Access Type

Open Access Dissertation

Date of Award

January 2013

Degree Type

Dissertation

Degree Name

Ph.D.

Department

Electrical and Computer Engineering

First Advisor

Le Yi Wang

Abstract

Establishing real-time models for electric motors is of importance for capturing authentic dynamic behavior of the motors to improve control performance, enhance robustness, and support diagnosis. Quantized sensors are less expensive and remote controlled motors mandate signal quantization. Such limitations on observations introduce challenging issues in motor parameter estimation. This dissertation develops estimators for model parameters of permanent magnet direct current motors (PMDC) using quantized speed measurements. A typical linearized model structure of PMDC motors is used as a benchmark platform to demonstrate the technology, its key properties, and benefits. Convergence properties are established. Simulations and experimental studies are performed to illustrate potential applications of the technology.

Remotely-controlled Permanent Magnet DC (PMDC) motors must transmit speed measurements and receive control commands via communication channels.

Sampling, quantization, data transfer, and signal reconstruction are mandatory in such networked systems, and introduce additional dynamic subsystems that substantially affect feedback stability and performance. The intimate interaction among sampling periods, signal estimation step sizes, and feedback dynamics entails careful design considerations in such systems. This dissertation investigates the impact of these factors on PMDC motor performance, by rigorous analysis, simulation case studies, and design trade-off examination. The findings of this dissertation will be of importance in providing design guidelines for networked mobile systems, such as autonomous vehicles, mobile sensors, unmanned aerial vehicles which often use electric motors as main engines.

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