Application of Adaptive Neural Network Systems in Controller Design of Nonlinear Processes

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Talkhoncheh, Mahdi Khajeh | 2015

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 47534 (06)
University: Sharif University of Technology
Department: Chemical and Petroleum Engineering
Advisor(s): Shahrokhi, Mohammad
Abstract:
In this project design of adaptive neural network controller for uncertain nonlinear systems in the presence of different restrictions has been investigated. After providing introduction and motivation, two general forms of nonlinear systems including strict- feedback and pure- feedback have been introduced. In order to solve the problem caused by each restriction, a solution has been proposed. For the strict- feedback nonlinear systems several restrictions are considered such as, unknown dynamic model, unmeasured state, input saturation, unknown control directions, time delays in states and input. Stability analysis based on the Lyapunov theorem has been established. In addition, two kinds of pure-feedback systems with known and unknown control directions have been considered. For each case, it is proved that all closed loop signals are semi-globally uniformly ultimately bounded (SGUUB) and the output tracking error converges to a small neighborhood of the origin by choosing the design parameters appropriately. Numerical example illustrates effectiveness of the proposed method for each systems
Keywords:
Adaptive Control ; Neural Networks ; Time Delay ; Input Constraint ; Adoptive Intelligence Observer ; Unknown Control Direction ; controller Singularity

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