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.
Highlights
The greenhouse climate control concerns the creation of a favorable environment for the crop in order to reach predetermined results for high yield, high quality and low production costs
Various advanced control techniques and related strategies, such as predictive control [1,2,3], adaptive control [4,5], nonlinear feedback control [6], fuzzy control [7,8], robust control [9], optimal control [10,11,12] and compatible control [13] have been proposed for greenhouse environment control
In order to verify the efficiency and performance of the proposed Neuro-PID control scheme, a series of simulations are presented in the present section
Summary
The greenhouse climate control concerns the creation of a favorable environment for the crop in order to reach predetermined results for high yield, high quality and low production costs. Various advanced control techniques and related strategies, such as predictive control [1,2,3], adaptive control [4,5], nonlinear feedback control [6], fuzzy control [7,8], robust control [9], optimal control [10,11,12] and compatible control [13] have been proposed for greenhouse environment control These studies are very important to real-world engineering application in greenhouse production. Most of these approaches are either theoretically complex or difficult to implement in actual greenhouse production
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