Extended robust iterative learning control design for industrial batch processes with uncertain perturbations

Abstract

For industrial batch processes subject to uncertain perturbations from cycle to cycle, a robust iterative learning control (ILC) scheme is proposed in this paper to realize robust tracking of the set-point profile for system operation. An important merit is that only measured output errors of current and previous cycles are used to design a synthetic ILC controller consisting of dynamic output feedback plus feedforward control, for the convenience of implementation. By introducing a slack variable matrix to construct a less comprehensive two-dimensional (2D) difference Lyapunov function that guarantees monotonical state energy decrease in both the time and batchwise directions, sufficient conditions are established in terms of linear matrix inequality (LMI) constraints for holding robust stability of the closed-loop ILC system. By solving these LMI constraints, the ILC controller is explicitly formulated, together with an adjustable robust H infinity performance level. An illustrative example of injection molding is given to demonstrate the effectiveness and merits of the proposed ILC design.