A Wavelet Neural Network Based Non-linear Model Predictive Controller for a Multi-variable Coupled Tank System

Kayode Owa Sanjay Sharma Robert Sutton

Kayode Owa, Sanjay Sharma, Robert Sutton. A Wavelet Neural Network Based Non-linear Model Predictive Controller for a Multi-variable Coupled Tank System[J]. 国际自动化与计算杂志(英)/International Journal of Automation and Computing, 2015, 12(2): 156-170. doi: 10.1007/s11633-014-0825-2
引用本文: Kayode Owa, Sanjay Sharma, Robert Sutton. A Wavelet Neural Network Based Non-linear Model Predictive Controller for a Multi-variable Coupled Tank System[J]. 国际自动化与计算杂志(英)/International Journal of Automation and Computing, 2015, 12(2): 156-170. doi: 10.1007/s11633-014-0825-2
Kayode Owa, Sanjay Sharma and Robert Sutton. A Wavelet Neural Network Based Non-linear Model Predictive Controller for a Multi-variable Coupled Tank System. International Journal of Automation and Computing, vol. 12, no. 2, pp. 156-170, 2015 doi:  10.1007/s11633-014-0825-2
Citation: Kayode Owa, Sanjay Sharma and Robert Sutton. A Wavelet Neural Network Based Non-linear Model Predictive Controller for a Multi-variable Coupled Tank System. International Journal of Automation and Computing, vol. 12, no. 2, pp. 156-170, 2015 doi:  10.1007/s11633-014-0825-2

A Wavelet Neural Network Based Non-linear Model Predictive Controller for a Multi-variable Coupled Tank System

doi: 10.1007/s11633-014-0825-2
基金项目: 

This work was supported by Petroleum Training Development Fund, Nigeria.

详细信息
    作者简介:

    Sanjay Sharma is a lecturer in the School of Marine Science and Engineering and head of the Marine and Industrial Dy-namic Analysis (MIDAS) Research Group. He worked as a signal and telecommunica-tion engineer in Indian Railways for four and a half years and was involved with route relay and solid state interlocking de-sign projects. Prior to joining Plymouth University in 2004, he held a position as a research engineer in the Intelligent Systems and Control Research Group at Queen University of Belfast, UK. His research interests include the application of soft comput-ing techniques in optimisation, fault diagnosis and parallel com-puting for neural and local model network training, evolutionary approaches to multiple-modelling and control of non-linear in-dustrial and marine systems. E-mail: sanjay.sharma@plymouth.ac.uk;Robert Sutton holds the degrees of B. Eng. (Tech.) in engineering production, and M. Eng. and Ph. D. degrees in control engineering from the University of Wales, UK. Prior to joining the Royal Navy as a commissioned officer in 1976, he served general engineering and student apprentice-ships with Firth Cleveland Fastenings Lim-ited, Pontypridd, UK, followed by a period as a research student in the Industrial Engineering Unit, UWIST, Cardiff. On completion of his service in 1992 in the rank of Lieu-tenant Commander Royal Navy, he took up an appointment at the University of Plymouth. Until recently, he was a head of School and is currently professor of control systems engineering in the School of Marine Science and Engineering. He is also a member of the Marine and Industrial Dynamic Analysis Research Group within the University. In addition, he is a member of the International Federation of Automatic Control Technical Committee on Marine Systems and the Society for Underwater Technology Underwater Robotics Group Committee. He is the author/co-author of over 180 book, journal and conference publications. On six occasions, he has been the recipient of premier awards from institutions for the most outstanding technical paper appearing in their journal for a given year. His research interests include the application of advanced con-trol engineering and artificial intelligence techniques to control problems. Ongoing research is concerned with the use of fuzzy logic, neurofuzzy algorithms, artificial neural networks and adap-tive search algorithms in the design of novel control systems for industrial and marine plant. E-mail: R.Sutton@plymouth.ac.uk

A Wavelet Neural Network Based Non-linear Model Predictive Controller for a Multi-variable Coupled Tank System

Funds: 

This work was supported by Petroleum Training Development Fund, Nigeria.

  • 摘要: In this paper, a novel real time non-linear model predictive controller (NMPC) for a multi-variable coupled tank system (CTS) is designed. CTSs are highly non-linear and can be found in many industrial process applications. The involvement of multi-input multi-output (MIMO) system makes the design of an effective controller a challenging task. MIMO systems have inherent couplings, interactions in-between the process input-output variables and generally have an complex internal structure. The aim of this paper is to design, simulate, and implement a novel real time constrained NMPC for a multi-variable CTS with the aid of intelligent system techniques. There are two major formidable challenges hindering the success of the implementation of a NMPC strategy in the MIMO case. The first is the difficulty of obtaining a good non-linear model by training a non-convex complex network to avoid being trapped in a local minimum solution. The second is the online real time optimisation (RTO) of the manipulated variable at every sampling time. A novel wavelet neural network (WNN) with high predicting precision and time-frequency localisation characteristic was selected for an MIMO model and a fast stochastic wavelet gradient algorithm was used for initial training of the network. Furthermore, a genetic algorithm was used to obtain the optimised parameters of the WNN as well as the RTO during the NMPC strategy. The proposed strategy performed well in both simulation and real time on an MIMO CTS. The results indicated that WNN provided better trajectory regulation with less mean-squared-error and average control energy compared to an artificial neural network. It is also shown that the WNN is more robust during abnormal operating conditions.
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A Wavelet Neural Network Based Non-linear Model Predictive Controller for a Multi-variable Coupled Tank System

doi: 10.1007/s11633-014-0825-2
    基金项目:

    This work was supported by Petroleum Training Development Fund, Nigeria.

    作者简介:

    Sanjay Sharma is a lecturer in the School of Marine Science and Engineering and head of the Marine and Industrial Dy-namic Analysis (MIDAS) Research Group. He worked as a signal and telecommunica-tion engineer in Indian Railways for four and a half years and was involved with route relay and solid state interlocking de-sign projects. Prior to joining Plymouth University in 2004, he held a position as a research engineer in the Intelligent Systems and Control Research Group at Queen University of Belfast, UK. His research interests include the application of soft comput-ing techniques in optimisation, fault diagnosis and parallel com-puting for neural and local model network training, evolutionary approaches to multiple-modelling and control of non-linear in-dustrial and marine systems. E-mail: sanjay.sharma@plymouth.ac.uk;Robert Sutton holds the degrees of B. Eng. (Tech.) in engineering production, and M. Eng. and Ph. D. degrees in control engineering from the University of Wales, UK. Prior to joining the Royal Navy as a commissioned officer in 1976, he served general engineering and student apprentice-ships with Firth Cleveland Fastenings Lim-ited, Pontypridd, UK, followed by a period as a research student in the Industrial Engineering Unit, UWIST, Cardiff. On completion of his service in 1992 in the rank of Lieu-tenant Commander Royal Navy, he took up an appointment at the University of Plymouth. Until recently, he was a head of School and is currently professor of control systems engineering in the School of Marine Science and Engineering. He is also a member of the Marine and Industrial Dynamic Analysis Research Group within the University. In addition, he is a member of the International Federation of Automatic Control Technical Committee on Marine Systems and the Society for Underwater Technology Underwater Robotics Group Committee. He is the author/co-author of over 180 book, journal and conference publications. On six occasions, he has been the recipient of premier awards from institutions for the most outstanding technical paper appearing in their journal for a given year. His research interests include the application of advanced con-trol engineering and artificial intelligence techniques to control problems. Ongoing research is concerned with the use of fuzzy logic, neurofuzzy algorithms, artificial neural networks and adap-tive search algorithms in the design of novel control systems for industrial and marine plant. E-mail: R.Sutton@plymouth.ac.uk

摘要: In this paper, a novel real time non-linear model predictive controller (NMPC) for a multi-variable coupled tank system (CTS) is designed. CTSs are highly non-linear and can be found in many industrial process applications. The involvement of multi-input multi-output (MIMO) system makes the design of an effective controller a challenging task. MIMO systems have inherent couplings, interactions in-between the process input-output variables and generally have an complex internal structure. The aim of this paper is to design, simulate, and implement a novel real time constrained NMPC for a multi-variable CTS with the aid of intelligent system techniques. There are two major formidable challenges hindering the success of the implementation of a NMPC strategy in the MIMO case. The first is the difficulty of obtaining a good non-linear model by training a non-convex complex network to avoid being trapped in a local minimum solution. The second is the online real time optimisation (RTO) of the manipulated variable at every sampling time. A novel wavelet neural network (WNN) with high predicting precision and time-frequency localisation characteristic was selected for an MIMO model and a fast stochastic wavelet gradient algorithm was used for initial training of the network. Furthermore, a genetic algorithm was used to obtain the optimised parameters of the WNN as well as the RTO during the NMPC strategy. The proposed strategy performed well in both simulation and real time on an MIMO CTS. The results indicated that WNN provided better trajectory regulation with less mean-squared-error and average control energy compared to an artificial neural network. It is also shown that the WNN is more robust during abnormal operating conditions.

English Abstract

Kayode Owa, Sanjay Sharma, Robert Sutton. A Wavelet Neural Network Based Non-linear Model Predictive Controller for a Multi-variable Coupled Tank System[J]. 国际自动化与计算杂志(英)/International Journal of Automation and Computing, 2015, 12(2): 156-170. doi: 10.1007/s11633-014-0825-2
引用本文: Kayode Owa, Sanjay Sharma, Robert Sutton. A Wavelet Neural Network Based Non-linear Model Predictive Controller for a Multi-variable Coupled Tank System[J]. 国际自动化与计算杂志(英)/International Journal of Automation and Computing, 2015, 12(2): 156-170. doi: 10.1007/s11633-014-0825-2
Kayode Owa, Sanjay Sharma and Robert Sutton. A Wavelet Neural Network Based Non-linear Model Predictive Controller for a Multi-variable Coupled Tank System. International Journal of Automation and Computing, vol. 12, no. 2, pp. 156-170, 2015 doi:  10.1007/s11633-014-0825-2
Citation: Kayode Owa, Sanjay Sharma and Robert Sutton. A Wavelet Neural Network Based Non-linear Model Predictive Controller for a Multi-variable Coupled Tank System. International Journal of Automation and Computing, vol. 12, no. 2, pp. 156-170, 2015 doi:  10.1007/s11633-014-0825-2
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