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This paper presents a new approach of an open-loop control for overhead cranes with load hoisting to suppress the sway motion after positioning, in which soft computing techniques are utilized. We apply a neural network to generate the desired trolley position. In order to obtain the trajectory of the trolley position to reduce the sway angle as much as possible, the neural network is trained by a particle swarm optimization (PSO). The suppression of the sway angle can be realized by moving the trolley along the obtained trajectory, that is, the open-loop control is established. By performing not only numerical simulations but also experiments, the effectiveness of the proposed method in suppressing the residual sway is demonstrated. In the present approach, it is a feature of the neural network trained by PSO that control systems can be constructed without any prior control knowledge if a mathematical model of a plant is given.

Open-Loop Control for Overhead Cranes with Load Hoisting by Using Soft Computing