Neural-Network-Based Optimal Control for a Class of Unknown Discrete-Time Nonlinear Systems Using Globalized Dual Heuristic Programming

In this paper, a neuro-optimal control scheme for a class of unknown discrete-time nonlinear systems with discount factor in the cost function is developed. The iterative adaptive dynamic programming algorithm using globalized dual heuristic programming technique is introduced to obtain the optimal...

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Veröffentlicht in:IEEE transactions on automation science and engineering Jg. 9; H. 3; S. 628 - 634
Hauptverfasser: Liu, Derong, Wang, Ding, Zhao, Dongbin, Wei, Qinglai, Jin, Ning
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway, NJ IEEE 01.07.2012
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adaptive dynamic programming
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
approximate dynamic programming
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Control system synthesis
Control theory. Systems
Controllers
Convergence
Discrete time systems
Dynamic programming
Exact sciences and technology
globalized dual heuristic programming
Heuristic
Intelligent control
Neural networks
Optimal control
Software
Theoretical computing
Adaptive algorithm
Control program
neural networks
Non linear control
Iterative method
Heuristic programming
Efficiency
Cost function
Dynamic programming
Optimal control (mathematics)
Generic programming
Numerical convergence
Intelligent control
approximate dynamic programming
Adaptive dynamic programming
globalized dual heuristic programming
Neural network
Non linear system
Cost control
Neurocontrollers
Optimal control
Discrete time
Distributed control
Function derivative
Derivative control
ISSN:1545-5955, 1558-3783
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Zusammenfassung:In this paper, a neuro-optimal control scheme for a class of unknown discrete-time nonlinear systems with discount factor in the cost function is developed. The iterative adaptive dynamic programming algorithm using globalized dual heuristic programming technique is introduced to obtain the optimal controller with convergence analysis in terms of cost function and control law. In order to carry out the iterative algorithm, a neural network is constructed first to identify the unknown controlled system. Then, based on the learned system model, two other neural networks are employed as parametric structures to facilitate the implementation of the iterative algorithm, which aims at approximating at each iteration the cost function and its derivatives and the control law, respectively. Finally, a simulation example is provided to verify the effectiveness of the proposed optimal control approach. Note to Practitioners-The increasing complexity of the real-world industry processes inevitably leads to the occurrence of nonlinearity and high dimensions, and their mathematical models are often difficult to build. How to design the optimal controller for nonlinear systems without the requirement of knowing the explicit model has become one of the main foci of control practitioners. However, this problem cannot be handled by only relying on the traditional dynamic programming technique because of the "curse of dimensionality". To make things worse, the backward direction of solving process of dynamic programming precludes its wide application in practice. Therefore, in this paper, the iterative adaptive dynamic programming algorithm is proposed to deal with the optimal control problem for a class of unknown nonlinear systems forward-in-time. Moreover, the detailed implementation of the iterative ADP algorithm through the globalized dual heuristic programming technique is also presented by using neural networks. Finally, the effectiveness of the control strategy is illustrated via simulation study.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1545-5955
1558-3783
DOI:10.1109/TASE.2012.2198057