A novel real-time non-linear wavelet-based model predictive controller for a coupled tank system. (July 2014)

• Record Type: Journal Article
• Authors: Owa, Kayode; Sharma, Sanjay; Sutton, Robert
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 228:Number 6(2014)
• Page Start: 419
• Page End: 432
• Publication Date: 2014-07
• Subjects: Wavelet neural network -- artificial neural network -- modelling -- system identification -- non-linear model predictive control -- real-time practical implementation -- genetic algorithms -- single input single output -- non-linear optimisation -- coupled tank system -- Simulink model; Mechanical engineering -- Periodicals; Automatic control -- Periodicals; Systems engineering -- Periodicals; 621.3
• DOI: 10.1177/0959651813520149
• Languages: English
• ISSNs: 0959-6518

Abstract

This article presents the design, simulation and real-time implementation of a constrained non-linear model predictive controller for a coupled tank system. A novel wavelet-based function neural network model and a genetic algorithm online non-linear real-time optimisation approach were used in the non-linear model predictive controller strategy. A coupled tank system, which resembles operations in many chemical processes, is complex and has inherent non-linearity, and hence, controlling such system is a challenging task. Particularly important is low-level control where often instability and oscillatory responses are observed. This article designs a wavelet neural network with high predicting precision and time–frequency localisation characteristics for an online prediction model in the non-linear model predictive controller to show the effectiveness of this approach in controlling the liquid at low level. To speed up the training process, a fast global search stochastic non-linear conjugate wavelet gradient algorithm is initially used to train the wavelet neural network structure before the genetic algorithm optimisation technique is utilised to tune adaptively the wavelet neural network parameters. The non-linear model predictive controller algorithm is tested for both approaches: first, in a simulation using identified models, and second, in a real-time practical application to a single-input single-output system coupled tank system. The results show an excellent control performance with respect to mean square error and average control energy values obtained.