Antifragile Control Systems: The case of mobile robot trajectory tracking in the presence of uncertainty

TL;DR

This paper introduces an antifragile control framework for mobile robots that not only withstands uncertainty and faults but also anticipates and exploits failures to enhance performance in complex, unpredictable environments.

Contribution

It presents a novel antifragile control design methodology specifically for mobile robot trajectory tracking, emphasizing anticipation and exploitation of faults and uncertainties.

Findings

Demonstrates improved trajectory tracking performance under uncertainty.

Provides a systematic controller design process based on antifragility.

Shows superiority over state-of-the-art controllers in fault scenarios.

Abstract

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.