Transport control of underactuated cranes

Abstract

Overhead cranes are important equipments that are used in many industries. They belong to underactuated mechanical systems. Since it is hard to obtain an accurate model for control design, this paper presents a design scheme for the transport control problem of overhead cranes with uncertainties. In this scheme, a variable structure control law based on sliding mode is designed for the nominal model and neural networks are utilized to learn the upper bound of system uncertainties. In the sense of Lyapunov theorem, the update formulas of the network weights are deduced to approximate the system uncertainties. From the design process and comparisons, it can be seen that: 1) the neural approximator is able to compensate the system uncertainties, 2) the control system possesses asymptotic stability, 3) better performance can be achieved. © 2011 Springer-Verlag.