Robust Model Predictive Iterative Learning Control for Iteration-Varying-Reference Batch Processes

Abstract

Model predictive iterative learning control (MPILC) is a popular approach to control batch systems with repetitive nature, as it is capable of tracking the plant reference trajectory with high accuracy and guaranteed closed-loop stability. However, varying reference trajectory often happens in batch processes, so that the tracking performance of a general MPILC can be deteriorated. With the reference variation being treated as bounded disturbance, this paper incorporates H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> control with MPILC to constitute a robust MPILC (RMPILC), so as to restrain the fluctuation of tracking error caused by the varying reference. This RMPILC adopts the linear parameter varying (LPV) model to represent the dynamic property of the nonlinear system. It then solves the input vector of controlled system by optimizing the tracking performance objective function with constraints in the form of linear matrix inequalities. The robust stability and convergence condition of RMPILC are analyzed. The effectiveness of proposed algorithm is verified thorough the simulations on a numerical example, and also a continuous stirred tank reactor (CSTR) system.