A linear input/output data-based predictive control with integral action property

Abstract

A linear input/output data-based predictive control with integral action is developed. The control input is obtained directly from the input/output data in a single step using subspace identification method. However, the state estimation in subspace identification gives a biased estimate and there exists a possibility of model mismatch when the controller is applied to a nonlinear process To overcome such difficulties, we add integral action to the controller by augmenting the integrated white noise disturbance model and use each of best linear unbiased estimation (BLUE) filter and Kalman filter as a stochastic observer for the unmeasured disturbance. When applied to a continuous styrene polymerization reactor, the proposed controller demonstrates an improved control performance.