Abstract
This paper presents the design of a novel nonlinear model predictive control (NMPC) strategy using a stochastic genetic algorithm (GA) to control highly nonlinear, uncertain and complex multivariable process with significant cross coupling effects between the process input and output variables. Raw multi-input and multi-output (MIMO) data from an experimental setup were collected and analysed. Both a GA and a backpropagation gradient descent based approach known as Levenberg-Marquardt Algorithm (LMA) are employed to train artificial neural network (ANN) nonlinear model. Real time practical experimental implementation on a MIMO coupled tank system is performed and the results show the effectiveness of the strategy. The approach can easily be adapted to other industrial processes. (6 pages)
Published Version
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