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 Introduction –Artificial Intelligence Fuzzy Control of Synchronous Motors Learning controller for torque ripple reduction in SR drives Conclusions

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 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 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 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)

KeKe z -1 e ee  + KeKe PMSM Reference Model W mm +  rr KK  + emem emem KmKm KmKm z -1 Fuzzy Inverse Model +  KuKu  Fuzzy Controller Fuzzy Learning of Synchronous Motors

Experimental result – Tracking Problem

Experimental result – Regulation Problem

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 Drives Input signals –Motor speed –Rotor position –Reference current Output: current increment (  I) Training signal: oscillating torque

Neuro Fuzzy Control of SR Drives

Neuro Fuzzy Position Estimation

Neuro Fuzzy position estimation

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.

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.