Abstract
Industrial processes are naturally multivariable in nature, which exhibit non-linear behaviour and complex dynamic properties. The multivariable four-tank system has attracted recent attention as it exhibits elegantly complex dynamics, which includes interaction, transmission zero, and non-minimum phase characteristics that emerge from a simple cascade of tanks. In this paper, a neural model predictive controller (NMPC) is compared with a decentralized PI controller designed for a four-tank system. The approach adopts the approximation capabilities of a recurrent Elman network for modelling purposes. The learning process is performed with a modification of a back propagation through time (BPTT) algorithm. The algorithms are simulated using MATLAB and the performances of NMPC and decentralized PI control are compared based on integral square error (ISE) values for set point tracking and load changes. The performance comparison shows that NMPC is a suitable controller for four-tank system.
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