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Neural-Network-Based Near-Optimal Control for a Class of Discrete-Time Affine Nonlinear Systems With Control Constraints

Huaguang Zhang, Yanhong Luo, Derong Liu·2009·IEEE Transactions on Neural Networks

Abstract

In this paper, the near-optimal control problem for a class of nonlinear discrete-time systems with control constraints is solved by iterative adaptive dynamic programming algorithm. First, a novel nonquadratic performance functional is introduced to overcome the control constraints, and then an iterative adaptive dynamic programming algorithm is developed to solve the optimal feedback control problem of the original constrained system with convergence analysis. In the present control scheme, there are three neural networks used as parametric structures for facilitating the implementation of the iterative algorithm. Two examples are given to demonstrate the convergence and feasibility of the proposed optimal control scheme.

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Metadata

Authors
Zhang, H., Luo, Y., & Liu, D.
Venue
IEEE Transactions on Neural Networks
Published
2009-09
Volume
20
Issue
9
Pages
1490-1503
ISSN
  • 1045-9227 (ISSN-L)
  • 1941-0093
OpenAlex
W2137092694
Cited by
649
References
3
FWCI
25.43
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Neural-Network-Based Near-Optimal Control for a Class of Discrete-Time Affine Nonlinear Systems With Control Constraints. https://app.cochat.ai/papers/10.1109/tnn.2009.2027233
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Zhang, H., Luo, Y., & Liu, D. (2009). Neural-Network-Based Near-Optimal Control for a Class of Discrete-Time Affine Nonlinear Systems With Control Constraints. IEEE Transactions on Neural Networks. https://doi.org/10.1109/tnn.2009.2027233

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.