A Theoretical Foundation of Goal Representation Heuristic Dynamic Programming

Goal representation heuristic dynamic programming (GrHDP) control design has been developed in recent years. The control performance of this design has been demonstrated in several case studies, and also showed applicable to industrial-scale complex control problems. In this paper, we develop the th...

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Vydané v:	IEEE transaction on neural networks and learning systems Ročník 27; číslo 12; s. 2513 - 2525
Hlavní autori:	Zhong, Xiangnan, Ni, Zhen, He, Haibo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.12.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptive dynamic programming (ADP) Algorithm design and analysis Approximation algorithms Control methods Convergence convergence analysis Design Dynamic programming Goal programming goal representation Heuristic Mathematical model neural network online learning and control Performance analysis Performance indices Problem solving Reinforcement Representations Theoretical analysis Upper bound
ISSN:	2162-237X, 2162-2388
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Abstract	Goal representation heuristic dynamic programming (GrHDP) control design has been developed in recent years. The control performance of this design has been demonstrated in several case studies, and also showed applicable to industrial-scale complex control problems. In this paper, we develop the theoretical analysis for the GrHDP design under certain conditions. It has been shown that the internal reinforcement signal is a bounded signal and the performance index can converge to its optimal value monotonically. The existence of the admissible control is also proved. Although the GrHDP control method has been investigated in many areas before, to the best of our knowledge, this is the first study of presenting the theoretical foundation of the internal reinforcement signal and how such an internal reinforcement signal can provide effective information to improve the control performance. Numerous simulation studies are used to validate the theoretical analysis and also demonstrate the effectiveness of the GrHDP design.
AbstractList	Goal representation heuristic dynamic programming (GrHDP) control design has been developed in recent years. The control performance of this design has been demonstrated in several case studies, and also showed applicable to industrial-scale complex control problems. In this paper, we develop the theoretical analysis for the GrHDP design under certain conditions. It has been shown that the internal reinforcement signal is a bounded signal and the performance index can converge to its optimal value monotonically. The existence of the admissible control is also proved. Although the GrHDP control method has been investigated in many areas before, to the best of our knowledge, this is the first study of presenting the theoretical foundation of the internal reinforcement signal and how such an internal reinforcement signal can provide effective information to improve the control performance. Numerous simulation studies are used to validate the theoretical analysis and also demonstrate the effectiveness of the GrHDP design.
Author	Haibo He Zhen Ni Xiangnan Zhong
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Snippet	Goal representation heuristic dynamic programming (GrHDP) control design has been developed in recent years. The control performance of this design has been...
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SubjectTerms	Adaptive dynamic programming (ADP) Algorithm design and analysis Approximation algorithms Control methods Convergence convergence analysis Design Dynamic programming Goal programming goal representation Heuristic Mathematical model neural network online learning and control Performance analysis Performance indices Problem solving Reinforcement Representations Theoretical analysis Upper bound
Title	A Theoretical Foundation of Goal Representation Heuristic Dynamic Programming
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