Aerial Manipulator Pushing a Movable Structure Using a DOB-Based Robust Controller

Lee, Dongjae; Seo, Hoseong; Jang, Inkyu; Lee, Seung-Jae; Kim, H. Jin

doi:10.1109/lra.2020.3047779

Cited by 42 publications

(18 citation statements)

References 24 publications

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“…q d and 𝝎 d are the reference attitude and angular velocity, respectively. The quaternion error q e = [ q e0 q ev ] T is calculated by (3). Then, the attitude control law is designed as…”

Section: Mstsmic For Attitude Subsystemmentioning

confidence: 99%

“…Unmanned aerial vehicles (UAVs) have attracted more and more attentions in recent years for their wide ranges of applications including pesticide spraying, aerial photography, environmental monitoring, mapping, search and rescue and so forth. In addition to these non‐contact tasks, there are increasing demands on the contact‐based tasks such as industrial non‐destructive testing (NDT) contact‐based inspection, 1 window or wall cleaning, 2 aerial manipulations with continuous forces 3 and so on. For these aerial physical interaction tasks where robots are required to interact with the environment, not only the motion control but also force control should be considered.…”

Section: Introductionmentioning

confidence: 99%

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Robust fault‐tolerant motion/force control of a fully‐actuated hexarotor using adaptive sliding mode impedance control

Rong

Jiao

2022

Intl J Robust & Nonlinear

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A robust fault-tolerant motion/force controller is vital for a fully-actuated unmanned aerial vehicle performing contact-based aerial manipulation tasks (e.g., window cleaning, infrastructure inspection and so on) in the presence of lumped disturbances (including external disturbances, model uncertainties, and actuator faults) with unknown boundaries. To address this problem, a disturbance-observer-based adaptive sliding mode impedance controller is proposed in this article. Using the impedance model as a reference model, the sliding mode impedance controller is constructed with a modified adaptive super-twisting gain technique to reduce the chattering phenomenon. To avoid overestimation of the unknown disturbance bounds, a novel adaptive third-order extended state observer is developed to actively estimate the disturbances and compensate for the controller without any priori knowledge of the disturbance bounds. Convergence of the proposed observer and stability of the closed-loop system are analyzed by the Lyapunov method. The effects of parameters in observers and controllers are presented under measurement noise through numerical simulations. In addition, guidelines for parameter selections are also provided. The advantages and effectiveness of the proposed control strategy are also demonstrated by simulating a push-and-slide scenario in the presence of lumped disturbances.

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Section: Mstsmic For Attitude Subsystemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust fault‐tolerant motion/force control of a fully‐actuated hexarotor using adaptive sliding mode impedance control

Rong

Jiao

2022

Intl J Robust & Nonlinear

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“…Along this line, recent works proposed both model-based and traditional motionplanning approaches. In [12] a Model Predictive Control (MPC) framework has been presented to open a hinged door, while in [13] the task of pushing a cart has been approached by dynamically updating the aerial robot position reference. While these approaches are able to tackle interaction tasks with dynamic environments in very structured conditions, no guarantees about their safety nor their robustness against model uncertainties and external disturbances can be made.…”

Section: Introductionmentioning

confidence: 99%

Energy Tank-Based Policies for Robust Aerial Physical Interaction with Moving Objects

Brunner¹,

Livio²,

Siegwart³

et al. 2022

Preprint

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Although manipulation capabilities of aerial robots greatly improved in the last decade, only few works addressed the problem of aerial physical interaction with dynamic environments, proposing strongly model-based approaches. However, in real scenarios, modeling the environment with high accuracy is often impossible. In this work we aim at developing a control framework for Omnidirectional Micro Aerial Vehicles (OMAVs) for reliable physical interaction tasks with articulated and movable objects in the presence of possibly unforeseen disturbances, and without relying on an accurate model of the environment. Inspired by previous applications of energy-based controllers for physical interaction, we propose a passivity-based impedance and wrench tracking controller in combination with a momentum-based wrench estimator. This is combined with an energy-tank framework to guarantee the stability of the system, while energy and power flow-based adaptation policies are deployed to enable safe interaction with any type of passive environment. The control framework provides formal guarantees of stability, which is validated in practice considering the challenging task of pushing a cart of unknown mass, moving on a surface of unknown friction, as well as subjected to unknown disturbances. For this scenario, we present, evaluate and discuss three different policies.

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“…As a result, an adaptation to heterogeneous surface is not possible. One could also employ disturbance observer-based robust control [11]. Such an approach has the disadvantage of being slow to react, especially in the presence of noisy measurements and inaccurate process models.…”

Section: Introductionmentioning

confidence: 99%

Learning Variable Impedance Control for Aerial Sliding on Uneven Heterogeneous Surfaces by Proprioceptive and Tactile Sensing

Zhang

Ott

Tognon

et al. 2022

IEEE Robot. Autom. Lett.

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The recent development of novel aerial vehicles capable of physically interacting with the environment leads to new applications such as contact-based inspection. These tasks require the robotic system to exchange forces with partially-known environments, which may contain uncertainties including unknown spatially-varying friction properties and discontinuous variations of the surface geometry. Finding a control strategy that is robust against these environmental uncertainties remains an open challenge. This paper presents a learning-based adaptive control strategy for aerial sliding tasks. In particular, the gains of a standard impedance controller are adjusted in real-time by a policy based on the current control signals, proprioceptive measurements, and tactile sensing. This policy is trained in simulation with simplified actuator dynamics in a student-teacher learning setup. The real-world performance of the proposed approach is verified using a tiltarm omnidirectional flying vehicle. The proposed controller structure combines data-driven and model-based control methods, enabling our approach to successfully transfer directly and without adaptation from simulation to the real platform. Compared to fine-tuned state of the art interaction control methods we achieve reduced tracking error and improved disturbance rejection.

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Aerial Manipulator Pushing a Movable Structure Using a DOB-Based Robust Controller

Cited by 42 publications

References 24 publications

Robust fault‐tolerant motion/force control of a fully‐actuated hexarotor using adaptive sliding mode impedance control

Robust fault‐tolerant motion/force control of a fully‐actuated hexarotor using adaptive sliding mode impedance control

Energy Tank-Based Policies for Robust Aerial Physical Interaction with Moving Objects

Learning Variable Impedance Control for Aerial Sliding on Uneven Heterogeneous Surfaces by Proprioceptive and Tactile Sensing

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