An adaptive iterative learning control approach based on disturbance estimation for manipulator system

Abstract

An adaptive iterative learning control approach based on disturbance estimation has been developed for trajectory tracking of manipulators with uncertain parameters and external disturbances. The external disturbances are estimated by the feedback iterative learning method, whereas the uncertain parameters are compensated by adaptive control. This approach which is based on the disturbance estimation technique provides a rapid convergence of trajectory tracking errors. According to the Lyapunov theory, the sufficient condition of the asymptotic stability has been developed for the 2-degrees of freedom (DOFs) manipulator system. The numerical results show that the adaptive iterative learning control approach based on disturbance estimation is feasible and effective for the 2-DOFs manipulator. A comparison of the adaptive iterative learning control method and the iterative learning control method is completed, which shows that the adaptive iterative learning control method performs a faster convergence of the disturbance to the steady state.

Adaptive Fractional-Order Non-Singular Fast Terminal Sliding Mode Control Based on Fixed Time Disturbance Observer for Manipulators

“…In recent years, in the field of trajectory tracking control of robotic arms, scholars have proposed many control schemes, such as neural network control [5][6][7], iterative learning control [8,9], fuzzy control [10][11][12], sliding mode control [13][14][15], and so on. Sliding mode control is a non-linear control with a discontinuous control construction.…”