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∞ 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.