On the Control of 2-D Systems by Intelligent Schemes

This paper is concerned with the problem of fuzzy iterative learning control for two-dimensional (2-D) state systems described by the Fornasini and Marchesini (FM) second state-space model. This paper gives iterative learning control (ILC) techniques, used to improve tracking control performance in 2-D processes. The fundamental concepts of the various ILC algorithms are presented, with a particular focus on a fuzzy-based algorithm called Fuzzy-ILC (FILC). The study indicates that one can solve a seemingly very difficult multivariable nonlinear tracking problem with relative ease by intelligently combining the ILC technique with fuzzy logic systems. The results of simulations show that our proposed FILC can reduce the trajectory error in far less number of iterations. This is due to the fact that FILC is providing a nonlinear mapping while all our others presented schemes is linear.