High-Order Neural-Network-Based Multi-Model Nonlinear Adaptive Decoupling Control for Microclimate Environment of Plant Factory
Author:
Wang Yonggang1, Chen Ziqi1, Jiang Yingchun2ORCID, Liu Tan1
Affiliation:
1. College of Information and Electronic Engineering, Shenyang Agricultural University, Shenyang 110866, China 2. College of Engineering, Shenyang Agricultural University, Shenyang 110866, China
Abstract
Plant factory is an important field of practice in smart agriculture which uses highly sophisticated equipment for precision regulation of the environment to ensure crop growth and development efficiently. Environmental factors, such as temperature and humidity, significantly impact crop production in a plant factory. Given the inherent complexities of dynamic models associated with plant factory environments, including strong coupling, strong nonlinearity and multi-disturbances, a nonlinear adaptive decoupling control approach utilizing a high-order neural network is proposed which consists of a linear decoupling controller, a nonlinear decoupling controller and a switching function. In this paper, the parameters of the controller depend on the generalized minimum variance control rate, and an adaptive algorithm is presented to deal with uncertainties in the system. In addition, a high-order neural network is utilized to estimate the unmolded nonlinear terms, consequently mitigating the impact of nonlinearity on the system. The simulation results show that the mean error and standard error of the traditional controller for temperature control are 0.3615 and 0.8425, respectively. In contrast, the proposed control strategy has made significant improvements in both indicators, with results of 0.1655 and 0.6665, respectively. For humidity control, the mean error and standard error of the traditional controller are 0.1475 and 0.441, respectively. In comparison, the proposed control strategy has greatly improved on both indicators, with results of 0.0221 and 0.1541, respectively. The above results indicate that even under complex conditions, the proposed control strategy is capable of enabling the system to quickly track set values and enhance control performance. Overall, precise temperature and humidity control in plant factories and smart agriculture can enhance production efficiency, product quality and resource utilization.
Funder
National Natural Science Foundation of China Natural Science Foundation of Liaoning Province
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference38 articles.
1. Goto, E. (2012, January 15–18). Plant production in a closed plant factory with artificial lighting. Proceedings of the VII International Symposium on Light in Horticultural Systems, Wageningen, The Netherlands. 2. An automatic plant growth measurement system for plant factory;Chen;IFAC Proc. Vol.,2013 3. Chowdhury, M., Kiraga, S., Islam, M.N., Ali, M., Reza, M.N., Lee, W.-H., and Chung, S.-O. (2021). Effects of temperature, relative humidity, and carbon dioxide concentration on growth and glucosinolate content of kale grown in a plant factory. Foods, 10. 4. Fuzzy logic in determining the control temperature and humidity in plant factory for cultivation of pak choy (Brassica chinensis L.) hydroponics;Umam;Indones. Green Technol. J.,2019 5. Ngo, V., Chung, S., Choi, J., Park, S., Kim, S., Ryu, D., and Kang, S. (2013, January 6–11). Control of temperature, humidity, and CO2 concentration in small-sized experimental plant factory. Proceedings of the International Symposium on New Technologies for Environment Control, Energy-Saving and Crop Production in Greenhouse and Plant, Jeju, Republic of Korea.
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