Robust nonlinear H∞ control of hyperbolic distributed parameter systems

A radial basis function (RBF) neural network model is developed for the identification of hyperbolic distributed parameter systems (DPSs). The empirical model is based only on process input-output data and is used for the estimation of the controlled variables at multiple spatial locations. The produced nonlinear model is transformed to a nonlinear state-space formulation, which in turn is used for deriving a robust H-infinity control law. The proposed methodology is applied to a long duct for the flow-based control of temperature distribution. The performance of the proposed method is illustrated by comparing it with conventional control strategies. (C) 2008 Elsevier Ltd. All rights reserved.