Intelligent control systems for dynamic mixing processes in seed processing machines with highly elastic working bodies

Introduction. The development of intelligent control systems by means of various production and technological processes is an urgent problem. Pre-sowing seed treatment is an important agricultural process, without which it is impossible to get a planned harvest of high quality. Materials and Methods. To create an intelligent system for control of seed mixing processes in seed processing machines before sowing, the technological process of pre-sowing treatment should be considered as a multi-level biotechnical system. There is a relationship between the objects of the biotechnological system in the process of pre-sowing seed treatment that can be represented in the form of a block diagram. A multi-level biotechnological system is considered as a cyber-physical system - a combination of various natural and artificial objects which is a single whole capable of self-preservation and development. Results. The components of an intelligent system for controlling dynamic mixing processes will be working memory, many fuzzy rules describing the execution of mixing operations, and a strategy for selecting rules depending on the state of the system. In developing the intelligent mixing process control system, a return strategy is implemented. The strategy of dynamic mixing system control is implemented by a direct conclusion. Discussion and Conclusion. The intelligent biotechnology control system will allow controlling the mixing process in real-time, correcting the kinematic parameters of the mixer and warning timely about the probability of damage for the elastic working element. Preliminary expert assessments and laboratory tests have shown that the use of an intelligent control system for seed treatment processes before sowing will improve the quality of the decisions made, reduce the control time of the mixing process by more than two times compared to existing control methods, reduce the physical load on the operator by 50% and increase the productivity of the mixing process by up to 20%.