A Data-Driven Multi-Device Collaborative Control Method in Coal Transportation System

The coal transportation system comprises several belt conveyors, which includes lots of electrical equipment. The devices are interconnected, and a problem with one of them will cause all devices to be paralyzed. So rapid and accurate multi-device collaborative control plays an important role in high safety performance and production efficiency. Traditional multi-device collaborative control algorithms depend on complex modeling with large-scale, complex constraints, uncertainties, and multi-objective conditions. Here, we propose a data-driven multi-device collaborative control method. In this paper, the neural network algorithm is selected to model the relationship between the equipment control operation and the environment, equipment status, and human activities, thus forming the multi-device collaborative control operation knowledge to guide the multi-device collaborative control in the actual operation process. Furthermore, experiments on real production datasets demonstrate the proposed approach can realize multi-device collaborative control in the coal transportation system, meeting the three goals of safety, energy-saving, and high efficiency simultaneously.