Decentralized learning control for robot manipulators

A new decentralized learning control scheme for tracking control of robot manipulators is presented in this paper. In this scheme, each joint is considered as a subsystem and controlled independently. The interactions from other subsystems are treated as deterministic uncertainties. The desired trajectory and the output of each subsystem are reparametrized by Fourier series (FS). A learning controller designed in Fourier space regulates each harmonic component individually by forcing Fourier coefficients (FCs) of the actual output approach to the corresponding FCs of the desired trajectory which are known constants. Since the information of the system phase-delay is included in FCs, the time-delay of the system can be easily compensated. The learning controller only uses the input and output information of the subsystem, no a priori knowledge about the system models is required. The asymptotic convergence of the tracking is proved in the paper. The experimental results on 3-DOF direct-drive robot show that convergent rate is faster compared with many other learning controllers and the performance of the closed-loop system is dramatically improved