DESIGN AND IMPLEMENTATION OF A NEURAL CONTROL SYSTEM AND PERFORMANCE CHARACTERIZATION WITH PID CONTROLLER FOR WATER LEVEL CONTROL

@article{Hasan2011DESIGNAI,
  title={DESIGN AND IMPLEMENTATION OF A NEURAL CONTROL SYSTEM AND PERFORMANCE CHARACTERIZATION WITH PID CONTROLLER FOR WATER LEVEL CONTROL},
  author={Md. Selim Hasan and Aiman Khan and Saqib Alam and Mehedi Hasan Pavel},
  journal={International Journal of Artificial Intelligence \& Applications},
  year={2011},
  volume={2},
  pages={79-88}
}
The objective of this thesis is to investigate and find a solution by designing the intelligent controller for controlling water level system, such as neural network. The controller also can be specifically run under the circumstance of system disturbances. To achieve these objectives, a prototype of water level control system has been built and implementations of both PID and neural network control algorithms are performed. In PID control, Ziegler Nichols tuning method is used to control the… 

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References

SHOWING 1-10 OF 25 REFERENCES
Neuro-predictive process control using online controller adaptation
TLDR
A novel architecture for integrating neural networks with industrial controllers is proposed, for use in predictive control of complex process systems and is used in the stabilization and transient control of U-tube steam generator water level.
Adaptive Neural Network Control for Drum Water Level Based on Fuzzy Self-Tuning
An adaptive neural network control strategy based on fuzzy self-tuning is presented. The strategy is applied to the control system for drum water level of coal-fired power plant. Fuzzy inference
Identification and control experiments using neural designs
TLDR
Real-time implementation of the designs using a hardware example case system illustrates the inherent capability of neural networks to handle nonlinearities, learn, and perform control effectively for a real world system, based on minimal system information.
Development and application of a gradient descent method in adaptive model reference fuzzy control
  • A. Naman, M. Z. Abdulmuin, H. Arof
  • Computer Science
    2000 TENCON Proceedings. Intelligent Systems and Technologies for the New Millennium (Cat. No.00CH37119)
  • 2000
TLDR
The paper derives the AM RFC and compares its performance with the more conventional methods of proportional-integral control and model-reference adaptive control, and finds that the AMRFC and MRAC have approximately similar performance, however they compare favorably to the PI controller.
Dynamic system identification using neural networks
TLDR
Simulated and experimental results for a second-order plant show that identification can be satisfactorily achieved and that neural network identifiers can represent nonlinear plant characteristics very well.
Advanced process control techniques for water treatment using artificial neural networks
TLDR
Various advanced process control techniques are described, the potentially large role of ANN models in implementing these techniques, and issues and solutions when using ANN in a real-time control system are described.
Online performance evaluation of a self-learning fuzzy logic controller applied to nonlinear processes
TLDR
The results presented show that even with a limited knowledge of the process, the self-learning procedure is able to develop a suitable set of rules and produce a satisfactory process performance with some degree of robustness and repeatability when applied to a nonlinear single-input single- output (SISO) or multi-input multi-output (MIMO) laboratory liquid-level processes.
An neural network based adaptive control for liquid level of molten steel smelting non-crystalloid flimsy alloy line
A new method based on neural network for controlling the liquid level of molten steel smelting non-crystalloid flimsy alloy line is presented. The improved BP neural network is used to adjust the
Predictive control by local linearization of a Takagi-Sugeno fuzzy model
Linear model based predictive control (MBPC) has many advantages but also drawbacks over nonlinear MBPC. In this paper a possibility of using linear MBPC to control nonlinear systems is investigated.
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