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Neural network solution for fixed-fi...
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Cheng, Tao.
Neural network solution for fixed-final time optimal control of nonlinear systems.
紀錄類型:
書目-電子資源 : 單行本
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:
Source: Dissertation Abstracts International, Volume: 67-10, Section: B, page: 5933.
Contained By:
Dissertation Abstracts International67-10B.
標題:
Engineering, Electronics and Electrical. -
電子資源:
Download PDF (下載PDF全文)
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.
Source: Dissertation Abstracts International, Volume: 67-10, Section: B, page: 5933.
Thesis (Ph.D.)--The University of Texas at Arlington, 2006.
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.
ISBN: 9780542942990Subjects--Topical Terms:
170927
Engineering, Electronics and Electrical.
Neural network solution for fixed-final time optimal control of nonlinear systems.
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Source: Dissertation Abstracts International, Volume: 67-10, Section: B, page: 5933.
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Adviser: Frank L. Lewis.
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Thesis (Ph.D.)--The University of Texas at Arlington, 2006.
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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.
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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.
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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.
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Download PDF (下載PDF全文)
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