Nonlinear Adaptive PID Control for Greenhouse Environment Based on RBF Network

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.

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“…The output layer can accomplish linear mapping. The RBF neural network, a unity of linear function and nonlinear function, has the advantages of simple structure, self-adapting architecture determination and quick convergence speed [15]. Gaussian function is used as the transfer function in the hidden layer, and the formula is: where Ri(x) is the ith neurons node, x is n dimensional input vector, Ci is the center of the ith hidden node function, αi is the normalized parameter of the ith hidden node, and r is the number of the hidden layer nodes.…”

Crack Orientation and Depth Estimation in a Low-Pressure Turbine Disc Using a Phased Array Ultrasonic Transducer and an Artificial Neural Network

“…In this work, the 8-dimensional feature vector F constructed in Section 3 is the input of the neural network, and the crack orientation angle and depth constitute the 2-dimensional output, then the structure of the RBF neural network is shown in Figure 8 [15]. …”

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“…and also by many other practical constraints (actuators, moistening, cycle, etc.) [16]. In spite of this widespread usage in industrial production, the effectiveness of PID controller is often limited especially owing to poor tuning, and tuning PID controllers efficiently is still a challenge for the greenhouse production and other industrial applications.…”