High-order internal model-based iterative learning control design for second-order hyperbolic distributed parameter systems

Aiming at a class of linear second-order hyperbolic distributed parameter systems (DPSs), we consider a P-type iterative learning control (ILC) scheme based on high-order internal models (HOIMs) to track the iteration-varying reference trajectories, where reference trajectories in the iteration axis are generated by a HOIM formula. By virtue of contraction mapping principle, it is proved that the actual output trajectory of the system can realize the perfect tracking of the expected trajectory on L² space along the iteration axis. Finally, numerical simulation cases verify the validity of the designed algorithm.