Learning High-Order Control Barrier Functions for Safety-Critical Control with Gaussian Processes

Belangrijkste concepten

Using Gaussian processes to mitigate model uncertainty in high-order control barrier functions.

Samenvatting

The article discusses the use of Gaussian processes to address model uncertainty in high-order control barrier functions (HOCBFs). It highlights the challenges posed by model uncertainty in ensuring system safety and introduces a data-driven approach to handle these uncertainties. The paper presents a method to convert chance constraints of HOCBFs into second-order cone constraints, enabling convex constrained optimization for safety filtering. The effectiveness of the proposed strategy is validated through numerical results. The study focuses on two main applications: adaptive cruise control with collision avoidance and an active suspension system. Simulation results demonstrate the superiority of the GP-based SOCP-HOCBF design over nominal QP-HOCBF controllers in maintaining system safety and performance.

Statistieken

A total number of 119 samples are collected for the adaptive cruise control simulation.
174 data points are collected for the active suspension system simulation.

Citaten

"The proposed GP-based controller could avoid unsafe behavior."
"The GP-based SOCP controller successfully recovers the performance of the true system."

Belangrijkste Inzichten Gedestilleerd Uit

Learning High-Order Control Barrier Functions for Safety-Critical Control with Gaussian Processes

by Mohammad Aal... om arxiv.org 03-15-2024

https://arxiv.org/pdf/2403.09573.pdf

Diepere vragen

What are some potential limitations or drawbacks of using Gaussian processes in high-order control barrier functions

One potential limitation of using Gaussian processes (GPs) in high-order control barrier functions is the computational complexity involved. GPs can be computationally expensive, especially when dealing with a large amount of data or high-dimensional spaces. This could result in longer processing times and may not be suitable for real-time applications where quick decision-making is crucial. Additionally, GPs rely on kernel functions to model the relationships between data points, and selecting an appropriate kernel function that accurately captures the underlying dynamics of the system can be challenging.

How might different types of uncertainties impact the effectiveness of the proposed strategy

Different types of uncertainties can impact the effectiveness of the proposed strategy in various ways. For example, if there are significant modeling errors or inaccuracies in estimating system parameters, such as mass or damping coefficients, it can lead to inaccurate predictions by the GP model. This would result in suboptimal safety constraints being imposed on the control system, potentially leading to unsafe operation. Moreover, uncertainties related to external disturbances or environmental factors that are not accounted for in the model could also affect the performance of the controller designed based on GP approximations.

How can this research be applied to other complex control systems beyond adaptive cruise control and active suspension systems

This research on learning high-order control barrier functions with Gaussian processes has broader implications beyond adaptive cruise control and active suspension systems. The methodology developed here can be applied to a wide range of complex control systems across various industries such as robotics, aerospace, autonomous vehicles, and industrial automation. By incorporating uncertainty quantification techniques like GPs into safety-critical control mechanisms, it enables robust and adaptive controllers that can handle uncertain environments effectively. The approach presented in this study offers a systematic framework for ensuring system safety while accounting for model uncertainty and variability inherent in real-world systems.