Time optimal control of overhead cranes with hoisting of the load
Automatica (Journal of IFAC)
On characterizations of the input-to-state stability property
Systems & Control Letters
Analytical structures and analysis of the simplest fuzzy PDcontrollers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Some new results on system identification with dynamic neural networks
IEEE Transactions on Neural Networks
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This paper considers the problem of PD control of overhead crane in the presence of uncertainty associated with crane dynamics. By using radial basis function neural networks, these uncertainties can be compensated effectively. This new neural control can resolve the two problems for overhead crane control: 1) decrease steady-state error of normal PD control. 2) guarantee stability via neural compensation. By Lyapunov method and input-to-state stability technique, we prove that these robust controllers with neural compensators are stable. Real-time experiments are presented to show the applicability of the approach presented in this paper.