Simulation of an iterative learning control system for fed-batch cell culture processes.

This paper describes an iterative learning control scheme for fed-batch operation where repetitive trajectory tracking tasks are required. The proposed learning strategy is model-independent, and it takes advantage of the repetitive feature of system operations with a certain degree of intelligence and requires only small size of dynamic database for the learning process. The convergence of the learning process is proven. An example of simultaneously tracking two predefined trajectories by iterative learning control with two control inputs is given to illustrate the methodology. Satisfactory performance of the learning system can be observed from the simulation results.