AI 中文总结
研究安全关键型自主系统自适应问题，提出维护架构图表示并推理的方法，经联合优化实现自适应，在差分驱动机器人实验中，相比固定架构显著降低跟踪误差，证明推理系统结构而非仅调参的价值。
AI 中文摘要
安全关键型自主系统通常在保持底层架构不变的情况下通过调整控制器参数来进行自适应。当传感、资源可用性或组件健康状况的变化使原始结构假设无效时，这种策略就会失效。本文介绍了一种方法，系统在运行期间维护其架构的显式、基于图的表示并据此进行推理。系统被建模为物理、功能和基于模型的模块的有向图，边捕获信息和控制依赖关系。自适应被视为对架构配置和模块参数的联合优化，使用基于监控-分析-规划-执行循环的有限状态机并受操作约束。通过残差分解和依赖加权影响传播来隔离性能下降，并使用稳定性感知机制过滤候选自适应。该方法在差分驱动机器人上针对传感器漂移和执行器故障进行了演示。固定架构分别累积高达24米和13米的跟踪误差，而架构感知自适应通过选择合适的故障配置将每种情况下的误差降低到1.5米以下。这些结果表明了对系统结构进行推理同时保持稳定性的价值，而不是仅仅依赖于参数调整。
英文摘要
Safety critical autonomous systems often adapt by adjusting controller parameters while keeping the underlying architecture fixed. This strategy breaks down when shifts in sensing, resource availability, or component health invalidate the original structural assumptions. This work introduces a method in which system maintain an explicit, graph-based representation of their architecture and reason over it during operation. The system is modeled as a directed graph of physical, functional, and model based modules, with edges capturing information and control dependencies. Adaptation is posed as a joint optimization over architectural configurations and module parameters, subject to operational constraints using a Monitor-Analyze-Plan-Execute loop-based finite state machine. Performance degradation is isolated via residual decomposition and dependency weighted influence propagation, and candidate adaptations are filtered using a stability aware mechanism. The approach is demonstrated on a differential drive robot under sensor drift and actuator faults. A fixed architecture accumulates tracking errors of up to 24 m and 13 m, respectively, whereas architecture aware adaptation reduces error under 1.5 m in each case, by selecting fault appropriate configurations. These results show the value of reasoning over system structure, while preserving stability, rather than relying solely on parameter tuning.
Comments10 pages conference paper
