Constrained Control of Quadrotor Using Laguerre Functions Based Model Predictive Control for Reference Tracking
Published in Proceedings of the 2021 5th International Conference on Advances in Robotics, 2021
This paper addresses the reference tracking problem of a quadrotor in presence of physical constraints using Laguerre functions-based Model Predictive Control (MPC). MPC necessitates in finding an optimal solution, even under system constraints. The use of Laguerre functions helps in reducing computation time, for the online implementation of the controller. The dynamic equations of the quadrotor are derived using the Newton-Euler method. A quadratic cost function is derived for the Laguerre-based MPC and Hildreth’s quadratic programming method is applied for the optimization of the quadratic cost in presence of constraints. The quadrotor model with constraints is simulated in MATLAB and results are presented for circular and helical 3D reference trajectories.
Recommended citation: Prayag Sharma, Utkarsh Bajpai, Subir Kumar Saha, and Adnan Jawed. 2022. Constrained Control of Quadrotor Using Laguerre Functions Based Model Predictive Control for Reference Tracking. In Proceedings of the 2021 5th International Conference on Advances in Robotics (AIR 21). Association for Computing Machinery, New York, NY, USA, Article 4, 1–7. https://doi.org/10.1145/3478586.3478604
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