Abstract
Cranes play a very important role in transporting heavy loads in various industries. However, because of its natural swinging characteristics, the control of crane needs to be considered carefully. This paper presents a control approach to a flexible cable crane system in consideration of both rope length varying and system constraints. At first, from Hamilton's extended principle the equations of motion that characterized coupled transverse-transverse motions with varying rope length of the gantry are obtained. The equations of motion consist of a system of partial differential equations. Then, a barrier Lyapunov function is used to derive the control located at the trolley end that can precisely position the gantry payload and minimize vibrations. The designed control is verified through extensive experimental studies.
Highlights
Overhead cranes are widely used in many applications such as manufacturing factories, marine industries and harbour operations, due to their capability of transporting heavy loads or hazardous materials
Many control strategies have been applied to the crane system that can be divided into three categories including open loop, closed loop, and combined open and closed loop control, see [1] for more details
A deadbeat control is used to control and accelerate the position response, while partial feedback linearization is in charge of minimizing and stabilizing the sway angle
Summary
Overhead cranes are widely used in many applications such as manufacturing factories, marine industries and harbour operations, due to their capability of transporting heavy loads or hazardous materials. In [4], the hybrid partial feedback linearization and deadbeat control scheme is applied to control the crane. A deadbeat control is used to control and accelerate the position response, while partial feedback linearization is in charge of minimizing and stabilizing the sway angle. To delivery high-performance control operation for overhead crane, four control schemes are combined in [5] to control a overhead crane. The reference signal generator based on typical anti-swing trajectory performed by an expert crane operator is used to supply reference state trajectory profiles. To overcome the sensitivity of measured signal for feedback control scheme, inverse dynamic that uses simulations of feedback control by machine learning has been proposed in [7]. Artificial neural network that can act in realtime is used to learn inverse dynamic model from actual crane
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