@ARTICLE{Yovchev_Kaloyan_Constrained_2020,
 author={Yovchev, Kaloyan and Delchev, Kamen and Krastev, Evgeniy},
 volume={vol. 30},
 number={No 1},
 journal={Archives of Control Sciences},
 pages={157-176},
 howpublished={online},
 year={2020},
 publisher={Committee of Automatic Control and Robotics PAS},
 abstract={Iterative Learning Control (ILC) is a well-known method for control of systems performing repetitive jobs with high precision. This paper presents Constrained Output ILC (COILC) for non-linear state space constrained systems. In the existing literature there is no general solution for applying ILC to such systems. This novel method is based on the Bounded Error Algorithm (BEA) and resolves the transient growth error problem, which is a major obstacle in applying ILC to non-linear systems. Another advantage of COILC is that this method can be applied to constrained output systems. Unlike other ILC methods the COILC method employs an algorithm that stops the iteration before the occurrence of a violation in any of the state space constraints. This way COILC resolves both the hard constraints in the non-linear state space and the transient growth problem. The convergence of the proposed numerical procedure is proved in this paper. The performance of the method is evaluated through a computer simulation and the obtained results are compared to the BEA method for controlling non-linear systems. The numerical experiments demonstrate that COILC is more computationally effective and provides better overall performance. The robustness and convergence of the method make it suitable for solving constrained state space problems of non-linear systems in robotics.},
 type={Article},
 title={Constrained Output Iterative Learning Control},
 URL={http://czasopisma.pan.pl/Content/115853/PDF/ACS-2020-1-8.pdf},
 doi={10.24425/acs.2020.132590},
 keywords={constrained output systems, convergence analysis, iterative learning control, robot manipulators},
}