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Neural Network Solution For Fixed-final Time Optimal Control Of Nonlinear Systems

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Neural Network Solution For Fixed-final Time Optimal Control Of Nonlinear Systems

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dc.contributor.author Cheng, Tao en_US
dc.date.accessioned 2007-08-23T01:56:15Z
dc.date.available 2007-08-23T01:56:15Z
dc.date.issued 2007-08-23T01:56:15Z
dc.date.submitted December 2006 en_US
dc.identifier.other DISS-1540 en_US
dc.identifier.uri http://hdl.handle.net/10106/190
dc.description.abstract In this research, practical methods for the design of H2 and H-Infinity optimal state feedback controllers for unconstrained and constrained input systems are proposed. The dynamic programming principle is used along with special quasi-norms to derive the structure of both the saturated and optimal controllers in feedback strategy form. The resulting Hamilton-Jacobi-Bellman (HJB) and Hamilton-Jacobi-Isaacs (HJI) equations are derived respectively. Neural networks are used along with the least-squares method to solve the Hamilton-Jacobi differential equations in the H2 case, and the cost and disturbance in the H-Infinity case. The result is a neural network unconstrained or constrained feedback controller that has been tuned a priori offline with the training set selected using Monte Carlo methods from a prescribed region of the state space which falls within the region of asymptotic stability. The obtained algorithms are applied to different examples including the linear system, chained form nonholonomic system, and Nonlinear Benchmark Problem to reveal the power of the proposed method. Finally, a certain time-folding method is applied to solve optimal control problem on chained form nonholonomic systems with above obtained algorithms. The result shows the approach can effectively provide controls for nonholonomic systems. en_US
dc.description.sponsorship Lewis, Frank en_US
dc.language.iso EN en_US
dc.publisher Electrical Engineering en_US
dc.title Neural Network Solution For Fixed-final Time Optimal Control Of Nonlinear Systems en_US
dc.type Ph.D. en_US
dc.contributor.committeeChair Lewis, Frank en_US
dc.degree.department Electrical Engineering en_US
dc.degree.discipline Electrical Engineering en_US
dc.degree.grantor University of Texas at Arlington en_US
dc.degree.level doctoral en_US
dc.degree.name Ph.D. en_US
dc.identifier.externalLink https://www.uta.edu/ra/real/editprofile.php?onlyview=1&pid=27
dc.identifier.externalLinkDescription Link to Research Profiles

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