Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285738
Title: Speed control of electric drives in the presence of load disturbances
Author: Goncalves da Silva, Wander
Awarding Body: Neewcastle University
Current Institution: University of Newcastle upon Tyne
Date of Award: 1999
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Abstract:
The speed control of a Brushless DC Motor Drive in the presence of load disturbance is investigated. Firstly some practical results are presented where a simple proportional-integral speed controller is used in the presence of a large step input speed demand as well as load disturbance. The wind-up problem caused by the saturation of the controller is discussed. In order to improve the performance of the proportional-integral speed controller in the presence of load variation, a load estimator is used with torque feedforward control. The results presented show the speed holding capability in the presence of load variation is significantly improved. A genetic algorithm is used on line to optimise the controller for different conditions such as large and small step input speed demand and load disturbance. The results presented show that a genetic algorithm is capable of finding the tuning of the controller for optimal performance. Single-input single-output and two-input two-output fuzzy speed controllers are also used and the results compared to a proportional-integral controller. Results are presented showing that a single-input single-output fuzzy controller works as a proportional controller with variable gain whereas the two-input two-output fuzzy controller is capable of driving the motor at variable speed and load torque with excellent performance. The robustness of the fuzzy controllers is compared to the proportional-integral controller and the results presented show that the fuzzy one is more robust then the proportional-integral. A genetic algorithm is also used on line for the optimisation of the two-input twooutput fuzzy speed controller and the results show that despite the large number of parameters to be optimised, the tuning for optimal performance is also possible.
Supervisor: Not available Sponsor: Fundacdo Coordenacäo de Aperfecoamento de Pessoal de Nivel Superior (CAPES) ; Fundagdo Educacional de Ituiutaba (FEIT)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.285738  DOI: Not available
Keywords: Fuzzy logic; Genetic algorithm; Load estimators Automatic control Control theory
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