Supplementary Control for Quantized Discrete-Time Nonlinear Systems Under Goal Representation Heuristic Dynamic Programming

This article is concerned with supplementary control of discrete-time nonlinear systems with multiple controllers in the framework of goal representation heuristic dynamic programming (GrHDP), where a logarithmic quantizer is used to govern the network communication. For the addressed problem, a neu...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 35; no. 3; pp. 3202 - 3214
Main Authors: Wang, Xueli, Ding, Derui, Ge, Xiaohua, Han, Qing-Long
Format: Journal Article
Language:English
Published: United States IEEE 01.03.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Artificial neural networks
Control systems
Control tasks
Discrete time systems
Dynamic programming
Dynamic stability
Frequency modulation
Goal programming
Goal representation heuristic dynamic programming (GrHDP)
Hamilton–Jacobi–Isaacs (HJI) equation
Heuristic
Neural networks
Nonlinear control
Nonlinear systems
Observers
Power system stability
Problem solving
quantization
Quantization (signal)
Representations
supplementary control
System performance
Zero sum games
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:This article is concerned with supplementary control of discrete-time nonlinear systems with multiple controllers in the framework of goal representation heuristic dynamic programming (GrHDP), where a logarithmic quantizer is used to govern the network communication. For the addressed problem, a neural network (NN)-based observer is first proposed to estimate the unknown system state in the simultaneous presence of quantized influence. In light of the estimated states and the ideal control inputs via a zero-sum game, a GrHDP algorithm with a reinforced term is developed to implement the supplementary control task, where some novel weight updating rules are constructed by virtue of an additional tunable parameter to improve the system performance. Furthermore, a set of conditions about the stability of estimated error dynamics of both observer states and updated NNs' weights are derived by resorting to the Lyapunov stability theory. Finally, the effectiveness of the developed method is verified by a power system and a numerical experiment.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2022.3201521