Velocity Control for Autonomous Following Platform Walking Speed Based on DBO Optimized BP-PID Algorithm
Abstract
This study addresses the issues of complexity in greenhouse operational environments and poor stability of existing mechanical walking systems by conducting research on the autonomous following electric platform walking speed control in greenhouses. Due to the system’s inherent nonlinearity and time-varying characteristics, traditional PID control algorithms fail to achieve effective control. Therefore, a dung beetle optimizer (DBO) optimized BP neural network PID control algorithm was proposed. This algorithm optimized the weights of the BP neural network by using the DBO algorithm, thereby accelerating the self-learning rate of the BP neural network. It achieved rapid and precise control of the greenhouse autonomous following electric platform walking speed, enhanced system response speed, and reduced overshoot. Experimental results demonstrated that at a walking speed of 1 m / s, the system exhibited an average response speed of 0.11 s, settling time of 0.27 s, and a maximum overshoot of 2.44% . When there were changes in track speed and direction, the system maintained advantages of fast response, minimal overshoot, and oscillation-free steady-state process. Compared with PID control algorithm, BP-PID control algorithm, GA-PID control algorithm, ACO-PID control algorithm, the DBO-BP-PID control algorithm showed superior performance in control stability and precision, effectively mitigating system hysteresis and nonlinear effects, thereby meeting the control requirements for greenhouse autonomous following electric platform walking speed.
Keywords: greenhouse, autonomous following electric platform, walking speed control, dung beetle optimization algorithm, BP-PID control
Download Full Text:
PDFReferences
LIU Jizhan. Research progress analysis of robotic harvesting technologies in greenhouse [ J]. Transactions of the Chinese Society for Agricultural Machinery, 2017,48(12) : 1 - 18. (in Chinese)
LI Tianhua, DONG Guangsheng, YAO Yukang, et al. Design and experiment of segmented and variable distance spraying robot for multi-span greenhouse; [ J]. Transactions of the Chinese Society for Agricultural Machinery ,2024, 55(2) ; 170 - 179. (in Chinese)
HU Jin, YANG Yongxia, LI Yuanfang, et al. Analysis and prospect of the environmental control systems for greenhouse [J]. Transactions of the CSAE, 2024, 40( 1) : 112 - 128. (in Chinese)
MAO Hanping, JIN Chun, CHEN Yong. Research progress and prospect on control methods of greenhouse environment [J]. Transactions of the Chinese Society for Agricultural Machinery, 2018,49(2) : 1 —13. (in Chinese)
B1 Song, ZHANG Guoxuan, LI Zhijun, et al. Positioning method of greenhouse plant protection robot based on distance measurement value correction [J] . Transactions of the Chinese Society for Agricultural Machinery,2023,54(8) ;347 -358. (in Chinese)
KANG Jun, MENG Wenjun, ABRAHAM A, et al. An adaptive FID neural network for complex nonlinear system control[ J]. Neurocomputing, 2014,135 :79 - 85.
SOMWANSH1 D, BUNDELE M, KUMAR G, et al. Comparison of fuzzy -PID and PID controller for speed control of DC motor using Lab VIEW [ J]. Procedia Computer Science, 2019,152:252 -260.
WANG Hongzhi, WANG Tingting, HU Huangshui, et al. PID control based on BP neural network optimized by Q-learning for speed control of BLDCM [J]. Journal of Jilin University ( Engineering and Technology Edition) , 2021 ,51 (6) ;2280 -2286. (in Chinese)
CHEN Wenming, WANG Gongpu, HU Lianglong, et al. Research on the control strategy of leafy vegetable harvester travel speed automatic control system [J] . AgriEngineering, 2022,4(4) :801 -825.
YIN Hongqiao, YI Wenjun, WU Jintao, et al. Adaptive fuzzy neural network PID algorithm for BLDCM speed control system [J]. Mathematics, 2022,10(1) : 118.
WANG Jun, LI Moudao, JIANG Weibin, et al. A design of FPGA-based neural network PID controller for motion control system[ J]. Sensors, 2022,22(3) :889.
KHAN L, KHATOON A, GAUK F, el al. Speed control of wheeled mobile robot by nature-inspired social spider algorithm- based FID controller [J ]. Processes, 2023 ,11(4) ; 1202.
ZHANG Hongyuan, HAN Mingjuan, Ml Yajie, et al. Research and implementation of intelligent control system for grassland grazing robotic vehicle[ J ]. Electronics, 2023,12(14) :3078.
Q1N Gui, LIU Xiaoming, GUO Jianye, et al. Experiment and demonstration of planting mechanization technology for reenhouse vegetable production J]. Agricultural Engineering, 2014,4( Supp. 2) ;41 -44,48. (in Chinese)
KUMMUAL В N, КОТА V R. Design of MFA -PSO based fractional order PID controller for effective torque controlled BLDC motor [J] . Sustainable Energy Technologies and Assessments, 2022,49:101644.
ELTOUM A M, HUSSEIN A, ABIDO M A. Hybrid fuzzy fractional-order PID-based speed control for brushless DC motor [J]. Arabian Journal for Science and Engineering, 2021 ,46( 10) :9423 -9435.
XU Jinghui, WANG Lei, TAN Xiaoqiang, et al. Application of PID control based on SOA optimization in intelligent irrigation system[ J] . Transactions of the Chinese Society for Agricultural Machinery, 2020,51(4) :261 -267. (in Chinese)
ZHENG Haiyan, AB1D Y. Optimization algorithm for PID control parameters of electrical equipment in rural electric drainage and irrigation stations [J] . Mobile Information Systems, 2022,2022:4268662.
ZHANG Zhizhou, LI Yang. An AEFA-based optimum design of fuzzy PID controller for attitude control flywheel with BLDC motor [J ]. Aerospace, 2022,9( 12) :789.
ZENG Xiongfei. The PID control algorithm based on partie swarm optimization optimized BP neural network [J] . Electronic Design Engineering, 2022,30(11) :69 -73 ,78. (in Chinese)
XUE Huan, LI Гао, LI Jie, et al. Multi-objective optimization for turning process of 304 stainless steel based on dung beetle optimizer-back propagation neural network and improved particle swarm optimization [ J ]. Journal of Materials Engineering and Performance, 2024,33(8) :3787 -3800.
XUE Jiankai, SHEN Bo. Dung beetle optimizer; a new meta-heuristic algorithm for global optimization [J ]. The Journal of Supercomputing, 2023,79(7) :7305 -7336.
HE Jiachen, FU Lihui. Robot path planning based on improved dung beetle optimizer algorithm[ J ]. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2024,46(4) ;235.
Refbacks
- There are currently no refbacks.
