Abstract
Cranes are widely used in the field of construction, logistics, and the manufacturing industry. Cranes that use wire ropes as the main lifting mechanism are deeply troubled by the swaying of heavy objects, which seriously restricts the working efficiency of the crane and even cause accidents. Compared with the single-pendulum crane, the double-pendulum effect crane model has stronger nonlinearity, and its controller design is challenging. In this paper, cranes with a double-pendulum effect are considered, and their nonlinear dynamical models are established. Then, a controller based on the radial basis function (RBF) neural network compensation adaptive method is designed, and a stability analysis is also presented. Finally, the hardware-in-the-loop experimental results show that the neural network compensation control can effectively improve the control performance of the controller in practice.
Highlights
Cranes are normally utilized to lift or move cargo in the field of construction, logistics, the manufacturing industry, etc
The neural network method had been introduced into the crane system, and the model of the double-pendulum crane follows Eq 2: M q q€ + C q, q_q_ + G q τ + d where q x θ1 θ2 T ; q€ x€ €θ1 €θ2 T ; τ F 0 0 T ; q_x_ θ_ 1 θ_ 2 T ; The control purpose of the double-pendulum crane is to help the trolley stop at the desired position while the first and second payloads which is set below the trolley keeps still
The hardware in the loop (HIL) simulation is introduced into the experiment, which is known as an advanced method in scientific research
Summary
Cranes are normally utilized to lift or move cargo in the field of construction, logistics, the manufacturing industry, etc. Zhang et al (Zhang et al, 2016) proposed an adaptive tracking controller based on double-pendulum overhead cranes with uncertainties and disturbances by building a new sliding function as the desired trajectory. Base on the mathematical model, an adaptive controller based on a radial basis function neural network compensation method is designed where the core of the controller is the robust controller, and the FIGURE 2 | Simplified model of the crane with double-pendulum. Owing to the development of neural network technology (Hunt et al, 1992; Feng, 1995), a great process had been made in the field of automatic control, signal processing, and pattern recognition In this case, the neural network method had been introduced into the crane system, and the model of the double-pendulum crane follows Eq 2:.
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