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.

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In this paper, a novel anti-windup scheme is proposed for the prevention of proportional-integralderivative (PID) controller integrator saturation by introducing a changing integration rate into the integral term. The method can achieve smooth adjustment for the integral term in a stepless way according to the error size, and it is easily implemented and has been successfully applied to a greenhouse climate control problem. The results show that the proposed anti-windup method can provide better closed-loop performance compared with the conventional AWBT method.