Robust Predictive and Iterative Learning Control as Applied to Batch Process

Batch processes can be nonlinear, are prone to repetitive and non-repetitive disturbances, and often have input and output constraints. Iterative learning and predictive control are used to design a controller that makes system outputs track a given set-point profile. Now, despite non-linearities, disturbances, and constraints, batch reactors can satisfy their production requirements. Iterative learning control systems contain two variables, time and batch, and so can be regarded, in essence, as two-dimensional systems. A Lyapunov function is used to stabilize these systems and LMIs (linear matrix inequalities) are used to optimize their control actions. A continuous stirred-tank reactor (CSTR) model, tracking a given reference temperature trajectory, isutilized to illustrate the effectiveness of the proposed control method.