Active disturbance rejection based load frequency control and voltage regulation in power systems

Dong, Lili; Mandali, Anusree; Morinec, A.G.; Zhao, Yang

doi:10.1007/s11768-018-8112-3

Cited by 9 publications

(4 citation statements)

References 25 publications

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“…As a system state variable, the noise value f (t) is susceptible to real-time estimation using the state estimation controller. The proposed ESO, in accordance with equation (33) as [26,27]:…”

Section: Extended State Observer (Eso)mentioning

confidence: 99%

“…ESO is capable of monitoring the system state variables x 1 and x 2 using z 1 and z 2 , respectively, when the parameters l 1 and l 2 are suitably selected [33]. If we assume that the amount f (.)…”

Section: Extended State Observer (Eso)mentioning

confidence: 99%

“…If we assume that the amount f (.) can be fully estimated by the state variable z 2 and the control rule is defined to run the system as follows [33]:…”

Section: Extended State Observer (Eso)mentioning

confidence: 99%

“…Therefore, the stability of the system can be guaranteed because Υ = −k d ẏtanh (z 2 ) ⩽ 0 since χ tanh (χ ) ⩾ tanh 2 (χ ) ⩾ 0 [33,34].…”

Section: Extended State Observer (Eso)mentioning

confidence: 99%

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Designing, modeling and controlling the angular bending of a foldable soft actuator without using a curvature sensor

My Nu,

Viet,

Truyen

2024

Smart Mater. Struct.

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Soft actuators have recently attracted considerable attention owing to their inherent flexibility and adaptability. Nevertheless, for a soft robot to successfully engage with its surroundings and perform tasks with optimal effectiveness, it encounters a range of obstacles, including the need for precise and skillful movement, the capacity to perceive its own position and motion, and the ability to effectively regulate its flexible structures. Researchers have developed techniques to integrate curvature sensors onto flexible devices, enabling them to detect and react to their positions. However, the integration of curvature sensors into flexible structures presents a substantial challenge in the structural manufacturing process. To address these concerns, this article presents a technique for designing, dynamic modeling, and controlling the bending angle of foldable soft actuator without the need for curvature sensors. An optimal design for the geometric dimensions of the soft structure utilizing origami concepts to guarantee the requisite bending properties is suggested. A model-based control method that considers both the motion dynamic and the air dynamic is proposed for controlling the angular bending of the actuator. The motion dynamic was developed using the constant volume principle of the elastomer material and the neo-Hookean hyperelastic theory to establish the correlation between the applied pressure and bending angle. This dynamic model incorporates both the hyperelastic material characteristics of silicone rubber and the geometry of the actuator. Soft actuators have variations in the air chamber's volume during operation, and accurately measuring this variation is challenging. In order to tackle this problem, the fuzzy active disturbance rejection controller is used to predict these variations. The controller possesses exceptional position-tracking capability. This control strategy exhibited excellent responsiveness throughout the range of steady-state error values from approximately 1o to 2o. Removing the curvature sensor increases the longevity of this soft actuator and promotes the efficiency of the manufacturing process, hence enhancing the practical application possibilities for the soft actuator made from super elastic material.

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Section: Extended State Observer (Eso)mentioning

confidence: 99%

Section: Extended State Observer (Eso)mentioning

confidence: 99%

“…If we assume that the amount f (.) can be fully estimated by the state variable z 2 and the control rule is defined to run the system as follows [33]:…”

Section: Extended State Observer (Eso)mentioning

confidence: 99%

“…Therefore, the stability of the system can be guaranteed because Υ = −k d ẏtanh (z 2 ) ⩽ 0 since χ tanh (χ ) ⩾ tanh 2 (χ ) ⩾ 0 [33,34].…”

Section: Extended State Observer (Eso)mentioning

confidence: 99%

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