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：本文提出了一种针对机电运动系统（如无人机与移动平台）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短的计算时间，以在系统稳定性、抗干扰能力与整体性能要求下，以所需速率提供控制输入。尽管文献中存在多种降低NMPC求解时间的方法，但这些时间可能仍不足以实现实时部署。本文提出ASAP-MPC方法，采用异步更新机制处理可变乃至过长的求解时间，始终保证完全收敛与实时执行。该NMPC算法与跟踪优化轨迹的线性状态反馈控制器相结合，以增强对潜在扰动及模型失配的鲁棒性。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连续的全局轨迹。通过在两种现有先进方法失效的实验场景（杂乱环境中四旋翼飞行器的硬件在环仿真，以及结构化实验环境中缩比卡车-拖车系统的机动操作）中的验证，展示了该框架在嵌入式应用中的适用性。