Application of Norm Optimal-Gain-Arguable Iterative Learning Control in a Flexible Manipulator

This letter presents a norm optimal-gain-arguable iterative learning control (NOGAILC) scheme for accurately tracking the trajectory of a single-link flexible robotic manipulator. The proposed approach is based on the optimal iterative learning control with an iteration-time-variable learning gain vector, which is optimized by the performance index who is consisted by the energy of tracking error and the intensity of learning effort weighted by a tuning factor. During the analysis of monotone convergence of the tracking error, we derive the uniqueness and the existence of the learning gain vector and obtain an explicit formula for the convergence factor. Through the theoretical analysis, this control scheme can effectively improve the performance of trajectory tracking for the robotic manipulator system and the learning initiative of iterative learning control. The convergence rate can be regulated by selecting an appropriate tuning factor. Numerical simulations confirm the validity and effectiveness of our proposed NOGAILC algorithm in flexible manipulator systems.