Long Range Predictive Control of Nonlinear Processes Based on Recurrent Neuro-Fuzzy Network Models |
| |
Authors: | J Zhang AJ Morris |
| |
Affiliation: | (1) Centre for Process Analytics and Control Technology, Department of Chemical & Process Engineering, University of Newcastle, Newcastle upon Tyne, UK, GB |
| |
Abstract: | 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. |
| |
Keywords: | : Fuzzy models Long range prediction Model-based predictive control Neural networks Neuro-fuzzy networks Process control |
本文献已被 SpringerLink 等数据库收录! |
|