Control relevant identification of a pH waste water neutralisation process using adaptive radial basis function networks

Abstract

An adaptive radial basis function network is developed for non-linear and time-varying processes based on control-relevant-identification of a pH waste water neutralisation processes. Most adaptive control techniques rely on fixed model structures and variable parameter estimates but these often lack the ability to control time-varying systems. A model structure and parameter updating algorithm is applied for the adaptive training of a radial basis function network (RBFN), to create an adaptive RBFN. Both the network structure (centres of radial basis function) and the related parameters (weights of centres) are updated on-line using an exponential window. Candidate centres are generated and eliminated on-line according to the operation of the system. The selection of centres is based on the contribution these candidate centres make to the output at each selection step, so that the network can track changes in both the system structure and parameters. Suitability of the identification schemes for model predictive control is demonstrated by means of a multiple step ahead prediction of a simulated, noise corrupted, pH neutralisation process.