ADRC in output and error form: connection, equivalence, performance

Madoński, Rafał; Herbst, Gernot; Stanković, Momir

doi:10.1007/s11768-023-00129-y

Cited by 15 publications

(3 citation statements)

References 25 publications

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“…The NLADRC control technique unifies the internal uncertainty perturbations, the internal unmodeled part, and the external perturbations of the system as internal and external perturbations, and estimates and feed-forward compensates them with the input and output data of the controlled object [19]. This compensation technique coincides with the robustness requirements of the UAV flight control system.…”

Section: A Inner-loop Nladrc Attitude Controlmentioning

confidence: 99%

Disturbance feedforward compensation based on Modelica modeling with the Self-immunity study of a quadrotor UAV

ZHOU,

BAO,

LIANG

et al. 2023

Preprint

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Due to the nonlinearity, underactuated, strong coupling, multi-data flow direction, and vulnerability to internal and external disturbances, the widely used PID control technology has been very difficult. Some emerging nonlinear control theory algorithms have good simulation performance, but they rely heavily on exact mathematical models, require a large amount of computation, and take a long time to solve. Therefore, based on the advantages of non-causal declarative modeling in Modelica language, this paper focuses on the analysis and implementation of the mechanism of observation, estimation, and compensation of nonlinear ADRC disturbances, PID-NLADRC linear nonlinear combined control strategy is designed to realize the flight control of a four-rotor UAV. The multi-target applications, such as fixed-point hover, cone spiral trajectory tracking and air obstacle avoidance fillet moment trajectory tracking, are verified respectively. The simulation results show that the designed PID-NLADRC control strategy has better performance than the Cascade PID control strategy in suppressing the fixed-point hover disturbance, it is fully verified that the PID-NLADRC control strategy has a smaller Euclidean range deviation and stronger disturbance rejection ability for tracking trajectories with different properties such as plane or space, smooth or transient, it can better meet the requirements of fast, accurate and robust flight control of four-rotor UAV.

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Section: A Inner-loop Nladrc Attitude Controlmentioning

confidence: 99%

Disturbance feedforward compensation based on Modelica modeling with the Self-immunity study of a quadrotor UAV

ZHOU,

BAO,

LIANG

et al. 2023

Preprint

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show abstract

“…A crucial component of ADRC is the extended state observer (ESO), which demonstrates remarkable interference and noise suppression capabilities [3][4]. The ADRC not only has faster response and smaller overshoot than PID control, but also effectively attenuates unknown disturbances, exhibits excellent adaptability to external disturbances, and ensures system stability [5].…”

Section: Introductionmentioning

confidence: 99%

Wind Power Virtual Inertia Frequency Modulation Control Based on Intelligent Swarm Auto-Disturbance Rejection

Li,

Yao

et al. 2024

Advances in Transdisciplinary Engineering

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This paper proposes a wind power virtual inertia control strategy based on the intelligent bee colony active disturbance rejection controller (ABC-ADRC). The proposed control method aims to improve the anti-interference capability of the virtual inertia control system and solve the challenge of tuning the parameters of the active disturbance rejection control (ADRC).The control strategy is developed based on the system frequency dynamic response equation. An active disturbance rejection controller is designed using a nonlinear feedback control law and an extended state observer. This controller aims to enhance the system’s ability to reject disturbances. However, tuning the parameters of the ADRC can be difficult. To address this issue, an ABC algorithm based on nectar collection behavior is proposed to iteratively optimize the ADRC parameters. The proposed control method is verified through simulations using Matlab/Simulink. The results demonstrate that the ABC-ADRC control strategy is more effective compared to traditional algorithms. It successfully controls the rotor speed to adjust the frequency of the power grid system, achieves power sharing, and eliminates adaptive noise. Furthermore, this method reduces the complexity of parameter settings, improves anti-interference ability, and enhances the robustness of the system.

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“…The structure of the HO-AR controllers used is based on the IPDT process model [17,35]. Therefore, the step response of the given non-minimum phase process is directly approximated by the IPDT model, which greatly simplifies the experimental identification of the process; • Thanks to the nature of such an IPDT approximation of the measured step response and the controller design used, the whole procedure can be described as a generalization of the Ziegler and Nichols method [12] for HO-ARCs and inverse response processes; • By using ultra-local integral models, the presented controller design is similar to some alternative approaches, such as active disturbance rejection control (ADRC) [36][37][38][39][40][41][42] or model-free control (MFC) [43,44], which differ mainly in the type of disturbance observer used;…”

mentioning

confidence: 99%

Practice-Oriented Controller Design for an Inverse-Response Process: Heuristic Optimization versus Model-Based Approach

Bistak,

Huba,

Vrancic

2024

Applied Sciences

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The proposed practice-oriented controller design (POCD) aims at stabilizing the system, reconstructing and compensating for disturbances while achieving fast and smooth step responses. This is achieved through a simple approach to process identification and controller tuning that takes into account control signal constraints and measurement noise. The proposed method utilizes POCD by eliminating the influence of the unstable zero dynamics of the inverse-response processes, which limits the achievable performance. It extends the previous work on PI and PID controllers to higher-order (HO) automatic reset controllers (ARCs) with low-pass filters. It is also extended according to POCD requirements while maintaining the simplified process model. The final result is an extremely simple design for a constrained controller that provides sufficiently smooth and robust responses to a wide family of HO-ARCs with odd derivatives, designed using integral plus dead time (IPDT) models and tuned by the multiple real dominant pole method (MRDP) and the circle criterion of absolute stability. The proposed design can be considered as a generalization of the Ziegler and Nichols step response method for inverse response processes and HO-ARCs.

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ADRC in output and error form: connection, equivalence, performance

Cited by 15 publications

References 25 publications

Disturbance feedforward compensation based on Modelica modeling with the Self-immunity study of a quadrotor UAV

Disturbance feedforward compensation based on Modelica modeling with the Self-immunity study of a quadrotor UAV

Wind Power Virtual Inertia Frequency Modulation Control Based on Intelligent Swarm Auto-Disturbance Rejection

Practice-Oriented Controller Design for an Inverse-Response Process: Heuristic Optimization versus Model-Based Approach

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