Safe distance prediction for braking control of bridge cranes considering anti-swing

Chen, Huili, Liu, Guoliang, Tian, Guohui, Zhang, Jianhua and Ji, Ze ORCID: https://orcid.org/0000-0002-8968-9902 2022. Safe distance prediction for braking control of bridge cranes considering anti-swing. International Journal of Intelligent Systems 37 (8) , pp. 4845-4863. 10.1002/int.22743

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Abstract

Cranes are widely deployed for lifting and moving heavy objects in dynamic environments with human coexistence. Suddenly appeared workers, vehicles, and robots can affect the safety of the cranes. To avoid possible collisions, the cranes must have prediction ability to know how dangerous the situation is. In this paper, we address the safety issues of bridge cranes based on its online physical states and control model. Due to the swing of the payload, the safe braking distance cannot be a constant value. Therefore, we here propose a model prediction control (MPC)-based anti-swing method for non-zero initial states, where a new reference trajectory and a new cost function for optimization are proposed, such that the proposed MPC method can control the crane to follow the proposed reference trajectory and achieve a stable stop state with anti-swing. Furthermore, an offline learning mechanism is introduced to learn a statistical model between the velocity of the crane and the safe braking distance achieved by using the proposed MPC braking control method. In this way, we can predict how far the crane would require to safely stop without swing based on its current velocity, which is the safe distance prediction to evaluate the dangerous level of the dynamic obstacle. Experiments using both a simulated crane and a real crane demonstrate that the proposed safe braking distance prediction method is effective for safe braking control of the bridge cranes.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Wiley
ISSN:	0884-8173
Date of First Compliant Deposit:	8 November 2021
Date of Acceptance:	5 November 2021
Last Modified:	24 Nov 2024 20:45
URI:	https://orca.cardiff.ac.uk/id/eprint/145371

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