Differential recurrent neural network based predictive control

Al Seyab, Rihab ORCID: 0000-0001-6384-193X and Cao, Y (2008) Differential recurrent neural network based predictive control. Differential recurrent neural network based predictive control, 21. pp. 1239-1244. doi:10.1016/S1570-7946(06)80216-6

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Al Sayeb, R.K. (2006) Differential recurrent neural network based predicative control.pdf - Accepted Version
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In this paper an efficient algorithm to train general differential recurrent neural network (DRNN) is developed. The trained network can be directly used in the nonlinear model predictive control (NMPC) context. The neural network is represented in a general nonlinear state-space form and used to predict the future dynamic behaviour of the nonlinear process in real time. In the new training algorithms, the ODEs of the model and the dynamic sensitivity are solved simultaneously using Taylor series expansion and automatic differentiation (AD) techniques. The same approach is also used to solve the online optimization problem in the predictive controller. The efficiency and effectiveness of the DRNN training algorithm and the NMPC approach are demonstrated through a two-CSTR case study. A good model fitting for the nonlinear plant at different sampling rates is obtained using the new method. A comparison with other approaches shows that the new algorithm can considerably reduce network training time and improve solution accuracy. The DRNN based NMPC approach results in good control performance under different operating conditions.

Item Type: Article
Article Type: Article
Uncontrolled Keywords: Predictive control; Recurrent neural networks; Nonlinear system identification; Nonlinear control
Subjects: T Technology > T Technology (General)
Divisions: Schools and Research Institutes > School of Education and Science
Research Priority Areas: Applied Business & Technology
Depositing User: Rihab Al Seyab
Date Deposited: 01 Nov 2019 11:23
Last Modified: 01 Sep 2023 11:43
URI: https://eprints.glos.ac.uk/id/eprint/7477

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