PID Controller Design Based on Radial Basis Function Neural Networks for the Steam Generator Level Control

Abstract

Steam generator level control system is a vital control system for the Pressurized Water Reactor (PWR). However, the steam generator level process is a highly nonlinear and non-minimum phase system, the conventional Proportional- Integral-Derivative (PID) control scheme with fixed parameters was difficult to obtain satisfactory control performance. The Radial Basis Function (RBF) Neural Networks based PID control strategy (RBFNN-PID) is proposed for the steam generator level control. This method can identify the mathematical model of the steam generator via the RBF neural networks, and then the PID parameters can be optimized automatically to accommodate the characteristic variation of the process. The optimal number of the hidden layer neurons is also discussed in this paper. The simulation results shows that the PID controller designed based on the RBF neural networks has good control performance on the steam generator level control.

Multivariable Model Reference Adaptive Control of an Industrial Power Boiler Using Recurrent RBFN

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“…In other words, in this paper, the model reference block is a neural network model and it is a PID controller. e major drawback of [33] is the use of PID in the boiler system because the boiler is a nonlinear and delayed system and the PID does not provide a good response. Multivariable control means simultaneous control of parameters by considering their interaction [34,35].…”

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“…When the number of the neurons of RBFNN increases, the accuracy of position identification improves. However, the running time also increases, affecting the control quality of the entire system [15]. In this study, the selected number of neurons in the hidden layer of each RBFNN is 10.…”

Performance Optimization of Ship Course Via Artificial Neural Network and Command Filtered Cdm-Backstepping Controller

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“…To reply to the presented problem, a robust nonlinear controller based command filtered backstepping method [11][12][13] and coefficient diagram method [14][15][16][17] is developed were its parameters are optimised using basis radial neural network optimization (RBFNN) [18][19][20][21]. This approach is also suitable for parameters estimation and system identification works, one of the most significant functions, called the 'Gaussian function', is used in the hidden layer.…”