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A function approximator approach to nonlinear systems analysis

Title:

A function approximator approach to nonlinear systems analysis

Ajayi, Oreoluwa Albert (2020) A function approximator approach to nonlinear systems analysis. Masters thesis, Concordia University.

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Abstract

A novel fuzzy inference system is introduced with desirable approximation properties for highly nonlinear systems that can be expressed in linear parameter varying form. This fuzzy inference system uses a hashing function to eliminate unnecessary computations and is compared with existing fuzzy inference systems. Furthermore, an application to nonlinear systems state estimation is provided, and the result is compared with that of the extended Kalman filter. The main benefit of this novel fuzzy inference system is its suitability to resource-constrained embedded control and estimation applications. Furthermore, multidimensional sampling is applied to the state-space variables and it is shown that (de)fuzzification in control systems and (de)modulation in communication systems are analogous. Finally, the values of fuzzy submodels as quantum mechanical objects are explored, for stability analysis and feedback controller synthesis for a class of nonlinear systems, using artificial intelligence approaches. Simulations confirm the effectiveness of the proposed approach.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (Masters)
Authors:Ajayi, Oreoluwa Albert
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Electrical and Computer Engineering
Date:13 August 2020
Thesis Supervisor(s):Aghdam, Amir
ID Code:987304
Deposited By: OREOLUWA ALBERT AJAYI
Deposited On:29 Jun 2021 20:49
Last Modified:29 Jun 2021 20:49
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