Doctoral Dissertations

Date of Award


Degree Type


Degree Name

Doctor of Philosophy


Engineering Science

Major Professor

Bimal K. Bose

Committee Members

Frederick W. Symonds, Walter L. Green, J. M. Bailey, B. R. Upadhyaya


Fuzzy logic shows enormous potential for advancing power electronics technology. Its application to DC and AC drives control is discussed here.

Initially, a phase-controlled bridge converter DC drive was considered. Analysis of converter performance at continuous and discontinuous conduction modes was first conducted. Fuzzy control was used to linearize the transfer characteristics of the converter in discontinuous conduction mode. It was then extended to current and speed loops, replacing the conventional proportional-integral controllers. The control algorithms were developed in detail, and verified by PC-SIMNON (developed by Lund Institute of Technology Sweden) digital simulation. Significant performance improvement was achieved over conventional control methods.

Efficiency optimization of an indirect vector controlled induction motor drive was next considered. An accurate loss model of the converter induction machine system was first developed. Steady-state fundamental and harmonics loss characteristics, besides the dynamic of the machine were analyzed and incorporated in the model, resulting in a new synchronous frame dynamic De-Qe equivalent circuit. The converter system has been modeled accurately for conduction and switching losses. The lossy models were then used in the validation of the fuzzy logic based on-line efficiency optimization control. At steady-state, the fuzzy controller adaptively changes the excitation current on the basis of measured input power, until the maximum efficiency point is reached. The pulsating torque, due to flux reduction, has been compensated by an ingenious feedforward scheme. During transients, rated flux is established, to get the best transient response. After a comprehensive simulation study, an experimental 5 hp drive system was tested, with the proposed controller implemented on a Texas Instrument TMS320C25 digital signal processor, and the theoretical development was fully validated.

Finally, fuzzy logic was applied in combination with model-reference adaptive control (MRAC) technique to slip gain tuning of an indirect vector controlled induction motor drive. The MRAC methods based on reactive power and D-axis voltage were combined through a weighting factor, generated by a fuzzy controller, that ensures the use of the best method for any point in the torque-speed plane. A second fuzzy controller tunes the slip gain based on combined detuning error and its slope. The drive performance was extensively investigated through simulations and experiments. The results confirmed the validity of the proposed method.

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