VOLUME 21(3), 2022 311 Date of publication SEP-30, 2022, date of current version JAN-10, 2022. www.computingonline.net / computing@computingonline.net Print ISSN 1727-6209 Online ISSN 2312-5381 DOI 10.47839/ijc.21.3.2686 Intellectual Scenario-synergetic Control of the Humidity and Temperature Regime of the Greenhouse Facilities DMYTRO POLISHCHYK 1 , VITALIY LYSENKO 1 , SERHII OSADCHIY 2 , NATALIA ZAIETS 1 1 National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine (e-mail: poli19poli94@gmail.com, lysenko@nubip.edu.ua, z-n@ukr.net) 2 Central Ukrainian National Technical University, Ukraine, ivserg215@gmail.com) Corresponding author: Dmytro Polishchyk (e-mail: poli19poli94@gmail.com). ABSTRACT The article substantiates the management of the humidity and temperature regime of greenhouse complexes on the basis of a scenario-synergetic approach. The scenarios for controlling the temperature and humidity conditions in the greenhouse using the approach of fuzzy neural networks are formed. The structure of an automated control system for technological processes is developed, which provides automated collection and processing of information for the implementation of control actions in order to improve the efficiency of the greenhouse complex on the basis of a scenario-synergetic approach. The corresponding fuzzy neural networks are synthesized for a synergistic assessment of the interaction of technological parameters. Estimation of the root-mean-square error in the synthesis of fuzzy neural networks confirms the possibility of their use for the synergistic formation of scenarios for controlling the temperature and humidity regime in greenhouses to reveal the presence of a synergistic effect. Production rules for scenario management of temperature and humidity conditions are formed. It is shown that the use of fuzzy neural networks for the formation of scenarios for controlling the humidity and temperature regime provides the possibility of obtaining the appropriate scenarios for making managerial decisions and their prompt correction. KEYWORDS intelligent control system; biotechnological object; greenhouse; mathematical filter; neural network; synergetic control. I. INTRODUCTION ROWING integration of technological approaches to the automation processes into the greenhouse production increases the resource efficiency, reduces the labor costs and increases the cost effectiveness of production [1, 2]. The traditional climate control systems within the under- cover greenhouse facilities which combine control and measurement systems for temperature, irrigation, additional lighting, СО 2 supply, ventilation as well as distributed sensor networks [25] are widely known [3, 4]. Providing that they are using peculiar software, such systems operate in two modes, automatic and semi-automatic. However, at the present-stage of development the computational intelligence systems demonstrate the following disadvantage – the control process does not take into account the crosscutting interrelation of the technological parameters, that is, when regulating one parameter leads to the divergence of others and the forecasted changes of the external environmental disturbances are not considered synergistically. Consequently, adapting the existing methods of automating the greenhouse facilities on the basis of their interactions in accordance with the limiting economic criteria is an urgent task. II. RELATED WORK Studying the works of Gao A., Chen H., Vögeling H., Lee Yongwei, Pozin G.M., Pryschep L.G., Stroy A.F., Takakura, Terence Belvins, Tkachenko V.A. and other scientists, experts proved that it is necessary to improve the existing microclimate control systems in the under-cover greenhouse facilities towards their comprehensiveness in terms of managerial decision making in order to come to a compromise between the energy expenditures and quality of the products [13-24]. Control systems were developed that provide intelligent formation of control strategies based on information received from a mobile robot [5, 26]. Also, control systems were developed for the process of growing tomatoes in greenhouses with algorithms for predicting external natural disturbances [6]. However, none of the existing control systems takes into account the synergistic effect of the mutual influence of G