Control Design for Nonlinear Stochastic Processes in the Presence of Output Constraint

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Esfandiar, Khadijeh | 2022

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 55826 (06)
University: Sharif University of Technology
Department: Chemical and Petroleum Engineering
Advisor(s): Shahrokhi, Mohammad
Abstract:
This work addresses adaptive neural control for a class of stochastic nonlinear systems in the nonstrict-feedback form. By introducing a nonlinear mapping, the output-constrained stochastic system transformed into a new system without constraint. The systems under study is subject to state time delay, input nonlinearity, unavailable states, unknown dynamics and actuator failure. The appropriate Lyapunov-Krasovskii functionals is used to compensate the time-delay effects, the neural network is used to approximate the unknown nonlinearities, the linear state observer is constructed to estimate the unmeasured states, and a variable separation method is used to deal with the difficulty caused by the nonstrict-feedback structure. It is shown that the designed controller guarantees that all the signals in the closed-loop remain bounded in probability, and the tracking error finally converges to a neighborhood of the origin without violating the constraint
Keywords:
Intelligent Approximator ; Output Constraint ; Variable Separation Method ; Lyapunov-Krasovskii Function ; Neural Network ; Stochastic Nonlinear Systems ; Nonlinear Mapping

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