Robustness of PD control for transporting quadrotor with payload uncertainties

Pratama, Gilang Nugraha Putu; Masngut, Ibnu; Cahyadi, Adha Imam; Herdjunanto, Samiadji

doi:10.1109/icstc.2017.8011844

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…Ref. [15] presents a model and a proportional-derivative control of a quadrotor with payload uncertainties; the payload is also considered a disturbance. A mathematical model of the payload and quadrotor was presented in [16] through the Euler-Lagrange formulation.…”

Section: Introductionmentioning

confidence: 99%

A Predictive Control Strategy for Aerial Payload Transportation with an Unmanned Aerial Vehicle

et al. 2021

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This paper presents the results of a model-based predictive control (MPC) design for a quadrotor aerial vehicle with a suspended load. Unlike previous works, the controller takes into account the hanging payload dynamics, the dynamics in three-dimensional space, and the vehicle rotation, achieving a good balance between fast stabilization times and small swing angles. The mathematical model is based on the Euler–Lagrange formulation and considers the dynamics of the vehicle, the cable, and the load. Then, the mathematical model is represented as an input-affine system to obtain the linear model for the control design. A constrained MPC strategy was designed and compared with an unconstrained MPC and an algorithm from the literature for the case of study. The constraints to be considered include the limits on the swing angles and the quadrotor position. The constrained control algorithm was constructed to stabilize the aerial vehicle. It aims to track a trajectory reference while attenuating the load swing, considering a maximum swing range of ±10∘. Numerical simulations were carried out to validate the control strategy.

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Section: Introductionmentioning

confidence: 99%

A Predictive Control Strategy for Aerial Payload Transportation with an Unmanned Aerial Vehicle

et al. 2021

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“…Shastry et al [36] used a nonlinear adaptive control method to manipulate the automatic delivery system of a quad-rotor. Pratama et al [37] employed a PD controller to stabilize a quad-rotor in the mission of unknown payload transportation; the uncertain inertia perturbation from payloads was considered. In [38], a linear matrix inequality-based nonlinear adaptive robust control approach was proposed for the quad-rotor delivering packages with unknown masses in the presence of wind field.…”

Section: Introductionmentioning

confidence: 99%

Disturbance Attenuation Predictive Optimal Control for Quad-Rotor Transporting Unknown Varying Payload

et al. 2020

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Quad-rotor is very suitable for payload transportation due to the merits of high maneuverability and free hovering. However, the unknown varying payloads can cause negative influences that act in forms of persistent disturbances and sudden changes, damaging flight performance especially the attitude stability seriously. Targeting the persistent disturbances, an entirely novel disturbance estimator (DE) which can estimate non-smooth disturbances in a highly accurate manner for feedback compensation is proposed in this paper. To deal with the sudden changes from prescribed references and the payloads that may induce too large overshoots and input surging, a type of predictive optimal controller, which considers tracking errors and their changing rates of a class of linear multiple-input-multiple-output systems, is developed. Simulation results show that the system enhanced by the DE has better control performance than the ones enhanced by the commonly used extended state observer or nonlinear disturbance observer. Compared with the typical control approaches, the proposed control scheme enables the quad-rotor attitude system more stable performance and more ideal inputs on both persistent disturbance and sudden change resisting during payload transportation. INDEX TERMS Quad-rotor, payload transportation, disturbance attenuation, predictive optimal control.

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“…The automatic delivery system of a quad-rotor was developed using a nonlinear adaptive control method (Shastry et al, 2018). A proportional-derivative (PD) controller was employed to stabilize a quad-rotor in transportation of unknown payload (Pratama et al, 2017); the uncertain inertia perturbation from payloads was considered. However, package delivered may be not fixed in mass and size, especially in civil logistics field, thus the control of quad-rotors for such application needs to be more thoroughly studied.…”

Section: Introductionmentioning

confidence: 99%

An internal model frame-based disturbance attenuation control scheme for quad-rotors transporting unknown payloads

Wang

Shi

et al. 2019

Transactions of the Institute of Measurement and Control

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The path following control of quad-rotors transporting uncertain payloads, whose control system is featured with time-variation, high nonlinearity and strong couplings among state variables and inputs, is investigated. Targeting this, an internal model frame-based disturbance attenuation control scheme that requires only rough model information is proposed. Firstly, a novel disturbance estimator (DE) for general single-input-single-output (SISO) system is proposed. Secondly, the DE proposed is applied in transporting unknown payloads by quad-rotors, to estimate the disturbances caused by the payloads for feedback compensation. Lastly, the control scheme based on internal model frame is designed to further reduce the remaining disturbances from disturbance estimation error.

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Robustness of PD control for transporting quadrotor with payload uncertainties

Cited by 6 publications

References 15 publications

A Predictive Control Strategy for Aerial Payload Transportation with an Unmanned Aerial Vehicle

A Predictive Control Strategy for Aerial Payload Transportation with an Unmanned Aerial Vehicle

Disturbance Attenuation Predictive Optimal Control for Quad-Rotor Transporting Unknown Varying Payload

An internal model frame-based disturbance attenuation control scheme for quad-rotors transporting unknown payloads

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