Port-Hamiltonian Passivity-Based Control on SE(3) of a Fully Actuated UAV for Aerial Physical Interaction Near-Hovering

Rashad, Ramy; Califano, Federico

doi:10.1109/lra.2019.2932864

Cited by 43 publications

(44 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While the Hamiltonian function, H(q, p), is the same as (9), the system dynamics do not satisfy (10) due to the SE(3) kinematics constraints in (15). However, the dynamics can be specified in a Port-Hamiltonian form [17,12,31] as in (11) with inter-connection matrix:…”

Section: Hamiltonian Dynamics On the Se(3) Manifoldmentioning

confidence: 99%

Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control

Duong

Atanasov

2021

Robotics: Science and Systems XVII

View full text Add to dashboard Cite

Accurate models of robot dynamics are critical for safe and stable control and generalization to novel operational conditions. Hand-designed models, however, may be insufficiently accurate, even after careful parameter tuning. This motivates the use of machine learning techniques to approximate the robot dynamics over a training set of state-control trajectories. The dynamics of many robots, including ground, aerial, and underwater vehicles, are described in terms of their SE(3) pose and generalized velocity, and satisfy conservation of energy principles. This paper proposes a Hamiltonian formulation over the SE(3) manifold of the structure of a neural ordinary differential equation (ODE) network to approximate the dynamics of a rigid body. In contrast to a black-box ODE network, our formulation guarantees total energy conservation by construction. We develop energy shaping and damping injection control for the learned, potentially under-actuated SE(3) Hamiltonian dynamics to enable a unified approach for stabilization and trajectory tracking with various platforms, including pendulum, rigid-body, and quadrotor systems.

show abstract

Section: Hamiltonian Dynamics On the Se(3) Manifoldmentioning

confidence: 99%

Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control

Duong

Atanasov

2021

Robotics: Science and Systems XVII

View full text Add to dashboard Cite

show abstract

“…( ) (8) has nontrivial, periodic solutions ⋆ (t) = ⋆ (t + T), ∀t ≥ 0, which are parameterized by the initial conditions (0).…”

Section: A1 (Target Oscillator) the Dynamical Systeṁ=mentioning

confidence: 99%

“…Alternatively, using a body‐fixed coordinate, in which A ( q ) is a rotation matrix in SO ( n ), 2 may yield a constant inertia matrix M under additional assumptions. See Reference 8 for the port‐Hamiltonian modeling on SE (3).…”

Section: Problem Formulation and Classical Approachmentioning

confidence: 99%

See 1 more Smart Citation

Path following of a class of underactuated mechanical systems via immersion and invariance‐based orbital stabilization

Manchester

Siguerdidjane

2020

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

This article aims to provide a new problem formulation of path following for mechanical systems without time parameterization nor guidance laws, namely, we express the control objective as an orbital stabilization problem. It is shown that it is possible to adapt the immersion and invariance technique to design static state-feedback controllers that solve this problem. In particular, we select the target dynamics adopting the recently introduced Mexican sombrero energy assignment method. To demonstrate the effectiveness of the proposed method we apply it to control underactuated marine surface vessels. K E Y W O R D S energy shaping, immersion and invariance, orbital stabilization, path following 1 INTRODUCTION Tracking a geometric path in mechanical systems is a task often encountered in various application fields, for example, robotics, aerospace, and autonomous vehicles. There are two approaches to formulate this problem, depending on whether the predefined path is parameterized by time or not, and they are known as trajectory tracking or path following, respectively. The former approach largely dominates the control literature and it has been extensively studied. Unfortunately, for nonminimum phase systems, which are common in these applications, the achievable performance has a fundamental limitation, first identified in Reference 1. It is possible to circumvent this limitation removing the time

show abstract

“…Several novel fully-actuated MAVs have been designed as solutions to the underactuated problem with conventional MAVs (Crowther et al, 2011; Rashad et al, 2019; Ryll et al, 2015, 2016). Due to the ability to generate forces and torques in all directions, fully-actuated MAVs has omnidirectional flight capabilities and improved maneuverability.…”

Section: Introductionmentioning

confidence: 99%

Extended state observer-based robust control of an omnidirectional quadrotor with tiltable rotors

Yang

Liao

et al. 2020

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

A quadrotor with tiltable rotors is a kind of omnidirectional multirotor aerial vehicle (MAV) that has demonstrated advantages of decoupling control of position from the control of orientation. However, quadrotors with tiltable rotors usually suffer from Coriolis term, modeling error and external disturbance. To this end, the extended state observer (ESO)-based controller is designed to estimate and compensate for the above adverse effects. Especially, the controller involves position and attitude controller in parallel. The attitude controller is made up of cascade control-loops: an outer quaternion-based attitude control-loop and an inner ESO-based Proportional derivative angular velocity control-loop. Similarly, the position controller consists of an outer proportional position control-loop and an inner ESO-based PD velocity control-loop. Besides, a linear control allocation strategy, which allocates the controller outputs to tilting angles and motor speed directly, is proposed to avoid the nonlinear allocation matrix. Extensive simulations and flight tests are carried out to illustrate the effectiveness and robustness of the proposed ESO-based controller.

show abstract

Port-Hamiltonian Passivity-Based Control on SE(3) of a Fully Actuated UAV for Aerial Physical Interaction Near-Hovering

Cited by 43 publications

References 20 publications

Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control

Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control

Path following of a class of underactuated mechanical systems via immersion and invariance‐based orbital stabilization

Extended state observer-based robust control of an omnidirectional quadrotor with tiltable rotors

Contact Info

Product

Resources

About