基于利用结构的ADMM与内点法的序列二次规划实现欠驱动双摆起摆的实时非线性模型预测控制
Real-Time Nonlinear MPC via Sequential Quadratic Programming with Structure-Exploiting ADMM and Interior-Point Methods for Underactuated Double-Pendulum Swing-Up
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中文总结 AI 辅助
针对2026年IJCAI-ECAI竞赛的欠驱动双摆控制任务，提出基于序列二次规划的实时非线性模型预测控制方法，在CloudPendulum硬件上实现可靠的起摆稳定与抗扰性能。
中文摘要 AI 辅助
将于2026年不来梅IJCAI-ECAI举办的第四届“RealAIGym人工智能奥运会”竞赛，要求参与者开发全局控制策略，用于将欠驱动双连杆系统起摆并稳定在竖直位置。与往届不同，本次竞赛要求参与者直接在可远程访问的CloudPendulum硬件上开发和评估控制策略，交互时间有限，且无法预先获取系统模型参数。本文提出一种基于最优控制的方法，采用序列二次规划实现实时非线性模型预测控制。结果表明，所提出的基于SQP的MPC控制器可实现可靠的起摆与稳定性能，同时对扰动具有鲁棒性。
英文摘要
The 4th "AI Olympics with RealAIGym" competition, to be held at IJCAI-ECAI 2026 in Bremen, challenges participants to develop a global control policy for swinging up and stabilizing an underactuated two-link system in its upright position. In contrast to previous editions, participants develop and evaluate their control strategies directly on remotely accessible CloudPendulum hardware, with limited interaction time and without prior knowledge of the system's model parameters. This paper presents an optimal-control-based approach employing real-time nonlinear model predictive control implemented using sequential quadratic programming. The results demonstrate that the proposed SQP-based MPC controller achieves reliable swing-up and stabilization performance, while maintaining robustness against disturbances.
发表机构
- University of Patras（帕特雷大学）
机构由 AI 辅助整理，请以论文原文为准。
