Mobile robots are ubiquitous. Such vehicles benefit from well-designed and calibrated control algorithms ensuring their task execution under precise uncertainty bounds. Yet, in tasks involving humans in the loop, such as elderly or mobility impaired, the problem takes a new dimension. In such cases, the system needs not only to compensate for uncertainty and volatility in its operation but at the same time to anticipate and offer responses that go beyond robust. Such robots operate in cluttered, complex environments, akin to human residences, and need to face during their operation sensor and, even, actuator faults, and still operate. This is where our thesis comes into the foreground. We propose a new control design framework based on the principles of antifragility. Such a design is meant to offer a high uncertainty anticipation given previous exposure to failures and faults, and exploit this anticipation capacity to provide performance beyond robust. In the current instantiation of antifragile control applied to mobile robot trajectory tracking, we provide controller design steps, the analysis of performance under parametrizable uncertainty and faults, as well as an extended comparative evaluation against state-of-the-art controllers. We believe in the potential antifragile control has in achieving closed-loop performance in the face of uncertainty and volatility by using its exposures to uncertainty to increase its capacity to anticipate and compensate for such events.

翻译：移动机器人已广泛应用。这类设备依赖精心设计并校准的控制算法，在精确的不确定性界限内确保任务执行。然而，在涉及人类参与的任务中（如老年人或行动不便者的辅助），问题维度随之升级。此时系统不仅需要补偿运行中的不确定性和波动性，更需预见并提供超越鲁棒性的响应。此类机器人需在类似人类居所的杂乱复杂环境中运行，运行过程中需应对传感器乃至执行器故障并维持运作——这正是本研究议题的核心。我们基于抗脆弱性原理提出新型控制设计框架。该设计旨在通过先前暴露于失败与故障的经历，提供高不确定性预见能力，并利用这种预知能力实现超越鲁棒性的性能表现。在将抗脆弱控制应用于移动机器人轨迹追踪的当前实例中，我们给出了控制器设计步骤、参数化不确定性与故障下的性能分析，以及针对最新控制器的扩展性对比评估。我们相信，抗脆弱控制通过将不确定性暴露转化为事件预见与补偿能力的提升，有望在不确定性与波动性环境中实现闭环性能突破。