Scopus

This paper proposes an adaptive input shaper for swing control of a five degrees of freedom tower crane under various parameter uncertainties together with payload hoisting and simultaneous motions. The real-time adaptive mechanism is designed using neural network and the shaper parameters can be updated based on current crane’s parameters. This approach avoids the requirement for re-design of controllers as in the conventional technique. Experiments are conducted to assess effectiveness of the controller under challenging scenarios up to 100% changes in the system’s natural frequency. These involve different speeds and payload masses, payload hoisting, and distances of trolley and jib. Results demonstrate that the shaper is robust against parameter uncertainties and its superiority is confirmed with an improvement of at least 50% as compared to a comparative robust shaper. The shaper also provides a satisfactory performance under obstacle avoidance where payload lifting and lowering are performed within a single manoeuvre.