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Graph Based Approach to Nonlinear Model Predictive Control with Application to Combustion Control and Flow Control

Title: A Graph Based Approach to Nonlinear Model Predictive Control with Application to Combustion Control and Flow Control.
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Name(s): Reese, Brandon M., author
Collins, E. (Emmanuel), professor co-directing dissertation
Alvi, Farrukh S., professor co-directing dissertation
Foo, Simon Y., university representative
Cattafesta, Louis N., committee member
Oates, William S., committee member
Florida State University, degree granting institution
College of Engineering, degree granting college
Department of Mechanical Engineering, degree granting department
Type of Resource: text
Genre: Text
Issuance: monographic
Date Issued: 2015
Publisher: Florida State University
Place of Publication: Tallahassee, Florida
Physical Form: computer
online resource
Extent: 1 online resource (127 pages)
Language(s): English
Abstract/Description: Systems with a priori unknown, and time-varying dynamic behavior pose a significant challenge in the field of Nonlinear Model Predictive Control (NMPC). When both the identification of the nonlinear system and the optimization of control inputs are done robustly and efficiently, NMPC may be applied to control such systems. This dissertation presents a novel method for adaptive NMPC, called Adaptive Sampling Based Model Predictive Control (SBMPC), that combines a radial basis function neural network identification algorithm with a nonlinear optimization method based on graph search. Unlike other NMPC methods, it does not rely on linearizing the system or gradient based optimization. Instead, it discretizes the input space to the model via pseudo-random sampling and feeds the sampled inputs through the nonlinear model, producing a searchable graph. An optimal path is found using an efficient graph search method. Adaptive SBMPC is used in simulation to identify and control a simple plant with clearly visualized nonlinear dynamics. In these simulations, both fixed and time-varying dynamic systems are considered. Next, a power plant combustion simulation demonstrates successful control of a more realistic Multiple-Input Multiple-Output system. The simulated results are compared with an adaptive version of Neural GPC, an existing NMPC algorithm based on Netwon-Raphson optimization and a back propagation neural network model. When the cost function exhibits many local minima, Adaptive SBMPC is successful in finding a globally optimal solution while Neural GPC converges to a solution that is only locally optimal. Finally, an application to flow separation control is presented with experimental wind tunnel results. These results demonstrate real time feasibility, as the control updates are computed at 100 Hz, and highlight the robustness of Adaptive SBMPC to plant changes and the ability to adapt online. The experiments demonstrate separation control for a NACA 0025 airfoil with Reynolds Numbers ranging from 90,000 to 150,000 for both fixed and pitching (.33 deg/s) angles of attack.
Identifier: FSU_migr_etd-9435 (IID)
Submitted Note: A Dissertation submitted to the Department of Mechanical Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy.
Degree Awarded: Spring Semester, 2015.
Date of Defense: April 2, 2015.
Keywords: Adaptive Control, Flow Control, Model Predictive Control, Neural Network, Nonlinear, Power Plant Control
Bibliography Note: Includes bibliographical references.
Advisory Committee: Emmanuel G. Collins, Professor Co-Directing Dissertation; Farrukh S. Alvi, Professor Co-Directing Dissertation; Simon Y. Foo, University Representative; Louis N. Cattafesta, Committee Member; William S. Oates, Committee Member.
Subject(s): Mechanical engineering
Persistent Link to This Record: http://purl.flvc.org/fsu/fd/FSU_migr_etd-9435
Owner Institution: FSU

Choose the citation style.
Reese, B. M. (2015). A Graph Based Approach to Nonlinear Model Predictive Control with Application to Combustion Control and Flow Control. Retrieved from http://purl.flvc.org/fsu/fd/FSU_migr_etd-9435