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Neural network solution for fixed-fi...

Cheng, Tao.

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Neural network solution for fixed-final time optimal control of nonlinear systems.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Neural network solution for fixed-final time optimal control of nonlinear systems./
作者:	Cheng, Tao.
面頁冊數:	110 p.
附註:	Adviser: Frank L. Lewis.
Contained By:	Dissertation Abstracts International67-10B.
標題:	Engineering, Electronics and Electrical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3239831
ISBN:	9780542942990

Neural network solution for fixed-final time optimal control of nonlinear systems.
Cheng, Tao.

Neural network solution for fixed-final time optimal control of nonlinear systems. - 110 p.

Adviser: Frank L. Lewis.

Thesis (Ph.D.)--The University of Texas at Arlington, 2006.

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.

ISBN: 9780542942990Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Neural network solution for fixed-final time optimal control of nonlinear systems.
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100 1 $a Cheng, Tao. $3 1298439
245 1 0 $a Neural network solution for fixed-final time optimal control of nonlinear systems.
300 $a 110 p.
500 $a Adviser: Frank L. Lewis.
500 $a Source: Dissertation Abstracts International, Volume: 67-10, Section: B, page: 5933.
502 $a Thesis (Ph.D.)--The University of Texas at Arlington, 2006.
520 $a 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.
520 $a 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.
520 $a In this research, practical methods for the design of H 2 and Hinfinity 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 H2 and Hinfinity optimal controllers in feedback strategy form. The resulting Hamilton-Jacobi-Bellman (HJB) and Hamilton-Jacobi-Isaacs (HJI) equations are derived respectively.
520 $a Neural networks are used along with the least-squares method to solve the Hamilton-Jacobi differential equations in the H 2 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.
590 $a School code: 2502.
650 4 $a Engineering, Electronics and Electrical. $3 626636
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710 2 0 $a The University of Texas at Arlington. $3 1025869
773 0 $t Dissertation Abstracts International $g 67-10B.
790 $a 2502
790 1 0 $a Lewis, Frank L., $e advisor
791 $a Ph.D.
792 $a 2006
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