OCP-Based Nonlinear Model Predictive Control for Quadrotor Trajectory Tracking

Abstract

Fast online optimization is essential for nonlinear model predictive control (NMPC) on resource-limited aerial platforms. This paper presents an analytical-derivative NMPC solver for quadrotor trajectory tracking. For the state-affine translational model with held thrust and attitude commands, fourth-order Runge--Kutta integration yields an exact discrete-time prediction map. Forward--backward difference equations provide the gradient and exact Hessian of the finite-horizon cost condensed over the input sequence. These derivatives drive a nested optimal-control-based direction recursion, while a shifted warm start reuses the preceding solution. The resulting solver addresses the unconstrained tracking problem without a general-purpose nonlinear programming package. Gazebo experiments show comparable tracking accuracy to iLQR and two acados SQP configurations, with mean solver-time speedups of $1.97$--$2.30$. In onboard flight tests, the mean solver time is $0.3121$\,ms, corresponding to speedups of $3.37$--$5.77$ over the same baselines. The largest measured solver time is $3.7754$\,ms within a $20$-ms control period. These measurements cover the numerical solver and exclude estimation, communication, and command publication.