Anti-noise model-free adaptive control and its application in the circulating fluidized bed boiler

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.

2011 ◽  
Vol 383-390 ◽  
pp. 79-85
Author(s):  
Dong Yuan ◽  
Xiao Jun Ma ◽  
Wei Wei

Aiming at the problems such as switch impulsion, insurmountability for influence caused by nonlinearity in one tank gun control system which adopts double PID controller to realize the multimode switch control between high speed and low speed movement, the system math model is built up; And then, Model Reference Adaptive Control (MRAC) method based on nonroutine reference model is brought in and the adaptive gun controller is designed. Consequently, the compensation of nonlinearity and multimode control are implemented. Furthermore, the Tracking Differentiator (TD) is affiliated to the front of controller in order to restrain the impulsion caused by mode switch. Finally, the validity of control method in this paper is verified by simulation.


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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