控制问题中的稀疏性：近期趋势与开放挑战
Sparsity in control problems: Recent trends and open challenges
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中文总结 AI 辅助
本综述系统梳理了控制问题中稀疏性设计的方法与算法，涵盖执行器调度、最小能量可控性等，并指出未来研究方向以应对系统复杂性增长。
中文摘要 AI 辅助
与工程的所有领域一样，控制工程师不断面临在实现高性能与传感、驱动和控制资源有限可用性之间的两难境地。这种紧张关系常常转化为以稀疏约束形式化的设计问题；也就是说，设计涉及决策变量，其数量、空间范围、时间分布或其他物理表达远小于任务相关性能指标的无约束优化理想场景。尽管这一基本限制强调了众多方法论方法和应用领域，但控制文献被分割成针对特定场景的多条研究线。本综述通过汇集处理相关控制问题（如执行器调度、最小能量可控性、最大手离控制（maximum hands-off control）和在线凸优化）中稀疏性的多条研究线，并在理论基础和算法之间编织一条主线，填补了这一空白。除了建立关于处理控制场景中稀疏性的问题和解决方案的连贯、结构化叙述外，我们还讨论了开放领域和未来研究的相关方向，这些方向受到控制系统日益增长的复杂性及其与普适计算、分布式学习、网络物理安全和生态足迹等关键领域的关系的推动。
英文摘要
As in all fields of engineering, control engineers constantly face a dichotomy between achieving high performance and the limited availability of resources for sensing, actuation, and control. This tension often translates into formalizing design problems with sparsity constraints; that is, the design involves decision variables whose number, spatial reach, temporal spread, or other physical expressions are much smaller than in the ideal scenario of unconstrained optimization of a task-related performance metric. Even though this fundamental limitation underlines a plethora of methodological approaches and application domains, the control literature is fragmented into several lines of work tailored to specific scenarios. This survey fills the gap by bringing together several lines of work on handling sparsity in relevant control problems such as actuator scheduling, minimal-energy controllability, maximum hands-off control, and online convex optimization, and weaving a red thread through theoretical foundations and algorithms. Beyond establishing a coherent, structured narrative of problems and solutions for handling sparsity in control scenarios, we discuss open areas and relevant directions for future research, motivated by the growing complexity of control systems and their relations with key domains such as pervasive computing, distributed learning, cyberphysical security, and ecological footprint.
发表机构
- University of Padova（帕多瓦大学）
- Delft University of Technology（代尔夫特理工大学）
- University of Texas at Dallas（德克萨斯大学达拉斯分校）
- Politecnico di Torino（都灵理工大学）
- Indian Institute of Science（印度科学学院）
- Hiroshima University（广岛大学）
机构由 AI 辅助整理，请以论文原文为准。
