Active Model-Based Control for Pneumatic Artificial Muscle

In this paper, a simple active-model-based control scheme is developed for the quadrotor slung load (QSL) system. The scheme works to improve the rejection of the influences caused by the abruptly changed load as a complementary enhancement while maintaining the structure and parameters of the original controller. A linearized model is first constructed with respect to the hovering state of a quadrotor. Modeling error is then introduced to describe the uncertainties caused by the load change and the simplified model. The modeling error is actively estimated by a Kalman filter (KF), while the estimation is further integrated into a normal controller, to enhance its performance of disturbance rejection. Experiments are conducted on a quadrotor controlled by the Pixhawk, which is one of the most popular controllers commercially available on the market. The improvements of the proposed scheme are shown by the comparisons between the controls with and without the active-model-based enhancement. The experiments also indicate that, with its simple structure and less computational algorithm, this active-model-based enhancement would be a feasible approach to enhance the commercial UAV controller to handle more uncertainties.

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“…In order to describe the disturbances caused by the slung load, we define the modeling error as [25,26]:…”

Section: Modeling Error and Its Estimationmentioning

confidence: 99%

“…represent related mean, covariance and variance values, respectively. Then, the KF estimator can be described as [26]:…”

Section: Modeling Error and Its Estimationmentioning

confidence: 99%

Active-Model-Based Control for the Quadrotor Carrying a Changed Slung Load

Yao

Liang

et al. 2019

Electronics

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“…(6), there are a wide variety of errors. To compensate for the uncertainties and unmodeled parts in the dynamic model of the QSL system, we employ a practical KF-based active modeling method [16], [17].…”

Section: Kf-based Active Modelingmentioning

confidence: 99%

“…In order to describe the disturbances caused by the slung load, we define the modeling error as [16], [17]:…”

Section: Kf-based Active Modelingmentioning

confidence: 99%

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KF-Based Active Modeling for a Quadrotor Slung Load System

Liang

et al. 2018

2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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The control of quadrotor slung load (QSL) systems is a challenging task as a result of some distinguishing features like the multiple nonlinearities and time-varying load swing. To design an effective control scheme, the top priority is to establish an excellent model. In this paper, a practical active modeling method based on the Kalman Filter (KF) for a QSL system is developed to compensate uncertainties in system dynamics. First, a simplified quadrotor model is formulated as the reference model. Second, modeling errors are introduced and the KF is adopted to actively estimate the errors involved in the reference model. Finally, some experiments are conducted to validate the remarkable performance of the proposed modeling strategy in terms of uncertainties as well as disturbances in the dynamic model.

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