A novel optimal terminal iterative learning control approach for linear time-varying systems

This work explores a novel optimal terminal ILC approach to generate a control signal only from the information of the terminal point rather than the whole trajectory. The presented scheme is data-driven using the measured I/O data without knowing any modeling information of the plant. Compared with traditional optimal ILCs, the Markov parameters of the system can be iteratively estimated, which is used as a key part of the control law´s learning gain. The stability and convergence is shown in the iteration domain by selecting suitable parameters.