Gramian-Based Data-Driven ILC for Continuous-Time Systems

We present a data-driven Iterative Learning Control (ILC) scheme for continuous-time systems using a ‘Gramian’ approach. We present some numerical experiments using Chebyshev Polynomial Orthogonal Bases (CPOB) in both model-driven and data-driven ILC for continuous-time systems. We show that in the model-driven ILC case, the utilisation of a CPOB framework results in improved performance over discrete-time methods for applications requiring high precision. In the data-driven case, the advantages of a CPOB approach are less evident and we discuss some of the open problems being investigated.