This paper presents a Nonlinear Model Predictive Control (NMPC) scheme targeted at motion planning for mechatronic motion systems, such as drones and mobile platforms. NMPC-based motion planning typically requires low computation times to be able to provide control inputs at the required rate for system stability, disturbance rejection, and overall performance. Although there exist various ways in literature to reduce the solution times in NMPC, such times may not be low enough to allow real-time implementations. This paper presents ASAP-MPC, an approach to handle varying, sometimes restrictively large, solution times with an asynchronous update scheme, always allowing for full convergence and real-time execution. The NMPC algorithm is combined with a linear state feedback controller tracking the optimised trajectories for improved robustness against possible disturbances and plant-model mismatch. ASAP-MPC seamlessly merges trajectories, resulting from subsequent NMPC solutions, providing a smooth and continuous overall trajectory for the motion system. This frameworks applicability to embedded applications is shown on two different experiment setups where a state-of-the-art method fails: a quadcopter flying through a cluttered environment in hardware-in-the-loop simulation and a scale model truck-trailer manoeuvring in a structured lab environment.

翻译：本文提出了一种面向机电运动系统（如无人机和移动平台）运动规划的非线性模型预测控制（NMPC）方案。基于NMPC的运动规划通常需要较低的计算时间，以便能以系统稳定性、抗干扰性和整体性能所需的速率提供控制输入。尽管文献中已存在多种降低NMPC求解时间的方法，但这些时间可能不足以实现实时部署。本文提出ASAP-MPC，一种采用异步更新方案处理变化且有时过大的求解时间的方法，始终确保完全收敛与实时执行。该NMPC算法与线性状态反馈控制器相结合，追踪优化后的轨迹，以增强对潜在干扰和系统模型失配的鲁棒性。ASAP-MPC无缝融合后续NMPC求解生成的轨迹，为运动系统提供平滑且连续的全局轨迹。该框架在嵌入式应用中的可行性通过两种实验场景（先进方法在此场景中失效）得到验证：在硬件在环仿真中穿越杂乱环境的四旋翼飞行器，以及在结构化实验环境中机动操作的缩比卡车-拖车模型。