Comments on Active Disturbance Rejection Control

Sun, Dong

doi:10.1109/tie.2007.909047

Cited by 147 publications

(6 citation statements)

References 7 publications

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“…The disturbance observer, the perturbation observer, the equivalent input disturbance-based estimation, and the extended state observer (ESO) are similar methods presented until this point [ 23 ]. According to [ 24 , 25 ], the ESO requires the smallest amount of system information. Moreover, it also allows for estimating the total disturbance, which considers internal model uncertainties, unmodeled dynamics, parasitic resistance, the tolerant drift of the inductor or capacitor, and external noises such as electromagnetic interference produced by the semiconductor switching action during operation.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Extended State Observer for the Dual Active Bridge Converters

Duong,

Trinh,

Ahn

et al. 2024

Sensors

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The DC–DC dual active bridge (DAB) converter has become one of the essential units for bidirectional energy distribution and connecting various renewable energy sources. When it comes to regulating the converter’s output voltage, integrating an extended state observer (ESO) offers the advantage of eliminating the need for a current sensor, thereby reducing system costs. The ESO with a high observer bandwidth tends to acquire a faster system convergence and greater tracking accuracy. However, its disturbance suppression performance will become poor compared to the ESO with a low observer bandwidth. Based on this, the adaptive ESO (AESO) is proposed in this study to make a compromise between tracking performance and disturbance suppression. When the system is subjected to a high voltage error, the observer bandwidth will increase to improve the tracking performance and decrease to enhance the disturbance suppression. In order to demonstrate that the proposed method is effective, it is compared to the ESO with a fixed observer bandwidth and the improved model-based phase-shift control (MPSC). These comparisons are made through simulation and experimental results in various operation scenarios.

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Section: Introductionmentioning

confidence: 99%

Adaptive Extended State Observer for the Dual Active Bridge Converters

Duong,

Trinh,

Ahn

et al. 2024

Sensors

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“…The characteristic feature of the ADRC is to use the extended state observer, which not only approximates the system states, but the extended states are defined as lumped disturbances. Thus, the internal disturbances and parameter uncertainties caused by the unmodeled dynamics are lumped together as an extended state, which can be estimated with ESO and compensated using a PD control law [25]. The ESO operates independently of the mathematical model of disturbances, demanding only a modest detail of the controlled process for its implementation [26].…”

Section: Introductionmentioning

confidence: 99%

Frequency Regulation in Interconnected Power System Through Enhanced Beluga Whale Optimized Flatness-Based Active Disturbance Rejection Control

Ali,

Xia,

Khan

et al. 2024

IEEE Access

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Ever-increasing dynamic surges in renewable-based electric power systems, notably wind and photovoltaic farms bring adverse impacts and challenges in terms of reliability and stability. The intermittency of renewable sources imposes significant deviations in frequency due to variations in demand. Wind power induces instability in the grid due to its vulnerable nature, and reduction in system inertia. To mitigate these dynamics issues, an optimal control technique based on flatness-based Active disturbance rejection control (FADRC) and utilizing an enhanced Beluga Whale optimization algorithm (EBWO) for a multi-area interconnected power system with photovoltaic generation. The proposed LFC model addresses the load perturbation and the deviation of tie-line power, with system uncertainties considered as lumped disturbances that are approximated by extended state observers. To achieve optimal performance, the Enhanced Beluga Whale optimization algorithm is adopted and integrated with the suggested controller to fine-tune the controller. To validate the formidable performance of the suggested scheme, different cases have been studied with the existing approaches. The simulation results reveal the supremacy and robustnessThis article has been accepted for publication in IEEE Access.

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“…Any system uncertainties, such as exogenous disruptions, unmodelled dynamics, and parameter perturbations, have a significant impact on the performance of a control system. The development of the any controller that attempts to fulfil these objectives while also assuring disturbance rejection and strong tracking performance in the face of huge uncertainty is complicated [14]. Because of this, anti-disturbance approaches are used for both external and internal loop controllers and estimators have been widely employed [15].…”

Section: Introductionmentioning

confidence: 99%

Fractional multi-loop active disturbance rejection control for a lower knee exoskeleton system

2023

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Rehabilitation Exoskeleton is becoming more and more important in physiotherapists’ routine work. To improve the treatment performance, such as reducing the recovery period and/or monitoring and reacting to unpredictable situations, the rehabilitation manipulators need to help the patients in various physical trainings. A special case of the active disturbance rejection control (ADRC) is applied to govern a proper realisation of basic limb rehabilitation trainings. The experimental study is performed on a model of a flexible joint manipulator, whose behaviour resembles a real exoskeleton rehabilitation device (a one-degree-of-freedom, rigid-link, flexible-joint manipulator). The fractional (FADRC) is an unconventional model-independent approach, acknowledged as an effective controller in the existence of total plant uncertainties, and these uncertainties are inclusive of the total disturbances and unknown dynamics of the plant. In this work, three FADRC schemes are used, the first one using a fractional state observer (FSO), or FADRC1, second one using a fractional proportional-derivative controller (FPD), or FADRC2, and the third one a Multi-loop fractional in PD-loop controller and the observer-loop (Feedforward and Feedback), or FADRC3. The simulated Exoskeleton system is subjected to a noise disturbance and the FADRC3 shows the effectiveness to compensate all these effects and satisfies the desired position when compared with FADRC1 and FADRC2. The design and simulation were carried out in MATLAB/Simulink.

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Comments on Active Disturbance Rejection Control

Cited by 147 publications

References 7 publications

Adaptive Extended State Observer for the Dual Active Bridge Converters

Adaptive Extended State Observer for the Dual Active Bridge Converters

Frequency Regulation in Interconnected Power System Through Enhanced Beluga Whale Optimized Flatness-Based Active Disturbance Rejection Control

Fractional multi-loop active disturbance rejection control for a lower knee exoskeleton system

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