A linear parameter-varying radial basis function model and predictive control of a chemical reactor

Abstract

A linear parameter-varying model (LPVM) is developed for nonlinear dynamic systems using a radial basis function (RBF) neural network. The training of the LPVM is formulated as the least-squares problem and the recursive orthogonal least-squares algorithm is applied. Model adaptation is also developed with a localized forgetting method for on-line weight updating. The LPVM-based model predictive control (MPC) is developed and the convexity of the optimization is preserved. The developed LPVM is applied to a laboratory-scaled chemical reactor rig. The real data modelling and on-line control implementation are presented. Decentralized proportional–integral–derivative (PID) control is also designed and implemented for the reactor for comparison. The superiority of the tracking performance by the LPVM-based MPC over that by PID control is clearly demonstrated. The performance using the fixed LPVM is also improved by using the adaptive model.