Adaptive Torque Feedforward Algorithm to Compensate Perturbations using Stochastic optimization Methods

This paper presents a novel approach to compensate periodic perturbations that occur in a vast variety of production processes. Typically, these perturbations interrupt the steady-state motion of mechanical systems, resulting in production inaccuracies. To address that problem, an adaptive torque feedforward algorithm is employed to systematically reduce unknown perturbations affects on the mechanical system. The algorithm is inherent stable and purely based on stochastic values. Therefore, the adaptation process uses only gradient-free optimization methods. All presented results can be, without any means, incorporated into a state-of-the-art programmable logic controller. Correctness of the stated algorithm is verified by simulation on the widely used two non-rigid coupled rotational masses spring-damper system corrupted by periodic perturbations on the load-end.