Abstract
The paper presents a method of obtaining a simplified fuzzy model of an induction motor from measured data, without the necessity of preliminary knowledge of its internal structure and parameters. With the aim of avoiding a heuristic search for linguistic control rules, the paper presents one of the possibilities of the application of this method for an inverse fuzzy model based control. The proposed simplified fuzzy model of an induction motor was applied in the control of the desired torque of the drive with induction motor. Obtained results were first verified by simulation in programme Matlab and finally experimentally validated by measurements on an IC inverter–induction motor system. Simulation results and experimental measurements confirmed the correctness of the proposed fuzzy modelling and control method and its applicability also to other nonlinear dynamic systems.
Highlights
The fuzzy approach in the description and control of dynamic systems has recently undergone considerable development
The paper deals with the setup of a simplified fuzzy model of an induction motor using method based on fuzzy linearization of non-linear dynamic system
Simulation results and experimental measurements have confirmed that the fuzzy model obtained by the described method can be exploited for relatively c 2014 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING
Summary
The fuzzy approach in the description and control of dynamic systems has recently undergone considerable development. A very widely used FLC type is control based on fuzzy modelling methods that offer an alternative approach to describing complex nonlinear systems [1], [2], [3], [4] and reduce the number of rules in modelling higher order nonlinear systems [5]. The philosophy of systems description on basis of their qualitative parameters enables the finding of relatively simple and practically applicable models even in cases of complex non-linear systems, at the cost of a certain degree of inaccuracy in their description [15]. Modern artificial intelligence methods (fuzzy approach, neural networks) enable the finding of its substantially more simple and practically applicable models based on the description of its significant qualitative properties [16], [17], [18], [19] and [20]
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