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Constrained multi-variable generalized predictive control using a dual neural network

  • ICONIP 2006
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Abstract

Multi-variable generalized predictive control algorithm has obtained great success in process industries. However, it suffers from a high computational cost because the multi-stage optimization approach in the algorithm is time-consuming when constraints of the control system are considered. In this paper, a dual neural network is employed to deal with the multi-stage optimization problem, and bounded constraints on the input and output signals of the control system are taken into account. The dual neural network has many favorable features such as simple structure, rapid execution, and easy implementation. Therefore, the computation efficiency, in comparison with the consecutive executions of numerical algorithms on digital computers, is increased dramatically. In addition, the dual network model can yield the exact optimum values of future control signals while many other neural networks only obtain the approximate optimal solutions. Hence the multi-variable generalized predictive control algorithm based on the dual neural network is suitable for industrial applications with the real-time computation requirement. Simulation examples are given to demonstrate the efficiency of the proposed approach.

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Acknowledgments

This research was supported in part by the National Natural Science Foundation of China (Grants 60205004, 50475179, 60334020, 60528002, 60621001, and 60635010), the National Basic Research Program (973) of China (Grant 2002CB312200), and the Hi-Tech R&D Program (863) of China (Grants 2002AA423160 and 2005AA420040).

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Authors and Affiliations

  1. Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, The Chinese Academy of Sciences, P.O. Box 2728, Beijing, 100080, China

    Long Cheng, Zeng-Guang Hou & Min Tan

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  1. Long Cheng
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  2. Zeng-Guang Hou
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  3. Min Tan
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Correspondence to Zeng-Guang Hou.

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Cheng, L., Hou, ZG. & Tan, M. Constrained multi-variable generalized predictive control using a dual neural network. Neural Comput & Applic 16, 505–512 (2007). https://doi.org/10.1007/s00521-007-0150-6

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  • DOI: https://doi.org/10.1007/s00521-007-0150-6

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