Distributed Hierarchical Control of Quadrotor UAVs: Design and Experimental Validation

2018 ◽  
Author(s):  
Bryce Mack ◽  
Christopher Noe ◽  
Trevor Rice ◽  
In Soo Ahn ◽  
Jing Wang
Author(s):  
Herschel C. Pangborn ◽  
Justin P. Koeln ◽  
Matthew A. Williams ◽  
Andrew G. Alleyne

This paper proposes and experimentally validates a hierarchical control framework for fluid flow systems performing thermal management in mobile energy platforms. A graph-based modeling approach derived from the conservation of mass and energy inherently captures coupling within and between physical domains. Hydrodynamic and thermodynamic graph-based models are experimentally validated on a thermal-fluid testbed. A scalable hierarchical control framework using the graph-based models with model predictive control (MPC) is proposed to manage the multidomain and multi-timescale dynamics of thermal management systems. The proposed hierarchical control framework is compared to decentralized and centralized benchmark controllers and found to maintain temperature bounds better while using less electrical energy for actuation.


2019 ◽  
Vol 63 (2) ◽  
pp. 122-132
Author(s):  
Zsófia Bodó ◽  
Béla Lantos

In this paper an improved approach is presented for integrating backstepping control of outdoor quadrotor UAVs. The controller uses the approximated nonlinear dynamic model, while for simulation or test purposes the quadrotor can be modeled either with the precise or the simplified model. A hierarchical integrating backstepping control algorithm was constructed that has the capability of handling every effect in the dynamic model and in the meantime successfully ignores the realistic measurement noises. The hierarchical control structure consists of position, attitude and rotor control, extended with path design with continuous acceleration and/or continuous jerk. The state estimation is based on sensor fusion. Control parameters can be easily tuned. Adaptive laws are elaborated for mass and vertical disturbance force estimation. The tracking algorithm is able to follow the prescribed path with small error. The sensory system and the state estimation are prepared for outdoor applications. The embedded control system contains a HIL extension to test the control algorithms before the first flight under real time conditions.


2020 ◽  
Vol 53 (2) ◽  
pp. 12992-12998
Author(s):  
Marcel Pendieu Kwaye ◽  
Riccardo Maria Vignali ◽  
Riccardo Lazzari

2015 ◽  
Vol 53 (01) ◽  
Author(s):  
L Spomer ◽  
CGW Gertzen ◽  
D Häussinger ◽  
H Gohlke ◽  
V Keitel

2018 ◽  
Vol 138 (8) ◽  
pp. 651-658 ◽  
Author(s):  
Keisuke Shirasaki ◽  
Naotaka Okada ◽  
Kenichiro Sano ◽  
Hideki Iwatsuki

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