Iterative learning control of robotic manipulators by hybrid adaptation schemes: Gradient and least squares hybrid adaptive laws

Abstract

This paper provides an alternative approach to solve iterative learning control (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. Two hybrid adaptation schemes are provided, and convergence properties of those are compared in the simulation studies.