Quadrotor State Estimation and Control Through a Novel qLPV Model For MPC and MHE

This paper introduces a novel quasi Linear Parameter Varying (qLPV) model to describe the quadrotor dynamics and constraints, overcoming the limitations of existing LPV models that rely on linearization, approximation and mainly limited to attitude dynamics. Alternatively, the proposed LPV model captures both position and attitude dynamics within one controller without involving cascaded structures. In addition, the nonlinear constraints associated with unit quaternion representation are reformulated as qLPV constraints, enabling their integration into the control and estimation processes. The efficiency of the model for control and estimation is demonstrated through the application of Model Predictive Control (MPC) and Moving Horizon Estimation (MHE), achieving comparable tracking accuracy to nonlinear MPC (NMPC) while reducing execution time by a factor of nine. This contribution offers a computationally efficient and real-time solution for quadrotor control and estimation, filling an important gap in the existing literature.