Neural model adaptation and predictive control of a chemical process rig

An adaptation algorithm for Gaussian radial basis function (RBF) network models is proposed. The model structure is adapted to cope with operating region change, while the weight parameters are updated to model time varying dynamics or uncertainties. The special feature is that the modeling accuracy is maintained during adaptation and, therefore, the control performance will not be degraded when the model structure changes. A localized forgetting method is proposed to deal with nonlinearities in different operating regions, and is implemented with the recursive orthogonal least squares (ROLS) training algorithm. The developed adaptive model is evaluated by real data modeling of a three-input three-output chemical process rig. Online model predictive control (MPC) of the rig is also conducted. Improved tracking performance with the adaptive model is demonstrated in comparison with nonadaptive model-based control and decentralized propotional-integral-differential (PID) control.