Abstract

Adaptive critic based neural networks have been found to be powerful tools in solving various optimal control problems. The adaptive critic approach consists of two neural networks which output the control values and the Lagrangian multipliers associated with optimal control. These networks are trained successively and when the outputs of the two networks are mutually consistent and satisfy the differential constraints, the controller network output produces optimal control. In this paper, we analyze the mechanics of convergence of the network solutions. We establish the necessary conditions for the network solutions to converge and show that the converged solution is optimal.

Meeting Name

2000 American Control Conference, 2000

Department(s)

Mechanical and Aerospace Engineering

Keywords and Phrases

Lagrangian Multipliers; Adaptive Control; Adaptive Critic Method; Convergence; Dynamic Programming; Learning; Learning (Artificial Intelligence); Necessary Conditions; Neural Networks; Neurocontrol; Neurocontrollers; Optimal Control

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2000 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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