Model-Free Adaptive Control Method for Nuclear Steam Generator Water Level

Author(s):  
Wang Mingmei ◽  
Cheng Qiming ◽  
Cheng Yinman ◽  
Wang Yingfei
Author(s):  
Na Dong ◽  
Wenjin Lv ◽  
Shuo Zhu ◽  
Donghui Li

Model-free adaptive control has been developed greatly since it was proposed. Up to now, model-free adaptive control theory has become mature and tends to be an effective solution for complex unmodeled industrial systems. In practical industrial processes, most control systems are inevitably accompanied by noise that will result in indelible error and may further cause inaccurate feedback to the output. In order to solve this kind of problem with model-free technique, this article incorporates an improved tracking differentiator into model-free adaptive control. After that, the anti-noise model-free adaptive control method with complete convergence analysis is proposed. Meanwhile, numerical simulation proves that the improved control method can quickly track a given signal with good resistance to noise interference. Finally, the effectiveness and practicability of the proposed algorithm are verified by experiments through the control of drum water level of circulating fluidized.


Author(s):  
Mo Tao ◽  
Zhiwu Ke ◽  
Xianling Li ◽  
Ruotong Qu ◽  
Zhenxing Zhao ◽  
...  

Due to the increasingly complexity of the nuclear power device system, the demands for its performance and security become rather high. Once through steam generator (OTSG) has been widely researched and applied because of its practical advantages like simple structure and small volume, good static performance and mobility. It can produce superheated steam, can work without dehumidification device, and can improve the thermal efficiency of a system. However OTSG has evidently shortages on tight coupling, nonlinearity, large hysteresis and strong interference. That makes its controller designing faces with great challenges. Keeping to the topic of OTSG (Including the feed pump and the feed valve), the main study subjects of this paper are: Firstly, identifying the plant system and researching the four stages changing of the steam pressure by driving the feed valve with a step signal. Non-minimum phase characteristic is found in the second stage. Secondly, discussing whether MFAC and PID are applicable to the non-minimum phase characteristic in the second stage of steam pressure changing. Whether they can eliminate the changing in the first stage, overcome the non-minimum phase characteristic, and finally reduce the cyclic stress damage of OTSG. Thirdly, for the “feed valve-steam pressure” channel, adjusting the feed valve to track the steam pressure. Then, whether the adjusting of the feed valve can resist the disturbance on steam pressure is researched. This research can help us avoid adjusting the circuit and the feed pump frequently and enhances the reliability and security of the system on the premise that no adjust on feed valve and the power of the first circuit. Compared with the simulation result of PID, the convergence rate of MFAC is evidently faster than PID’s. In brief, MFAC has obvious superiorities in tracking, adapting, anti-interference and overcoming large hysteresis when compared with PID controller.


Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3241 ◽  
Author(s):  
Xiaofei Zhang ◽  
Hongbin Ma

Model-free adaptive control (MFAC) builds a virtual equivalent dynamic linearized model by using a dynamic linearization technique. The virtual equivalent dynamic linearized model contains some time-varying parameters, time-varying parameters usually include high nonlinearity implicitly, and the performance will degrade if the nonlinearity of these time-varying parameters is high. In this paper, first, a novel learning algorithm named error minimized regularized online sequential extreme learning machine (EMREOS-ELM) is investigated. Second, EMREOS-ELM is used to estimate those time-varying parameters, a model-free adaptive control method based on EMREOS-ELM is introduced for single-input single-output unknown discrete-time nonlinear systems, and the stability of the proposed algorithm is guaranteed by theoretical analysis. Finally, the proposed algorithm is compared with five other control algorithms for an unknown discrete-time nonlinear system, and simulation results show that the proposed algorithm can improve the performance of control systems.


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