提出线性约束可行性分析新理论，提升自主系统控制效率。
Feasibility Analysis and Constraint Selection in Optimization-Based Controllers
- 基于必要充分条件分析线性约束可行性，构建新判定框架。
- 算法在仿真中性能媲美顶尖方法，计算效率更优。
- 适合研究约束优化控制的学者与自动驾驶系统开发者。
在自主系统控制中，约束优化是核心问题，涵盖从底层控制到高层规划的广泛应用。本文提供了一种新的理论分析，推导出线性约束可行性的必要且充分条件，并在此基础上开发了针对自主系统控制的可行约束选择新方法。通过一系列仿真实验，验证了所提算法在性能上可媲美现有先进方法，同时具备更高的计算效率。重要的是，该分析为约束不可行性评估、分析与处理提供了全新的理论框架。
原文摘要 · Abstract (English)
Control synthesis under constraints is at the forefront of research on autonomous systems, in part due to its broad application from low-level control to high-level planning, where computing control inputs is typically cast as a constrained optimization problem. Assessing feasibility of the constraints and selecting among subsets of feasible constraints is a challenging yet crucial problem. In this work, we provide a novel theoretical analysis that yields necessary and sufficient conditions for feasibility assessment of linear constraints and based on this analysis, we develop novel methods for feasible constraint selection in the context of control of autonomous systems. Through a series of simulations, we demonstrate that our algorithms achieve performance comparable to state-of-the-art methods while offering improved computational efficiency. Importantly, our analysis provides a novel theoretical framework for assessing, analyzing and handling constraint infeasibility.
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