Human Operator Learning on Double-Pendulum Bridge Cranes

Abstract

Oscillation of crane payloads makes it challenging to manipulate payloads quickly, accurately, and safely. The problem is compounded when the payload creates a double-pendulum effect. This paper evaluates an input-shaping control method for reducing double-pendulum oscillations. Human operator performance testing on a 10-ton industrial bridge crane is used to verify the effectiveness and robustness of the method. The tests required the operators to drive the crane numerous times over a period of eight days. Data from these experiments show that human operators perform manipulation tasks much faster and safer with the proposed control scheme. Furthermore, considerably less operator effort is required when input shaping is used to limit the oscillation. These experiments also show that significant learning occurred when operators did not have the aid of input shaping. However, the performance never approached that achieved with input shaping without any training. With input shaping enabled, only moderate learning occurred because operators were able to drive the crane near its theoretical limit during their first tests.