具有实时可行性的输入-状态稳定近似非线性模型预测控制
Input-to-state Stable Approximate Nonlinear Model Predictive Control with Realtime Feasibility
AI总结：
本文提出一种基于控制李雅普诺夫函数与控制障碍函数的轻量型近似鲁棒NMPC律，拓展了可实时计算的无穷小时域NMPC方案，通过航天器控制实验验证了其有效性。
AI中文摘要：
本文基于一对输入-状态控制李雅普诺夫函数和鲁棒控制障碍函数，提出了一种计算轻量型近似鲁棒非线性模型预测控制（NMPC）律。该成果以近期提出的名义无穷小时域NMPC方案为基础并对其进行了拓展，该方案可在嵌入式硬件上实时计算非线性约束系统的反馈律，且仅需求解小规模二次规划问题。针对非线性约束航天器控制的数值实验，以及与文献中其他鲁棒NMPC方案的对比，验证了所提方案的有效性。
英文摘要：
In this paper, a computationally lightweight approximate robust nonlinear model predictive control (NMPC) law is proposed based on a pair of input-to-state control Lyapunov function and robust control barrier function. The result builds upon and augments a recently introduced nominal infinitesimal- horizon NMPC scheme which permits small-sized quadratic programs to compute the feedback law for nonlinear constraint systems on embedded hardware in real time. Numerical experiments for nonlinear constrained spacecraft control and comparison to other robust NMPC schemes from the literature demonstrate the effectiveness of the proposed scheme.
