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Enhanced adaptive nonlinear extended state observer for pure feedback systems with matched and mismatched disturbances
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作者 Mahtab Delpasand Mohammad Farrokhi 《Control Theory and Technology》 EI CSCD 2024年第2期254-268,共15页
In this paper, an enhanced adaptive nonlinear extended state observer (EANESO) for single-input single-output pure feedback systems in the presence of external time-varying disturbances is proposed. In this paper, a n... In this paper, an enhanced adaptive nonlinear extended state observer (EANESO) for single-input single-output pure feedback systems in the presence of external time-varying disturbances is proposed. In this paper, a nonlinear system with matched and mismatched disturbances is considered. The conventional extended state observer (ESO) can only be applied to systems that are in the form of integral chains. Moreover, this method has limitations in the face of mismatched disturbances. In the presence of time-varying disturbances, the traditional ESOs cannot estimate the disturbances accurately. To overcome this limitation, an EANESO is proposed in this paper. The main idea is to design the nonlinear ESO (NESO) to estimate the states of the system and multiple disturbances simultaneously. The observer gains are considered time-varying and adjusted with adaptation laws to improve the estimation accuracy and overcome the peaking phenomenon. Next, the proposed controller is designed based on output feedback to eliminate the effects of multiple disturbances and stabilize the closed-loop system. Subsequently, the stability analysis of the closed-loop system and convergence of the observer error are discussed. Finally, the proposed method is applied to the inverted pendulum system. The simulated results show good performance of the proposed method as compared with a recently published scheme in the related literature. 展开更多
关键词 nonlinear extended state observer Enhanced adaptive extended state observer Adaptation law Multiple-channel disturbances Inverted pendulum system Time-varying gain
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Adaptive backstepping control for levitation system with load uncertainties and external disturbances 被引量:6
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作者 李金辉 李杰 +1 位作者 余佩倡 王连春 《Journal of Central South University》 SCIE EI CAS 2014年第12期4478-4488,共11页
To explore the precise dynamic response of the levitation system with active controller, a maglev guide way-electromagnet-air spring-cabin coupled model is derived firstly. Based on the mathematical model, it shows th... To explore the precise dynamic response of the levitation system with active controller, a maglev guide way-electromagnet-air spring-cabin coupled model is derived firstly. Based on the mathematical model, it shows that the inherent nonlinearity, inner coupling, misalignments between the sensors and actuators, load uncertainties and external disturbances are the main issues that should be solved in engineering. Under the assumptions that the loads and external disturbance are measurable, the backstepping module controller developed in this work can tackle the above problems effectively. In reality, the load is uncertain due to the additions of luggage and passengers, which will degrade the dynamic performance. A load estimation algorithm is introduced to track the actual load asymptotically and eliminate its influence by tuning the parameters of controller online. Furthermore,considering the external disturbances generated by crosswind, pulling motor and air springs, the extended state observer is employed to estimate and suppress the external disturbance. Finally, results of numerical simulations illustrating closed-loop performance are provided. 展开更多
关键词 maglev backstepping control nonlinearity mass variation adaptiveness extended state observer
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Fault ride-through capability improvement in a DFIG-based wind turbine using modified ADRC 被引量:2
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作者 Seyed Reza Mosayyebi Seyed Hamid Shahalami Hamed Mojallali 《Protection and Control of Modern Power Systems》 2022年第1期751-787,共37页
In this paper,an overview of several strategies for fault ride-through(FRT)capability improvement of a doubly-fed induction generator(DFIG)-based wind turbine is presented.Uncertainties and parameter variations have a... In this paper,an overview of several strategies for fault ride-through(FRT)capability improvement of a doubly-fed induction generator(DFIG)-based wind turbine is presented.Uncertainties and parameter variations have adverse effects on the performance of these strategies.It is desirable to use a control method that is robust to such distur-bances.Auto disturbance rejection control(ADRC)is one of the most common methods for eliminating the effects of disturbances.To improve the performance of the conventional ADRC,a modified ADRC is introduced that is more robust to disturbances and offers better responses.The non-derivability of the fal function used in the conventional ADRC degrades its efficiency,so the modified ADRC uses alternative functions that are derivable at all points,i.e.,the odd trigonometric and hyperbolic functions(arcsinh,arctan,and tanh).To improve the effciency of the proposed ADRC,fuzzy logic and fractional-order functions are used simultaneously.In fuzzy fractional-order ADRC(FFOADRC),all disturbances are evaluated using a nonlinear fractional-order extended state observer(NFESO).The performance of the suggested structure is investigated in MATLAB/Simulink.The simulation results show that during disturbances such as network voltage sag/swell,using the modified ADRCs leads to smaller fluctuations in stator flux amplitude and DC-link voltage,lower variations in DFIG velocity,and lower total harmonic distortion(THD)of the stator current.This demonstrates the superiority over conventional ADRC and a proportional-integral(PI)controller.Also,by chang-ing the crowbar resistance and using the modified ADRCs,the peak values of the waveforms(torque and currents)can be controlled at the moment of fault occurrence with no significant distortion. 展开更多
关键词 Doubly-fed induction generator(DFIG) Auto disturbance rejection control(ADRC) Fault ride-through(FRT) Wind turbine nonlinear fractional-order extended state observer(NFESO)
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