caa a22 4500 475833910 CHVBK 20180406123848.0 cr unu---uuuuu 170329e20000501xx s 000 0 eng 10.1007/s005210070035 doi (NATIONALLICENCE)springer-10.1007/s005210070035 Long Range Predictive Control of Nonlinear Processes Based on Recurrent Neuro-Fuzzy Network Models [Elektronische Daten] [J. Zhang, A.J. Morris] A recurrent neuro-fuzzy network-based nonlinear long range model predictive control strategy is proposed in this paper. The process operation is partitioned into several fuzzy operating regions. Within each region, a local linear model is used to model the process. The global model output is obtained through the centre of gravity defuzzification. Based upon a neuro-fuzzy network model, a nonlinear model-based predictive controller can be developed by combining several local linear model-based predictive controllers which usually have analytical solutions. This strategy avoids the time consuming numerical optimisation procedure, and the uncertainty in convergence to the global optimum which are typically seen in conventional nonlinear model-based predictive control strategies. Furthermore, control actions obtained based on local incremental models contain integration actions which can nat-urally eliminate static control offsets. The technique is demonstrated by an application to the modelling and control of liquid level in a water tank.: Springer-Verlag London Limited, 2000 Keywords: Fuzzy models nationallicence Long range prediction nationallicence Model-based predictive control nationallicence Neural networks nationallicence Neuro-fuzzy networks nationallicence Process control nationallicence Zhang J. Centre for Process Analytics and Control Technology, Department of Chemical & Process Engineering, University of Newcastle, Newcastle upon Tyne, UK, GB aut Morris A.J. Centre for Process Analytics and Control Technology, Department of Chemical & Process Engineering, University of Newcastle, Newcastle upon Tyne, UK, GB aut https://doi.org/10.1007/s005210070035 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s005210070035 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zhang J. Centre for Process Analytics and Control Technology, Department of Chemical & Process Engineering, University of Newcastle, Newcastle upon Tyne, UK, GB aut NATIONALLICENCE 700 1- Morris A.J. Centre for Process Analytics and Control Technology, Department of Chemical & Process Engineering, University of Newcastle, Newcastle upon Tyne, UK, GB aut Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer