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Normalized-Model Reference System for Parameter Estimation of Induction Motors

by Adolfo Véliz-Tejo, Juan Carlos Travieso-Torres, Andrés A. Peters, Andrés Mora and Felipe Leiva-Silva

Energies 2022, 15(13), 4542; https://doi.org/10.3390/en15134542 - 21 Jun 2022

Cited by 9 | Viewed by 2590

Abstract

This manuscript proposes a short tuning march algorithm to estimate induction motors (IM) electrical and mechanical parameters. It has two main novel proposals. First, it starts by presenting a normalized-model reference adaptive system (N-MRAS) that extends a recently proposed normalized model reference adaptive controller for parameter estimation of higher-order nonlinear systems, adding filtering. Second, it proposes persistent exciting (PE) rules for the input amplitude. This N-MRAS normalizes the information vector and identification adaptive law gains for a more straightforward tuning method, avoiding trial and error. Later, two N-MRAS designs consider estimating IM electrical and mechanical parameters. Finally, the proposed algorithm considers starting with a V/f speed control strategy, applying a persistently exciting voltage and frequency, and applying the two designed N-MRAS. Test bench experiments validate the efficacy of the proposed algorithm for a 10 HP IM. Full article

(This article belongs to the Special Issue Design and Control of Electrical Motor Drives II)

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Abstract

To feasibly achieve economical and satisfactory robust velocity tracking of an induction machine (IM), we propose an Arduino-implemented intelligent speed controller. Because a voltage/frequency controlled IM framework is simple and well suited for being controlled by the proposed speed controller, it is adopted herein. Taking into account easy implementation and good performance, we design the controller using a modified Ziegler-Nichols PID (modified Z-N PID) and a fuzzy logic controller (FLC). The modified Z-N PID and the FLC are connected in tandem. The latter is designed based on the output signal of the former for adaptively yielding adequate torque commands. Experimental results of IM velocity tracking controlled by our PC-based and Arduino-based speed controllers consistently show that the proposed design scheme can yield remarkable tracking performance and robustness. In addition, it is demonstrated that the proposed Arduino-implemented controller is not only viable but also effective in terms of cost, size and tracking performance. Full article

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