RECURRENT NEURAL NETWORKS IN DECOUPLING CONTROL OF MULTIVARIABLE NONLINEAR SYSTEMS
Michael Nikolaou, Vijaykumar Hanagandi · Chemical Engineering Communications · 1995
In this work we focus on the synergy between modeling with RNNs, and nonlinear controller design for decoupling control. The thesis of the paper is that recurrent neural networks (RNNs) can be conveniently used in an integrated black-box modeling and controller design methodology for decoupling control of multivariable nonlinear systems. A simulation example on a multivariable continuous-stirred-tank-reactor (CSTR) is provided to elucidate related issues. The effects of modeling uncertainty and state reconstruction on decoupling performance are specifically discussed.