用扰动反馈提升非线性模型预测控制的鲁棒性，确保实时安全运行。
Guaranteed Robust Nonlinear MPC via Disturbance Feedback
- 将系统拆分为理想模型、扰动反馈控制器和误差边界，联合优化
- 保证约束满足、输入到状态稳定性和递归可行性，适用于火箭着陆等复杂场景
- 开源实现支持实时应用，适合机器人与航空航天领域的高可靠性控制
机器人必须在存在扰动和模型偏差的情况下满足安全关键的状态与输入约束。本文提出一种快速、可扩展且适合实时实现的鲁棒模型预测控制（RMPC）方法，能够保证鲁棒约束满足性、输入到状态稳定性（ISS）及递归可行性。核心思想是将不确定的非线性系统分解为：(i) 标准非线性动态模型，(ii) 扰动反馈控制器，(iii) 模型误差上界，并通过顺序凸规划联合优化这些部分。生成的凸子问题利用最新的扰动反馈 MPC 求解器高效求解。方法在多种动力学系统中验证，包括具有可转向推力的火箭着陆问题。开源代码已发布于 https://github.com/antoineleeman/robust-nonlinear-mpc。
原文摘要 · Abstract (English)
Robots must satisfy safety-critical state and input constraints despite disturbances and model mismatch. We introduce a robust model predictive control (RMPC) formulation that is fast, scalable, and compatible with real-time implementation. Our formulation guarantees robust constraint satisfaction, input-to-state stability (ISS) and recursive feasibility. The key idea is to decompose the uncertain nonlinear system into (i) a nominal nonlinear dynamic model, (ii) disturbance-feedback controllers, and (iii) bounds on the model error. These components are optimized jointly using sequential convex programming. The resulting convex subproblems are solved efficiently using a recent disturbance-feedback MPC solver. The approach is validated across multiple dynamics, including a rocket-landing problem with steerable thrust. An open-source implementation is available at https://github.com/antoineleeman/robust-nonlinear-mpc.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。