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. https://doi.org/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. https://doi.org/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

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

  • Received Date: 2013-11-17
  • Rev Recd Date: 2014-01-14
  • Publish Date: 2015-04-01
  • 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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