A disturbance observer approach with online Q-filter tuning for position control of quadcopters

Mishra, Shatadal; Zhang, Wenlong

doi:10.23919/acc.2017.7963503

Cited by 10 publications

(5 citation statements)

References 14 publications

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“…In order to overcome this, traditional control plus DOB method is used to improve disturbance suppression performance. [20][21][22] The key idea of DOB is to reject lumped disturbances including both action from external environment and parameter variation using its estimation computed by error between control input and output of inverted nominal model so as to provide higher robustness. Thus, DOB includes the nominal model of the plant and low pass filter, so called Q-filter, to attenuate the estimation noise.…”

Section: Robust Attitude Control Using Dobmentioning

confidence: 99%

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Robust attitude control of a quadrotor unmanned aerial vehicles using optimized disturbance observer

Kim¹,

Paek²,

Yong‐Sim³

et al. 2022

Optim Control Appl Methods

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This article proposes a method of realizing robust attitude stabilization control of a quadrotor UAV using the disturbance observer (DOB), where Q-filter is optimally designed considering performances of attenuating disturbance and estimation noise, and robust stability against parameter variations and sampling duration delay due to discrete time control in microcontroller. Disturbance rejection performance mainly depends on the bandwidth determined by cut-off frequency of DOB's Q-filter. However, extension of the Q-filter's bandwidth must be restricted due to the requirement of robust stability against parameter uncertainty and sampling duration delay in onboard microcontroller for flight control of quadrotor. In order to optimize the disturbance rejection performance under the restriction of bandwidth, an H ∞ norm minimization problem is defined considering the frequency bands of disturbance and noise. The optimization problem is transformed into standard H ∞ control design problem by pseudo loop factorization method so as to resolve the structural constraint of Q-filter. Then, stability of the discrete time system configured by digital controller is tested in relation with the time constant of the designed DOB's Q-filter under different inertia moment values. The comparisons between simulation results by different control methods validate that the robust control method using proposed DOB provides a better performance of disturbance attenuation than that using conventional DOB.

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Section: Robust Attitude Control Using Dobmentioning

confidence: 99%

“…On the other hand, DOB has been applied to attitude and trajectory tracking control of quadrotor, improving the control performances. [20][21][22] In particular, 22 proposed a method by which Q-filter of DOB is tuned to deal with both drift and noise from the onboard sensors.…”

Section: Introductionmentioning

confidence: 99%

Robust attitude control of a quadrotor unmanned aerial vehicles using optimized disturbance observer

Kim¹,

Paek²,

Yong‐Sim³

et al. 2022

Optim Control Appl Methods

View full text Add to dashboard Cite

show abstract

“…Based on the dynamic relationship between h of the target UAV and the ones (h k , k ∈ Ω) of the training UAVs in Eq. (19), the convergence of the learning algorithm in Eq. (16) for heterogeneous UAVs can be obtained as…”

Section: Formulationmentioning

confidence: 99%

“…Among others, the trajectory generation and tracking algorithms enable UAVs to accurately and aggressively maneuver in a cluttered and indoor environment with reduced uncertainties (e.g., [17]). The disturbance observer for UAVs estimates and compensates large unknown external disturbances exerted on UAVs and enhances the robustness of UAV's attitude control (e.g., [18,19]). The feedforward techniques provide additional flexibility to further enhance the UAV's tracking performance.…”

Section: Introductionmentioning

confidence: 99%

A Preliminary Study on a Physical Model Oriented Learning Algorithm With Application to UAVs

Zheng

Zhu

Liang

2019

Volume 3, Rapid Fire Interactive Presentations: Advances in Control Systems; Advances in Robotics and Mechatronics; Automotive

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This paper provides a preliminary study for an efficient learning algorithm by reasoning the error from first principle physics to generate learning signals in near real time. Motivated by iterative learning control (ILC), this learning algorithm is applied to the feedforward control loop of the unmanned aerial vehicles (UAVs), enabling the learning from errors made by other UAVs with different dynamics or flying in different scenarios. This learning framework improves the data utilization efficiency and learning reliability via analytically incorporating the physical model mapping, and enhances the flexibility of the model-based methodology with equipping it with the selflearning capability. Numerical studies are performed to validate the proposed learning algorithm.

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“…Real-life missions, however, can be enduring, and include flights through cluttered and unpredictable spaces, such as forests or populated urban areas (Liu et al 2017). With a large area of research already established on guaranteeing robust flights in structured environments, (Raffo et al 2010;Mishra and Zhang 2017), researchers are now attempting to achieve robust flights in complex and dynamic environments (Spasojevic et al 2020). Obstacle avoidance algorithms are developed in this context which ensure that the vehicle remains in pre-defined safety corridors at all times (Falanga et al 2018a;Ames et al 2019;Richter et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Towards reconfigurable and flexible multirotors

Patnaik

Zhang

2021

Int J Intell Robot Appl

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Reconfigurable multirotors (RMs) with flexible frames and integrated mechanical compliance are increasingly explored to develop multifunctional aerial robots with high power-to-weight ratios. In this paper, we review the state-of-the-art research on RMs and classify them into three broad categories as tiltrotors, multimodal and foldable RMs. The RMs with the ability to arbitrarily orient their thrust by employing tilting rotors are classified as tiltrotors, the multirotors with multi modal locomotion capabilities by transforming the chassis into land/water propulsion systems are classified as multimodal RMs, and those which can alter the size of the chassis are labelled as foldable RMs. Existing platforms are analyzed from three different perspectives-mechanical design, challenges of low-level control and high level motion planning techniques. Finally, we identify the critical parameters for design optimization, and discuss the issues associated with developing a common control and motion planning algorithm that can leverage any RM's features to demonstrate successful autonomous missions.in body frame i i th Rotor's spin velocity N ∈ ℝ n Vector of squared rotor spin velocities k i ∈ ℝ Constant to calculate torque generated by ith motor ∈ ℝ Event trigger i ∈ 1 i th Rotor tilt angle i ∈ 1 i th Arm folding angle A ∈ ℝ 3×3 Control allocation matrix A g ∈ ℝ 3×3 Control allocation matrix in ground mode Abbreviations LLFC Low-level flight controller HLMP High-level motion plannning MoCap Indoor motion capture system * Karishma Patnaik

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A disturbance observer approach with online Q-filter tuning for position control of quadcopters

Cited by 10 publications

References 14 publications

Robust attitude control of a quadrotor unmanned aerial vehicles using optimized disturbance observer

Robust attitude control of a quadrotor unmanned aerial vehicles using optimized disturbance observer

A Preliminary Study on a Physical Model Oriented Learning Algorithm With Application to UAVs

Towards reconfigurable and flexible multirotors

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