Dedicated to

In this thesis, we present new iterative learning control (ILC) frameworks for multiple points tracking problems. Conventionally, one demand prior to designing ILC systems for such problems is to plan a reference trajectory that passes through all given points at given times. After that, an ILC controller is generated to follow this trajectory in the iteration domain. These works do not follow that direction. First, we present an algorithm in which not only control signal but also reference trajectory is updated at each trial. The scheme investigates the relationship between reference trajectory and ILC tracking control for im-proving system performance, particularly rate of convergence. Moreover, we show that it is possible to make benefits from cooperating between trajectory planning and tracking control stages. Second, a new ILC scheme is proposed to 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. Other contributions include multiple points ILC with input constraints and disturbances, uncertainties in data points which happen both repetitively and non-repetitively. Our works are then examined in numerical examples and applications. c©201