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UBC Theses and Dissertations

Implementation and evaluation of an intelligent tuner for an ill-defined servo-motor system Barlev, Shimshon

Abstract

This research deals with automated, knowledge-based tuning of servo motors. Conventional adaptive techniques can perform unsatisfactorily when the controlled system is complex and incompletely known. Furthermore, they cannot directly capture and utilize the knowledge of experienced human operators, in tuning a servo system. The tuning technique developed and implemented in this work can overcome these shortcomings. To integrate the controller of a high speed servo-motor with the tuning knowledge of experienced system operators, a hierarchical control structure is developed in this research. Specifically, the programmable hard controller of a servo-motor is tuned automatically in the lowest level. In the highest level, tuning knowledge expressed as a set of linguistic rules is generated and mathematically formulated using fuzzy set theory and fuzzy logic. This leads to the development of an off-line decision table in which tuning actions are matched with the servo-motor performance. A computer implementation of a servo expert is used in the intermediate level to update the controller parameters so that the actual response would meet a set of predefined performance specifications expressed in terms of the performance of a reference model. Learning and self-organization, as well as automated specification updating, if necessary, are used to improve the performance accuracy and system robustness. The intelligent tuner is implemented on a commercially available servo-motor system, and experiments are carried out to demonstrate its performance when implemented on the physical system. Furthermore, simulation results are used to evaluate the performance of the intelligent tuner when implemented on an ill-defined process.

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