Nonlinear Adaptive PID Control for Greenhouse Environment Based on RBF NetworkReport as inadecuate




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1

School of Information Engineering, Zhejiang Agriculture & Forestry University, Lin’an 311300, China

2

Department of Control Science and Engineering, Tongji University, Shanghai 200092, China





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Abstract This paper presents a hybrid control strategy, combining Radial Basis Function RBF network with conventional proportional, integral, and derivative PID controllers, for the greenhouse climate control. A model of nonlinear conservation laws of enthalpy and matter between numerous system variables affecting the greenhouse climate is formulated. RBF network is used to tune and identify all PID gain parameters online and adaptively. The presented Neuro-PID control scheme is validated through simulations of set-point tracking and disturbance rejection. We compare the proposed adaptive online tuning method with the offline tuning scheme that employs Genetic Algorithm GA to search the optimal gain parameters. The results show that the proposed strategy has good adaptability, strong robustness and real-time performance while achieving satisfactory control performance for the complex and nonlinear greenhouse climate control system, and it may provide a valuable reference to formulate environmental control strategies for actual application in greenhouse production. View Full-Text

Keywords: nonlinear adaptive control; neuro-PID control; Radial Basis Function RBF; greenhouse environment control; Genetic Algorithm GA nonlinear adaptive control; neuro-PID control; Radial Basis Function RBF; greenhouse environment control; Genetic Algorithm GA





Author: Songwei Zeng 1, Haigen Hu 1,2,* , Lihong Xu 2,* and Guanghui Li 1

Source: http://mdpi.com/



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