We designed a feedback controller to automate crane operations by controlling the load position and its swing. First, a PD tracking controller is designed to follow a prescribed trajectory. Then, another controller is added to the control loop to damp the load swing. The anti-swing controller is designed based on two techniques: a time-delayed feedback of the load swing angle and an anti-swing fuzzy logic controller (FLC). The rules of the FLC are generated by mapping the performance of the time-delayed feedback controller. The same mapping method used for generating the rules can be applied to mimic the performance of an expert operator. The control algorithms were designed for gantry cranes and then extended to tower cranes by considering the coupling between the translational and rotational motions. Experimental results show that the controller is effective in reducing load oscillations and transferring the load in a reasonable time. To experimentally validate the theory, we had to compensate for friction. To this end, we estimated the friction and then applied a control action to cancel it. The friction force was estimated by assuming a mathematical model and then estimating the model coefficients using an off-line identification technique, the method of least squares (LS).

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The objective of crane control is to build an algorithm to move a load from point to point in the shortest time without inducing large swings. We assume that this objective cannot be accomplished in less than a single oscillation cycle of the load. Therefore, the controller is built to move the load such that it completes only one oscillation cycle at the end of the motion. Consequently, the settling time of the system should be equal to the period of oscillation of the load. This criterion enables the calculation of the controller feedback gains for varying load weight and cable length. The controller is built first for overhead cranes and then modified for tower cranes. Two controllers are used, one for the rotational motion of the tower and the other for the translational motion of the trolley. Numerical simulations show that the controller is effective in reducing the load oscillations and transferring the load in a reasonable time compared with that of optimal control.

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