Date of Graduation

2014

Document Type

Dissertation

Degree Type

PhD

College

Statler College of Engineering and Mineral Resources

Department

Mechanical and Aerospace Engineering

Committee Chair

Mario Perhinschi

Committee Member

Larry Banta

Committee Member

Mridul Gautam

Committee Member

Wade Huebsch

Committee Member

Powsiri Klinkhachorn

Committee Member

Victor Mucino

Abstract

This dissertation presents the design, development, and simulation testing of an adaptive trajectory tracking algorithm capable of compensating for various aircraft subsystem failures and upset conditions. A comprehensive adaptive control framework, here within referred to as the immune model reference adaptive control (IMRAC) algorithm, is developed by synergistically merging core concepts from the biologically- inspired artificial immune system (AIS) paradigm with more traditional optimal and adaptive control techniques. In particular, a model reference adaptive control (MRAC) algorithm is enhanced with the detection and learning capabilities of a novel, artificial neural network augmented AIS scheme. With the given modifications, the MRAC scheme is capable of detecting and identifying a given failure or upset condition, learning how to adapt to the problem, responding in a manner specific to the given failure condition, and retaining the learning parameters for quicker adaptation to subsequent failures of the same nature.

The IMRAC algorithm developed in this dissertation is applicable to a wide range of control problems. However, the proposed methodology is demonstrated in simulation for an unmanned aerial vehicle. The results presented show that the IMRAC algorithm is an effective and valuable extension to traditional optimal and adaptive control techniques. The implementation of this methodology can potentially have significant impacts on the operational safety of many complex systems.

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