Paper Info
Title
Towards Predictive Anti-Sway Control Of Hanging Loads: Model-Based Controller Design For A Knuckle Boom Crane
Abstract
Crane-based cargo handling constitutes a major part of today's logistics sector. Regardless of the used crane type oscillations of the hanging load have to be inhibited to guarantee both save and efficient loading operations. In particular, knuckle boom cranes which are often used offshore exhibit four degrees of freedom to position the load. As the main winch is often used for holding the load at a constant height above a stationary reference level (Active Heave Compensation) the crane joints are considered to damp out spatial load oscillations (Anti-Sway Control - ASC). In this paper, a predictive ASC scheme at kinematic level is proposed. Opposed to approaches previously reported in context of knuckle boom cranes the framework of model predictive control (MPC) enables two central features: First, dynamic limitations imposed by the crane's structure as well as actuators are considered explicitly during payload stabilization. Moreover, the two contradicting objectives of reference tracking and sway reduction are conveniently blended by introducing a tolerance envelope around the reference trajectory. This allows for damping action at a prescribed positioning accuracy. Feedback of the load position is realized by an Extended Kalman Filter (EKF). Operability of the derived control scheme is demonstrated for an initial load deflection using a robot-based test setup. In closed-loop the load swing is rapidly confined to a plane and eliminated thereafter. The crane related values remain bounded at all times.
Year
DOI
Venue
2019
10.23919/ECC.2019.8795871
2019 18TH EUROPEAN CONTROL CONFERENCE (ECC)
Field
DocType
Citations 
Deflection (engineering),Extended Kalman filter,Kinematics,Control theory,Computer science,Model predictive control,Active heave compensation,Winch,Trajectory,Payload
Conference
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Philipp Schubert100.68
Sebastian Stemmler222.58
Dirk Abel37643.90