LAUSR

For the existing adaptive robot control methods considering kinematic and dynamic uncertainties, the strict persistent excitation is necessary for parameter convergence. To alleviate this stringent constraint and improve identification and tracking capabilities, a composite learning control strategy is proposed for task space trajectory tracking. First a task space control structure with separate kinematics and dynamics is designed, then a composite learning technique is introduced to the parameter identification process. The asymptotical stability is proved using Lyapunov methods. Besides, the exponentially converge of kinematic and dynamic estimation errors as well as the exponential trajectory tracking is guaranteed when a weak interval excitation condition holds. Simulation results on a planar robot model show the strategy’s effectiveness.