Nonlinear Adaptive PID Control for Greenhouse Environment Based on RBF Network

Sensors

Songwei Zeng²  Haigen Hu²  Lihong Xu¹ 

[1] Department of Control Science and Engineering, Tongji University, Shanghai 200092, China;School of Information Engineering, Zhejiang Agriculture & Forestry University, Lin'an 311300, China; E-Mails:

关键词: nonlinear adaptive control;    neuro-PID control;    Radial Basis Function (RBF);    greenhouse environment control;    Genetic Algorithm (GA);   

DOI  :  10.3390/s120505328

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland