Operational/Task Space Learning Control of Robot Manipulators with Dynamical Uncertainties
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In this work, we consider the problem of operational/task space tracking control of a robot manipulator where a periodic desired end-effector pose is to be tracked. Specifically, we designed a repetitive learning controller that guarantees asymptotic end-effector tracking of periodic trajectories (with known period) while "learning" the overall uncertainties in the system dynamics. The proposed controller does not make use of the inverse kinematic formulation on the position level and the stability of the closed-loop system is guaranteed via Lyapunov based arguments. Numerical studies are conducted on a two link planar robot are presented to illustrate the performance and viability of the proposed method.