Online Learning-based Receding Horizon Control of Tilt-rotor Tricopter: A Cascade Implementation

TY - GEN

T1 - Online Learning-based Receding Horizon Control of Tilt-rotor Tricopter: A Cascade Implementation

AU - Mehndiratta, Mohit

AU - Kayacan, Erdal

N1 - USB ISBN: 978-1-5386-5427-9 Print on Demand(PoD) ISBN: 978-1-5386-5429-3 Electronic ISSN: 2378-5861

PY - 2018/8/9

Y1 - 2018/8/9

N2 - This study manifests a learning-based cascade nonlinear model predictive control (NMPC) algorithm for the trajectory tracking of a tilt-rotor tricopter UAV; wherein two time-varying aerodynamic parameters, thrust and drag-moment coefficients, are estimated online incorporating nonlinear moving horizon estimation method. Since the performance of a model-based controller is guaranteed for an accurate mathematical model of the system to be controlled, it is indeed important to estimate the changing dynamics in order to make NMPC adaptive - and therefore robust - to the time-varying operational disturbances. To further illustrate the tracking capability of learning-based cascade NMPC, a complex square-shaped trajectory is flown and is observed to be well tracked. To the best of our knowledge, this is the first application of an online learning-based cascade NMPC to a complicated aerospace system. Moreover, owing to ACADO toolkit, the overall execution time of the closed-loop is below 4 milliseconds, which eventually demonstrates the real-time potential of the presented control framework.

AB - This study manifests a learning-based cascade nonlinear model predictive control (NMPC) algorithm for the trajectory tracking of a tilt-rotor tricopter UAV; wherein two time-varying aerodynamic parameters, thrust and drag-moment coefficients, are estimated online incorporating nonlinear moving horizon estimation method. Since the performance of a model-based controller is guaranteed for an accurate mathematical model of the system to be controlled, it is indeed important to estimate the changing dynamics in order to make NMPC adaptive - and therefore robust - to the time-varying operational disturbances. To further illustrate the tracking capability of learning-based cascade NMPC, a complex square-shaped trajectory is flown and is observed to be well tracked. To the best of our knowledge, this is the first application of an online learning-based cascade NMPC to a complicated aerospace system. Moreover, owing to ACADO toolkit, the overall execution time of the closed-loop is below 4 milliseconds, which eventually demonstrates the real-time potential of the presented control framework.

UR - http://www.scopus.com/inward/record.url?scp=85052584466&partnerID=8YFLogxK

U2 - 10.23919/ACC.2018.8430814

DO - 10.23919/ACC.2018.8430814

M3 - Article in proceedings

SP - 6378

EP - 6383

BT - 2018 Annual American Control Conference, ACC 2018

PB - IEEE

T2 - 2018 American Control Conference (ACC)

Y2 - 27 June 2018 through 29 June 2018

ER -