Journal
NEUROCOMPUTING
Volume 190, Issue -, Pages 172-178Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2016.01.020
Keywords
Four-tank benchmark; Discrete-time recurrent neural network; Globally exponentially stable
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Funding
- Fundamental Research Funds for the Central Universities [XDJK2014C118]
- Natural Science Foundation of China [61403313, 61374078]
- Natural Science Foundation Project of Chongqing CSTC [cstc2014jcyjA40014]
- Graduate Student Research Innovation Project of Chongqing [CYS2015053]
- NPRP from the Qatar National Research Fund (a member of Qatar Foundation) [NPRP 4-1162-1-181]
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Based on model predictive control techniques, this paper presents a discrete-time recurrent neural network for solving four-tank benchmark problem which is reformulated to a convex programming problem. If the weighting matrices are positive definite symmetric, it is shown that the proposed neural network is globally exponentially stable and exponentially convergent to the exact optimal solutions. Finally, the experimental results have testified the effectiveness of the proposed approach and shown that the four-tank benchmark problem can be well resolved. (C) 2016 Elsevier B.V. All rights reserved.
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