motors can use the features of “Intelligent” drives:

    • AC machines (Induction Motors, Synchronous Machine)

    • Switched Reluctance Motors


Fuzzy control of synchronous motors

Fuzzy Control of Synchronous Motors

  • Fuzzy Logic Adaptation Mechanism (FLAM)

    • Objective is to change the rule definitions in the fuzzy logic controller (FLC) base table, according the comparison between the reference model and the system output.

    • Composed by a fuzzy inverse model and knowledge base modifier

    • It was used to prove the effectiveness of the control in a TMS320C30 DSP-based speed fuzzy control of a permanent magnet synchronous motor (PMSM)


Fuzzy learning of synchronous motors

m

Reference

Model W

r

e

+

Ke

+

Fuzzy

Controller

z-1

PMSM

Ku

+

Ke

em

e

z-1

+

em

Km

Km

Fuzzy

Inverse

Model

K

Fuzzy Learning of Synchronous Motors


Experimental result tracking problem

Experimental result – Tracking Problem


Application of learning methodologies in control of power electronics drives

Experimental result – Regulation Problem


Neuro fuzzy control of sr drives

Neuro Fuzzy Control of SR Drives

  • Low cost (material and manufacturing)

  • Good thermal behavior

  • Fault tolerance

  • Reliable

  • Easy to repair

  • Torque ripple

  • Nonlinear model


Neuro fuzzy control of sr drives1

Neuro Fuzzy Control of SR Drives

  • Input signals

    • Motor speed

    • Rotor position

    • Reference current

  • Output: current increment (I)

  • Training signal: oscillating torque


Neuro fuzzy control of sr drives2

Neuro Fuzzy Control of SR Drives


Neuro fuzzy control of sr drives3

Neuro Fuzzy Control of SR Drives


Neuro fuzzy control of sr drives4

Neuro Fuzzy Control of SR Drives


Neuro fuzzy position estimation

Neuro Fuzzy Position Estimation


Application of learning methodologies in control of power electronics drives

Neuro Fuzzy position estimation


Conclusions

Conclusions

  • The adaptive fuzzy strategy presented applied for PMSM drives has proved to be very effective when applied for motion control applications.

  • It has been implemented on a speed control of a PM motor, it can be extended for other kinds closed loop motor control.

  • One highlighted characteristic of this algorithm is that it can compensate non-linear load variations without the need of a completely modeled load.


Conclusions1

Conclusions

  • For the SR drive the neuro-fuzzy strategy has shown to be effective to reduce torque oscillations

  • The adaptive algorithm automatically learns a current profile without the need of observers and state estimators.


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