真实机器人系统的保证：统一形式化控制器综合与可达集一致性辨识
Guarantees for Real Robotic Systems: Unifying Formal Controller Synthesis and Reachset-Conformant Identification
AI总结：
针对安全关键场景中形式化属性难以转移到真实机器人的问题，提出将可达集一致性模型辨识与控制器综合相结合的方法，确保模型安全属性可转移到真实机器人，并通过真实机器人跟踪控制实验验证了其可转移性。
摘要：
机器人越来越多地用于安全关键场景，如机器人手术或人机交互。为确保严格的性能标准，形式化控制器综合是保证机器人按预期行为运作的有前景方向。然而，只有当模型合适时，形式化保证的属性才能转移到真实机器人上。我们通过将可达集一致性模型的辨识与控制器综合相结合来解决这一问题。由于该可达集一致性模型包含真实机器人的所有测量行为，模型的安全属性可转移到真实机器人。我们在真实机器人上合成跟踪控制器，通过实验证明了这种可转移性。
英文摘要：
Robots are used increasingly often in safety-critical scenarios, such as robotic surgery or human-robot interaction. To ensure stringent performance criteria, formal controller synthesis is a promising direction to guarantee that robots behave as desired. However, formally ensured properties only transfer to the real robot when the model is appropriate. We address this problem by combining the identification of a reachset-conformant model with controller synthesis. Since the reachset-conformant model contains all the measured behaviors of the real robot, the safety properties of the model transfer to the real robot. The transferability is demonstrated by experiments on a real robot, for which we synthesize tracking controllers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
