A model predictive control method for 7-DoF tower cranes with distributed mass payloads and variable rope lengths

Abstract

Existing research on tower cranes mainly focuses on single pendulum and double pendulum models with point mass payloads. However, in the hoisting and transportation of large payloads, such as rockets, bridge boxes, and structural steel materials, the volume and shape of the payloads cannot be ignored. These characteristics introduce complex dynamic behaviors and make it challenging to directly apply existing control methods. Inspired by the above considerations, this paper presents a model predictive control (MPC) method for 7-degrees-of-freedom (DoF) tower cranes with distributed mass payloads (DMPs). Firstly, a dynamic model is established for 7-DoF tower cranes, considering DMPs, variable rope lengths, and the three-dimensional swing angles of hooks and payloads. Then, MPC methods with actuator saturation constraints are designed to achieve accurate positioning and payload swing suppression for 7-DoF tower cranes, while enhancing robustness in dynamic environments. It can also efficiently handle system parameter uncertainties and disturbances. Finally, experiments conducted on the tower crane experiment platform provide a more intuitive demonstration of the proposed method’s ability to efficiently suppress the swing of DMPs while ensuring the positioning accuracy of jibs, trolleys, and hoisting ropes.