Iterative learning control for optimal multiple-point tracking

This paper presents a new optimization-based iterative learning control (ILC) framework for multiple-point tracking control. Conventionally, one demand prior to designing ILC algorithms for such problems is to build a reference trajectory that passes through all given points at given times. In this paper, we produce output curves that pass close to the desired points without considering the reference trajectory. Here, the control signals are generated by solving an optimal ILC problem with respect to the points. As such, the whole process becomes simpler; key advantages include significantly decreasing the computational cost and improving performance. Our work is then examined in both continuous and discrete systems.