Stability-Enhanced Predictive Safety Filters for Integrating Learning-Based Controllers and Humans into Safety-Critical Systems

المفاهيم الأساسية

The core message of this article is to propose a modular safety filter layer that ensures constraint satisfaction and various stability specifications, enabling the integration of learning-based controllers and humans into safety-critical systems.

الملخص

The article addresses the problem of applying potentially unsafe and non-stabilizing desired inputs to safety-critical systems, such as those found in automated driving, smart factories, or surgical robotics. It proposes a stability-enhanced predictive safety filter that extends an existing model predictive safety filter formulation to provide stability guarantees in addition to constraint satisfaction.

The key highlights and insights are:

1. The proposed framework extends well-known stability results from model predictive control (MPC) theory while supporting commonly used design techniques. It can provide different levels of stability, ranging from bounded convergence to uniform asymptotic stability.

2. The formulation is easily adaptable to a variety of different MPC settings, resulting in a modular framework that can be tailored to robust or dynamic tracking settings. This provides an online adaptable closed-loop performance bound.

3. The design and implementation of the scheme are illustrated using an advanced driver assistance system example, requiring safety and stability with respect to a dynamic reference trajectory.

الإحصائيات

None.

اقتباسات

None.

الرؤى الأساسية المستخلصة من

Stability Mechanisms for Predictive Safety Filters

by Elias Milios... في arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05496.pdf

استفسارات أعمق

How can the proposed framework be extended to handle uncertainties in the system dynamics or external disturbances

To handle uncertainties in the system dynamics or external disturbances, the proposed framework can be extended by incorporating robust control techniques. One approach is to introduce a robust tube-based model predictive control (MPC) strategy. By defining a robust tube around the nominal trajectory, the framework can account for uncertainties and disturbances, ensuring stability and constraint satisfaction even in the presence of variations in the system dynamics. Robust MPC techniques, such as constraint tightening and robust optimization, can be utilized to enhance the system's resilience to uncertainties and disturbances, providing a more robust and reliable control strategy.

What are the potential challenges and limitations in applying the stability-enhanced predictive safety filters to high-dimensional systems with complex constraints

When applying stability-enhanced predictive safety filters to high-dimensional systems with complex constraints, several challenges and limitations may arise. One challenge is the computational complexity associated with solving optimization problems for high-dimensional systems, which can lead to increased computation time and resource requirements. Additionally, handling complex constraints in high-dimensional spaces may require sophisticated algorithms and optimization techniques to ensure feasibility and stability. The scalability of the framework to large-scale systems with intricate constraints needs to be carefully considered to maintain efficiency and effectiveness. Moreover, the interpretation and implementation of stability guarantees in complex systems with multiple interacting components can pose challenges in ensuring robust and reliable performance.

How could the framework be adapted to handle safety-critical systems with multiple agents or vehicles, where coordination and cooperation are essential

Adapting the framework to handle safety-critical systems with multiple agents or vehicles necessitates the integration of coordination and cooperation mechanisms. In scenarios involving multiple agents, such as autonomous vehicles or robotic systems, the framework can be extended to incorporate decentralized control strategies and communication protocols. By introducing coordination mechanisms, such as consensus algorithms or distributed MPC, the safety-enhanced predictive filters can facilitate collaborative decision-making and coordination among multiple agents to ensure system-wide safety and stability. Furthermore, the framework can be tailored to include collision avoidance algorithms, cooperative path planning, and communication protocols to enable safe interactions and coordination between multiple agents in safety-critical systems.