A learning controller for robot manipulators using Fourier series

We proposed a new learning controller for decentralized tracking control of nonlinear robot manipulators. When the desired trajectory of each subsystem of the robot lasts for a finite duration, it can be approximated by a Fourier series with constant harmonic magnitudes. For each subsystem of the robot, a learning controller is designed to individually control each harmonic component of the actual output, although it is cross-related to other components in nonlinear systems. The learning algorithm is designed such that each harmonic magnitude of the actual output converges to that of the desired trajectory within the system bandwidth. Since this decentralized learning controller is designed in Fourier space instead of time domain, the system's time-delay could be easily compensated. This learning controller is only based on the local input and output information; no a priori structure or parameters of the system model are required. The experimental results on a 3-DOF direct-drive robot are presented.