Modelling and multi-objective optimal control of batch processes using recurrent neuro-fuzzy networks

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Abstract

In this paper, the modelling and multi-objective optimal control of batch processes, using a recurrent neuro-fuzzy network, are presented. The recurrent neuro-fuzzy network, forms a “global” nonlinear long-range prediction model through the fuzzy conjunction of a number of “local” linear dynamic models. Network output is fed back to network input through one or more time delay units, which ensure that predictions from the recurrent neuro-fuzzy network are long-range. In building a recurrent neural network model, process knowledge is used initially to partition the processes non-linear characteristics into several local operating regions, and to aid in the initialisation of corresponding network weights. Process operational data is then used to train the network. Membership functions of the local regimes are identified, and local models are discovered via network training. Based on a recurrent neuro-fuzzy network model, a multi-objective optimal control policy can be obtained. The proposed technique is applied to a fed-batch reactor.

This work was supported by the UK EPSRC (GR/N13319, GR/R10875).
Jie Zhang received his BSc degree in Control Engineering from Hebei University of Technology, Tianjin, China, in 1986 and his PhD degree in Control Engineering from City University, London, in 1991. He is a Lecturer in the School of Chemical Engineering & Advanced Materials, University of Newcastle, England. His research interests include: neural networks, neuro-fuzzy systems, fault detection and diagnosis, process control, intelligent control systems, optimal control of batch processes, iterative learning control, and multivariate statistical process control. He has published over 150 papers in international journals, books, and conferences. He served as a reviewer for many prestigious international journals including IEEE Transactions on Neural Networks, IEEE Transactions on Fuzzy Systems, Neural Networks, Automatica, Chemical Engineering Science, and IEE Proceedings. He is on the Editorial Board of Neurocomputing published by Elsevier. He is a Senior Member of IEEE, a member of the IEEE Control Systems Society, and IEEE Computational Intelligence Society.