Masters Theses

Kim C. Ng

Date of Award

12-1995

Degree Type

Thesis

Degree Name

Master of Science

Major

Electrical Engineering

Major Professor

Mohan M. Trivedi

Committee Members

Igor Alexeff, Marshall Pace

Abstract

The main focus of this thesis is on the development of intelligent robotic systems which are capable of learning a wide array of intelligent behaviors. The framework utilized in this development is that of nonclassical control architectures to realize real-time and primarily reactive system performance.

A Neural integrated Fuzzy conTroller (NiF-T), which integrates the fuzzy logic representation of human knowledge with the learning capability of neural networks, is developed for nonlinear dynamic control problems. The NiF-T architecture comprises three distinct parts: (1) Fuzzy logic Membership Functions (FMF), (2) Rule Neural Network (RNN), and (3) Output-Refinement Neural Net- work (ORNN). FMF are utilized to fuzzify input parameters. RNN interpolates the fuzzy rule set; after defuzzification, the output is used to train ORNN. The weights of the ORNN can be adjusted on-line to fine-tune the controller.

NiF-T can be applied for a wide range of sensor-driven robotics applications, which are characterized by high noise levels and nonlinear behavior, and where system models are unavailable or are unreliable. In this thesis, real-time imple- mentations of Ball Balancing Beam (BBB), autonomous mobile robot navigation, and mobile robot convoying behavior utilizing the NiF-T are realized. The NiF-T successfully balances balls with one-third of the required 27 rules. With learning capability, these systems approach their goals more frequently, in general. The robot, SMAR-T, successfully and reliably hugs the wall, locks onto the hall center, and convoys a leader at any specified distance.

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