Title: A non-minimum phase robust nonlinear neuro-wavelet predictive control strategy for a quadruple tank process
Authors: Kayode Owa; Asiya Khan; Sanjay Sharma; Robert Sutton
Addresses: Automated Scheduling Optimisation and Planning (ASAP) Research Group, University of Nottingham, UK ' Autonomous Marine Systems (AMS) Research Group, University of Plymouth, UK ' Autonomous Marine Systems (AMS) Research Group, University of Plymouth, UK ' Autonomous Marine Systems (AMS) Research Group, University of Plymouth, UK
Abstract: In process industries model-plant mismatch is a significant problem. Quadruple tank process (QTP) can be configured both in minimum phase and non-minimum phase (NMP). However, in NMP, the control of QTP poses a challenge. This paper addresses that and presents a novel robust wavelet based non-minimum phase control (NMPC) strategy for the challenging QTP using genetic algorithm to find the optimised value of the manipulated variables in NMPC at every sampling time. The QTP is modelled based on wavelet neural network. The simulation results indicate that significant improvements have been achieved both in modelling and control strategies for a QTP system compare to conventional approaches such as the Levenberg-Marquardt.
Keywords: wavelet neural network; WNN; right hand plane zero; RHPZ; non-minimum-phase; NMP; nonlinear model predictive control; NMPC; quadruple-tank process; QTP; genetic algorithms; GA; multi input multi output; MIMO; model-plant mismatch; MPM; nonlinear optimisation; coupled tank system; CTS.
International Journal of Process Systems Engineering, 2018 Vol.4 No.4, pp.207 - 229
Available online: 16 Jul 2018 *Full-text access for editors Access for subscribers Purchase this article Comment on this article