Real-time implementation of decentralized adaptive formation control on multi-quadrotor systems

Koksal, Nasrettin; Fi̇dan, Barış; Büyükkabasakal, Kemal

doi:10.1109/ecc.2015.7331020

Cited by 8 publications

(2 citation statements)

References 10 publications

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“…We employ an LS based PI scheme to estimate the uncertain inertial parameters. From the attitude dynamics Equation (1), following the procedure in [44,45], we first define a linear parametric model avoiding need for signal differentiation and the associated noise sensitivity issue by use of the stable filter 1(s+λ),3.33333ptλ>0, as follows:zφ=θφ*sans-serifΦφ,zφ=s(s+λ)φ˙,θφ*=1Jφ,sans-serifΦφ=2lKb(s+λ)(s+b)uφ, noting that the Euler rate φ˙ (obtained using the IMU and the filters in Section 3) and the control signal uφ are measurable, and l,K,b are known constant parameters.…”

Section: Adaptive Optimal Attitude Tracking Control Designmentioning

confidence: 99%

Adaptive Linear Quadratic Attitude Tracking Control of a Quadrotor UAV Based on IMU Sensor Data Fusion

Koksal

Jalalmaab

Fi̇dan

2018

Sensors

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In this paper, an infinite-horizon adaptive linear quadratic tracking (ALQT) control scheme is designed for optimal attitude tracking of a quadrotor unmanned aerial vehicle (UAV). The proposed control scheme is experimentally validated in the presence of real-world uncertainties in quadrotor system parameters and sensor measurement. The designed control scheme guarantees asymptotic stability of the close-loop system with the help of complete controllability of the attitude dynamics in applying optimal control signals. To achieve robustness against parametric uncertainties, the optimal tracking solution is combined with an online least squares based parameter identification scheme to estimate the instantaneous inertia of the quadrotor. Sensor measurement noises are also taken into account for the on-board Inertia Measurement Unit (IMU) sensors. To improve controller performance in the presence of sensor measurement noises, two sensor fusion techniques are employed, one based on Kalman filtering and the other based on complementary filtering. The ALQT controller performance is compared for the use of these two sensor fusion techniques, and it is concluded that the Kalman filter based approach provides less mean-square estimation error, better attitude estimation, and better attitude control performance.

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Section: Adaptive Optimal Attitude Tracking Control Designmentioning

confidence: 99%

Adaptive Linear Quadratic Attitude Tracking Control of a Quadrotor UAV Based on IMU Sensor Data Fusion

Koksal

Jalalmaab

Fi̇dan

2018

Sensors

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“…2) Continuous Control Law: By employing the aforementioned ideas regarding DNFs and given that A i (p i (t 0 )) for some t 0 ≥ 0, we propose a decentralized control law u i for the transition π k → i π k , as defined in Def. 3.…”

Section: A Continuous Control Designmentioning

confidence: 99%

Decentralized motion planning with collision avoidance for a team of UAVs under high level goals

Verginis

Dimarogonas

2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-This paper addresses the motion planning problem for a team of aerial agents under high level goals. We propose a hybrid control strategy that guarantees the accomplishment of each agent's local goal specification, which is given as a temporal logic formula, while guaranteeing inter-agent collision avoidance. In particular, by defining 3-D spheres that bound the agents' volume, we extend previous work on decentralized navigation functions and propose control laws that navigate the agents among predefined regions of interest of the workspace while avoiding collision with each other. This allows us to abstract the motion of the agents as finite transition systems and, by employing standard formal verification techniques, to derive a high-level control algorithm that satisfies the agents' specifications. Simulation and experimental results with quadrotors verify the validity of the proposed method.

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Neural Network Based Adaptive Consensus of Multi-quadrotor System

Meng

Zhu

Yang

et al. 2022

Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control

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Real-time implementation of decentralized adaptive formation control on multi-quadrotor systems

Cited by 8 publications

References 10 publications

Adaptive Linear Quadratic Attitude Tracking Control of a Quadrotor UAV Based on IMU Sensor Data Fusion

Adaptive Linear Quadratic Attitude Tracking Control of a Quadrotor UAV Based on IMU Sensor Data Fusion

Decentralized motion planning with collision avoidance for a team of UAVs under high level goals

Neural Network Based Adaptive Consensus of Multi-quadrotor System

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