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Abstract

This thesis investigates the hybrid application of stochastic and heuristic algorithms, in particular
genetic algorithms (GA), simulated annealing (SA) and Greedy search algorithms for the design of
linear and nonlinear control systems. We compare the rate of convergence, computational effort
required (FLOPS) and ease of implementation. Where possible, results are compared with the
more traditional control system design methodologies. Two specific practical applications include
aircraft flight control systems, and a nonlinear example of an industrial bioreactor fermentation
process.
Stochastic algorithms (GA) and heuristic algorithms (SA, Greedy, Tabu search) are powerful
search methods, capable of locating the global minimum or maximum (extremum) of multimodal
functions. They operate without the need for function gradients and are robust to noisy data. The
current research trend is directed towards the solution to constrained multiobjective optimization
problems of multimodal functions which may result in a family of optimal solutions (i.e Pareto
optimal set) and game theoretic approaches such as Nash and Stackelberg Equilibria.
Genetic algorithms suffer from one particular drawback, the rate of convergence can be
unacceptably slow if accurate solutions are sought. To overcome this deficiency, hybridization of
genetic algorithms with fast local search procedures are often used. Two heuristic based search
procedures are: greedy search and fast simulated annealing.
We investigate three types of Hybrid algorithms: (i) genetic algorithms (GA), (ii) hybrid GA +
simulated annealing (SA), and (iii) hybrid GA + greedy search. These methods are applied to
solving off-line linear and nonlinear control problems which may otherwise have no direct
analytical solution. In cases where solutions are obtainable using conventional methods, results are
compared with hybrid algorithms. Robustness against modeling errors, nonlinearities, disturbances
and parametric uncertainty will also be discussed.
We investigate five specific design applications, these include: training radial basis function (RBF)
neural networks, robust eigenstructure assignment (ESA), model reference adaptive control
(MRAC), robust mixed H2/H00 design, and lastly fault detection and isolation (FDI).
We show that hybrid algorithms can perform better, can handle a broader class of problems, and
have fewer restrictions than conventional methods. Furthermore, stochastic and heuristic methods
can directly deal with constraints.

Item Type:	Thesis - PhD
Authors/Creators:	Dirita, Vito
Keywords:	Genetic algorithms, Linear control systems, Simulated annealing (Mathematics)
Copyright Holders:	The Author
Copyright Information:	Copyright 2002 the Author - The University is continuing to endeavour to trace the copyright owner(s) and in the meantime this item has been reproduced here in good faith. We would be pleased to hear from the copyright owner(s).
Additional Information:	Thesis (Ph.D.)--University of Tasmania, 2002. Includes bibliographical references
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