Modelling and Multi-Objective Optimal Control of Batch Processes Using Recurrent Neuro-fuzzy Networks

doi:10.1007/s11633-006-0001-4

January 2006

Article Contents

Modelling and Multi-Objective Optimal Control of Batch Processes Using Recurrent Neuro-fuzzy Networks. International Journal of Automation and Computing, vol. 3, no. 1, pp. 1-7, 2006. doi: 10.1007/s11633-006-0001-4

Cite as: Modelling and Multi-Objective Optimal Control of Batch Processes Using Recurrent Neuro-fuzzy Networks. International Journal of Automation and Computing, vol. 3, no. 1, pp. 1-7, 2006. doi: 10.1007/s11633-006-0001-4

Modelling and Multi-Objective Optimal Control of Batch Processes Using Recurrent Neuro-fuzzy Networks

1.
School of Chemical Engineering and Advanced Materials, University of Newcastle, Newcastle upon Tyne NE1 7RU, UK

Received: 2005-03-03

Fund Project:

This work was supported by the UK EPSRC (GR/N13319, GR/R10875).

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.
- Optimal control,
- batch processes,
- neural networks,
- multi-objective optimisation

References

[1]	D.Bonvin.Optimal operation of batch reactors:a personal view.Journal of Process Control,vol.8,no.5-6,pp.355-368,1998.
[2]	D.Ruppen,C.Benthack,D.Bonvin.Optimization of batch reactor operation under parametric uncertainty-computational aspects.Journal of Process Control,vol.5,no.4,pp.235-240,1995.
[3]	Y.Tian,J.Zhang,A.J.Morris.Modelling and optimal control of a batch polymerisation reactor using a hybrid sacked recurrent neural network model.Industrial & Engineering Chemistry Research,vol.40,no.21,pp.4525-4535,2001.
[4]	Y.Tian,J.Zhang,A.J.Morris.Optimal control of a batch emulsion copolymerisation reactor based on recurrent neural network models.Chemical Engineering and Processing,vol.41,no.6,pp.531-538,2002.
[5]	J.Zhang.A reliable neural network model based optimal control strategy for a batch polymerisation reactor.Industrial & Engineering Chemistry Research,vol.43,no.4,pp.1030-1038,2004.
[6]	Z.Xiong,J.Zhang.Modelling and optimal control of fedbatch processes using a novel control affine feedforward neural network.Neurocomputing,vol.61,pp.317-337,2004.
[7]	Z.Xiong,J.Zhang.Batch-to-batch iterative optimisation control based on recurrent neural network models.Journal of Process Control,vol.15,no.1,pp.11-21,2005.
[8]	J.Zhang,A.J.Morris.Recurrent neuro-fuzzy networks for nonlinear process modelling.IEEE Transactions on Neural Networks,vol.10,no.2,pp.313-326,1999.
[9]	T.Takagi,M.Sugeno.Fuzzy identification of systems and its application to modelling and control.IEEE Transactions on Systems Man and Cybernetics,vol.15,no.1,pp.116-132,1985.
[10]	T.A.Johansen,B.A.Foss.Constructing NARMAX models using ARMAX models.International Journal of Control,vol.58,no.5,pp.1125-153,1993.
[11]	L.X.Wang.Adaptive Fuzzy Systems and Control:Design and Stability Analysis.Prentice Hall,Englewood Cliffs,NJ,1994.
[12]	J.Zhang,A.J.Morris.Fuzzy neural networks for nonlinear systems modelling.IEE Proceedings,Control Theory and Applications,vol.142,no.6,pp.551-561,1995.
[13]	J.Zhang,A.J.Morris.Process modelling and fault diagnosis using fuzzy neural networks.Fuzzy Sets and Systems,vol.79,no.1,pp.127-140,1996.
[14]	D.E.Rumelhart,G.E.Hinton,R.J.Williams.Learning internal representations by error propagation.In:D.E.Rumelhart,J.L.McClelland.(Eds),Parallel Distributed Processing,MIT Press,pp.319-362,1986.
[15]	D.Marquardt.An algorithm for least squares estimation of nonlinear parameters.SIAM Journal on Applied Mathematics,vol.11,no.2,pp.431-441,1963.
[16]	P.J.Werbos.Backpropagation through time:what it does and how to do it.Proceedings of IEEE,vol.78,no.10,pp.1550-1560,1990.
[17]	P.Terwiesch,D.Ravemark,B.Schenker,D.W.T.Rippin.Semi-batch process optimization under uncertainty:Theory and experiments.Computers & Chemical Engineering,vol.22,no.1-2,pp.201-213,1998.
[18]	F.W.Gembicki.Vector Optimisation for Control with Performance and Parameter Sensitivity Indices.Ph.D.dissertation,Case Western Reserve University,Cleveland,USA,1974.

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通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

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Modelling and Multi-Objective Optimal Control of Batch Processes Using Recurrent Neuro-fuzzy Networks

1. School of Chemical Engineering and Advanced Materials, University of Newcastle, Newcastle upon Tyne NE1 7RU, UK

Received: 2005-03-03

Fund Project:

This work was supported by the UK EPSRC (GR/N13319, GR/R10875).

Key words:

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.

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Reference (18)

[1]	D.Bonvin.Optimal operation of batch reactors:a personal view.Journal of Process Control,vol.8,no.5-6,pp.355-368,1998.
[2]	D.Ruppen,C.Benthack,D.Bonvin.Optimization of batch reactor operation under parametric uncertainty-computational aspects.Journal of Process Control,vol.5,no.4,pp.235-240,1995.
[3]	Y.Tian,J.Zhang,A.J.Morris.Modelling and optimal control of a batch polymerisation reactor using a hybrid sacked recurrent neural network model.Industrial & Engineering Chemistry Research,vol.40,no.21,pp.4525-4535,2001.
[4]	Y.Tian,J.Zhang,A.J.Morris.Optimal control of a batch emulsion copolymerisation reactor based on recurrent neural network models.Chemical Engineering and Processing,vol.41,no.6,pp.531-538,2002.
[5]	J.Zhang.A reliable neural network model based optimal control strategy for a batch polymerisation reactor.Industrial & Engineering Chemistry Research,vol.43,no.4,pp.1030-1038,2004.
[6]	Z.Xiong,J.Zhang.Modelling and optimal control of fedbatch processes using a novel control affine feedforward neural network.Neurocomputing,vol.61,pp.317-337,2004.
[7]	Z.Xiong,J.Zhang.Batch-to-batch iterative optimisation control based on recurrent neural network models.Journal of Process Control,vol.15,no.1,pp.11-21,2005.
[8]	J.Zhang,A.J.Morris.Recurrent neuro-fuzzy networks for nonlinear process modelling.IEEE Transactions on Neural Networks,vol.10,no.2,pp.313-326,1999.
[9]	T.Takagi,M.Sugeno.Fuzzy identification of systems and its application to modelling and control.IEEE Transactions on Systems Man and Cybernetics,vol.15,no.1,pp.116-132,1985.
[10]	T.A.Johansen,B.A.Foss.Constructing NARMAX models using ARMAX models.International Journal of Control,vol.58,no.5,pp.1125-153,1993.
[11]	L.X.Wang.Adaptive Fuzzy Systems and Control:Design and Stability Analysis.Prentice Hall,Englewood Cliffs,NJ,1994.
[12]	J.Zhang,A.J.Morris.Fuzzy neural networks for nonlinear systems modelling.IEE Proceedings,Control Theory and Applications,vol.142,no.6,pp.551-561,1995.
[13]	J.Zhang,A.J.Morris.Process modelling and fault diagnosis using fuzzy neural networks.Fuzzy Sets and Systems,vol.79,no.1,pp.127-140,1996.
[14]	D.E.Rumelhart,G.E.Hinton,R.J.Williams.Learning internal representations by error propagation.In:D.E.Rumelhart,J.L.McClelland.(Eds),Parallel Distributed Processing,MIT Press,pp.319-362,1986.
[15]	D.Marquardt.An algorithm for least squares estimation of nonlinear parameters.SIAM Journal on Applied Mathematics,vol.11,no.2,pp.431-441,1963.
[16]	P.J.Werbos.Backpropagation through time:what it does and how to do it.Proceedings of IEEE,vol.78,no.10,pp.1550-1560,1990.
[17]	P.Terwiesch,D.Ravemark,B.Schenker,D.W.T.Rippin.Semi-batch process optimization under uncertainty:Theory and experiments.Computers & Chemical Engineering,vol.22,no.1-2,pp.201-213,1998.
[18]	F.W.Gembicki.Vector Optimisation for Control with Performance and Parameter Sensitivity Indices.Ph.D.dissertation,Case Western Reserve University,Cleveland,USA,1974.

Modelling and Multi-Objective Optimal Control of Batch Processes Using Recurrent Neuro-fuzzy Networks

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