näkemys - Control Systems - # Parameter Estimation and Uncertainty Quantification

Uncertainty Quantification in Adaptive Safety-Critical Control

Q: How can the proposed framework be applied to real-world safety-critical systems

The proposed framework for online parameter estimation and uncertainty quantification in adaptive safety-critical control can be applied to real-world systems by enhancing the adaptability and safety of autonomous systems operating in complex environments. By integrating learning-based techniques with control strategies, the framework enables these systems to dynamically adjust to uncertainties that may arise during operation. This is particularly crucial in safety-critical applications where traditional control methods may fall short in handling unforeseen variations or disturbances. The ability to quantify uncertainty through set-based estimates allows for a more robust approach to system design and operation, ensuring that safety requirements are met even in uncertain conditions. Real-world implementations could include autonomous vehicles, robotic systems, aerospace applications, and industrial automation where adaptability and safety are paramount.

Q: What are the potential limitations or drawbacks of relying on set-based estimates for uncertainty quantification

While set-based estimates offer significant advantages in terms of uncertainty quantification compared to pointwise estimates, there are potential limitations associated with their use. One drawback is the computational complexity involved in maintaining and updating sets of possible parameters over time. As the number of constraints grows unbounded for extended time horizons, implementing set-based estimates efficiently becomes challenging, especially for real-time applications. Additionally, conservative bounds on parameter estimation errors generated by set-based approaches can lead to overly cautious controllers or decision-making processes which might limit system performance or responsiveness. Balancing between accuracy and conservatism when utilizing set-based estimates requires careful consideration based on specific application requirements.

Q: How might advancements in uncertainty quantification impact the future development of autonomous systems

Advancements in uncertainty quantification have the potential to significantly impact the future development of autonomous systems across various domains. Improved methods for estimating uncertainties can enhance the reliability and safety of these systems by providing a clearer understanding of how they will behave under different conditions or disturbances. This knowledge enables better risk assessment procedures leading to more informed decision-making processes within autonomous platforms such as self-driving cars or unmanned aerial vehicles (UAVs). Furthermore, advancements in uncertainty quantification could drive innovation towards developing adaptive algorithms that can autonomously adjust their behavior based on changing environmental factors or operational requirements without compromising overall system integrity or safety standards.

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Efficiently estimating parameters and quantifying uncertainty in adaptive safety-critical control systems.

Tiivistelmä

The paper introduces a framework for online parameter estimation and uncertainty quantification in adaptive safety-critical control. It leverages continuous-time recursive least squares (RLS) algorithms to generate parameter estimates efficiently. By using objects like zonotopes, set-based estimates are propagated over time, aiding in synthesizing safety-critical controllers for systems with uncertainties. The integration of learning and control raises questions about reliability and safety, addressed through adaptive control schemes with stability guarantees. Safety properties are formalized using set invariance concepts, extended to control systems via control barrier functions (CBFs). Robust adaptive approaches reduce conservatism by assuming known bounds on system parameters. Various uncertainty quantification mechanisms are explored, including set membership identification (SMID) and concurrent learning adaptive control methods. These techniques allow for efficient computation of bounds on parameter estimation errors while accounting for additive disturbances.

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arxiv.org

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"This work is supported by the NSF under grants DGE-1840990 and IIS-2024606."
"Alternate uncertainty quantification techniques used for aCBFs include concurrent learning [17], [18]."
"The FE condition requires Φ(t)⊤Φ(t) to be invertible for all t ≥ T."

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Tärkeimmät oivallukset

Uncertainty Quantification for Recursive Estimation in Adaptive Safety-Critical Control

by Max H. Cohen... klo arxiv.org 03-14-2024

https://arxiv.org/pdf/2304.01901.pdf

Uncertainty Quantification for Recursive Estimation in Adaptive Safety-Critical Control

Syvällisempiä Kysymyksiä

How can the proposed framework be applied to real-world safety-critical systems

The proposed framework for online parameter estimation and uncertainty quantification in adaptive safety-critical control can be applied to real-world systems by enhancing the adaptability and safety of autonomous systems operating in complex environments. By integrating learning-based techniques with control strategies, the framework enables these systems to dynamically adjust to uncertainties that may arise during operation. This is particularly crucial in safety-critical applications where traditional control methods may fall short in handling unforeseen variations or disturbances. The ability to quantify uncertainty through set-based estimates allows for a more robust approach to system design and operation, ensuring that safety requirements are met even in uncertain conditions. Real-world implementations could include autonomous vehicles, robotic systems, aerospace applications, and industrial automation where adaptability and safety are paramount.

What are the potential limitations or drawbacks of relying on set-based estimates for uncertainty quantification

While set-based estimates offer significant advantages in terms of uncertainty quantification compared to pointwise estimates, there are potential limitations associated with their use. One drawback is the computational complexity involved in maintaining and updating sets of possible parameters over time. As the number of constraints grows unbounded for extended time horizons, implementing set-based estimates efficiently becomes challenging, especially for real-time applications. Additionally, conservative bounds on parameter estimation errors generated by set-based approaches can lead to overly cautious controllers or decision-making processes which might limit system performance or responsiveness. Balancing between accuracy and conservatism when utilizing set-based estimates requires careful consideration based on specific application requirements.

How might advancements in uncertainty quantification impact the future development of autonomous systems

Advancements in uncertainty quantification have the potential to significantly impact the future development of autonomous systems across various domains. Improved methods for estimating uncertainties can enhance the reliability and safety of these systems by providing a clearer understanding of how they will behave under different conditions or disturbances. This knowledge enables better risk assessment procedures leading to more informed decision-making processes within autonomous platforms such as self-driving cars or unmanned aerial vehicles (UAVs). Furthermore, advancements in uncertainty quantification could drive innovation towards developing adaptive algorithms that can autonomously adjust their behavior based on changing environmental factors or operational requirements without compromising overall system integrity or safety standards.