Optimal control of unknown nonaffine nonlinear discrete-time systems based on adaptive dynamic programming

An intelligent-optimal control scheme for unknown nonaffine nonlinear discrete-time systems with discount factor in the cost function is developed in this paper. The iterative adaptive dynamic programming algorithm is introduced to solve the optimal control problem with convergence analysis. Then, t...

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Veröffentlicht in:Automatica (Oxford) Jg. 48; H. 8; S. 1825 - 1832
Hauptverfasser: Wang, Ding, Liu, Derong, Wei, Qinglai, Zhao, Dongbin, Jin, Ning
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier Ltd 01.08.2012
Elsevier
Schlagworte:
Adaptive control systems
Adaptive critic designs
Adaptive dynamic programming
Algorithmics. Computability. Computer arithmetics
Applied sciences
Approximate dynamic programming
Computer science; control theory; systems
Computer simulation
Control system synthesis
Control systems
Control theory. Systems
Cost function
Dynamical systems
Exact sciences and technology
Globalized dual heuristic programming
Intelligent control
Neural network
Nonlinear dynamics
Nonlinearity
Optimal control
Theoretical computing
Adaptive dynamic programming
Globalized dual heuristic programming
Optimal control
Approximate dynamic programming
Neural network
Adaptive critic designs
Intelligent control
Control system analysis
Adaptive algorithm
Adaptive system
Control program
Non linear control
Iterative method
Fork join problem
Adaptive method
Heuristic programming
Efficiency
Cost function
Dynamic programming
Optimal control (mathematics)
Generic programming
Numerical convergence
Non linear system
Cost control
Discrete time
ISSN:0005-1098, 1873-2836
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:An intelligent-optimal control scheme for unknown nonaffine nonlinear discrete-time systems with discount factor in the cost function is developed in this paper. The iterative adaptive dynamic programming algorithm is introduced to solve the optimal control problem with convergence analysis. Then, the implementation of the iterative algorithm via globalized dual heuristic programming technique is presented by using three neural networks, which will approximate at each iteration the cost function, the control law, and the unknown nonlinear system, respectively. In addition, two simulation examples are provided to verify the effectiveness of the developed optimal control approach.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2012.05.049