Stable receding horizon control basedonrecurrent networks
C. Kambhampati, A. Delgado, J.D. Mason, Kevin Warwick · IEE Proceedings - Control Theory and Applications · 1997
The last decade has seen the re-emergence of artificial neural networks as an alternative to traditional modelling techniques for the control of nonlinear systems. Numerous control schemes have been proposed and have been shown to work in simulations. However, very few analyses have been made of the working of these networks. The authors show that a receding horizon control strategy based on a class of recurrent networks can stabilise nonlinear systems.