Iterative learning control of robotic manipulators by hybrid adaptation schemes

Abstract

Iterative learning control (ILC) schemes have been one of the useful methods to achieve tracking control for uncertain processes with less prior information. Those generate desired control inputs for tracking through repetitions of the same tasks on the finite time interval, and have been applied to the control of various processes which execute the same operations over and over again. This paper provides an alternative approach to solve ILC of robotic manipulators by introducing hybrid adaptation schemes. The hybrid adaptation schemes are adaptive control structures which involve continuous-time control of processes and discrete-time updates of tuning parameters simultaneously. The main advantage of the proposed methodology is that the reference signals to be followed and the time intervals on which each operation is defined, are not necessarily identical to the ones in the other operations. Those peculiar features are owing to parameter estimation schemes included in the proposed methods.