Intelligent Simulation-Based Training System for Harvester Operators

A human operator still remains a critical member in any of numerous man-machine systems, who eventually determines the effectiveness of a whole system. Improvement of operator training quality in the learning process may be considered as the shortest path to productivity enhancement. The paper deals with modern approaches to operator training. Today, development of simulation-based training systems is an essential trend in the professional training of operators for technological, transport and information systems for various use. Along distinct and well-known advantages, training simulators have some drawbacks including such as a limited number of simulation scenarios of the operating environment, difficulties in individualization of a training program. We have proposed an approach mitigating the effect of the above-mentioned drawbacks due to artificial intelligence technologies. The proposed intelligent simulation-based training system ensures individualization of an operator training process based on their performance. The use of genetic algorithms enhances the variability of a simulated environment due to combination of its parameters. The proposed structure of a multi-user system makes it possible to train operators in the common simulated environment.