Iterative Learning Control of a Robotic Arm Experiment Platform with Input Constraint

This paper addresses the vibration control and the trajectory tracking control of a robotic arm system with input constraint. A hyperbolic tangent function and a saturation function are adopted to tackle the input constraint. By defining a composite energy function, a dual-loop iterative learning control (ILC) law is designed by integrating a restrained learning law and a saturated feedback law. For the closed-loop system, the angle displacements are asymptotically regulated to track a prescribed constant trajectory and the elastic displacements are asymptotically suppressed to zero along the iteration axis. Simulation and experimental results are provided to illustrate the effectiveness of the designed ILC law.