ASAP-MPC：一种用于非线性模型预测控制在线运动规划的异步更新方案
ASAP-MPC: An Asynchronous Update Scheme for Online Motion Planning with Nonlinear Model Predictive Control
AI总结：
针对NMPC运动规划求解时间难以满足实时性的问题，本文提出ASAP-MPC异步更新框架，结合线性状态反馈实现轨迹无缝融合，并在四旋翼和卡车-拖车实验中验证其嵌入式适用性。
摘要：
本文提出了一种非线性模型预测控制（NMPC）方案，面向无人机和移动平台等机电运动系统的运动规划。基于NMPC的运动规划通常需要较低的计算时间，才能以所需速率提供控制输入，从而保证系统稳定性、扰动抑制和整体性能。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支持实时实现。本文提出ASAP-MPC，这是一种通过异步更新方案处理变化的、有时大到受限的求解时间的方法，始终允许完全收敛和实时执行。该NMPC算法与线性状态反馈控制器相结合，由后者跟踪优化轨迹，以增强对可能扰动和对象-模型失配的鲁棒性。ASAP-MPC无缝融合由后续NMPC求解得到的轨迹，为运动系统提供平滑且连续的整体轨迹。该框架在两种不同实验装置上的嵌入式应用适用性得到验证，而一种最先进方法在这些装置上失效：在硬件在环仿真中穿越杂乱环境的四旋翼飞行器，以及在结构化实验室环境中机动的缩尺模型卡车-拖车。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
