Reinforcement Learning-based Receding Horizon Control with Adaptive Control Barrier Functions for Safety-Critical Systems

Grunnleggende konsepter

Proposing a Reinforcement Learning-based Receding Horizon Control approach with Model Predictive Control and Control Barrier Functions to enhance safety and performance in critical systems.

Sammendrag

The content discusses the challenges in optimal control methods for safety-critical systems and introduces a novel approach using Reinforcement Learning-based Receding Horizon Control with Control Barrier Functions. It addresses issues with traditional methods and showcases the effectiveness of the proposed approach through a case study on automated merging control for Connected and Automated Vehicles (CAVs).

I. Introduction

• Safety-critical systems applications across various sectors.
• Challenges with Control Barrier Functions (CBFs) and Quadratic Programs (QPs).

II. Preliminaries

• Definition of Control Barrier Functions and relative degree.
• Introduction to High Order Control Barrier Functions (HOCBF).

III. Problem Formulation

• Control of safety-critical systems with performance objectives.
• Formulation of safety constraints and stage costs.

IV. Parameterized MPC-CBF Control Design

• Formulation of the MPC objective and constraints.
• Introduction of adaptive HOCBF constraints.

V. Multi-Agent Control of CAVs

• Coordination of Connected and Automated Vehicles in conflict areas.
• Definition of Control Zones and objectives for CAVs.

VI. Simulation Results

• Implementation details of the proposed approach.
• Comparison of results between the proposed method and a baseline approach.
• Analysis of average travel time, acceleration, fuel consumption, and infeasible cases.

Statistikk

"The hyperparameters for solving the problem (15) are: L = 100m, φ = 1.2s, δ = 3.74m, umax = 4 m/s2, umin = −5, ϕmin = −π/4, ϕmax = π/4, m/s2, vmax = 20m/s, vmin = 0m/s, vdes = 15m/s, N = 5."
"The learning rate lr = 10^-5 and lr = 10^-4 for actor and critic, respectively."
"The sampling rate of the discretization and the control update period for control is ∆= 0.2s."

Sitater

"Optimal control methods provide solutions to safety-critical problems but easily become intractable."
"Results demonstrate improved performance and a significant reduction in the number of infeasible cases compared to traditional heuristic approaches."

Viktige innsikter hentet fra

Reinforcement Learning-based Receding Horizon Control using Adaptive Control Barrier Functions for Safety-Critical Systems

by Ehsan Saboun... klokken arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17338.pdf

Dypere Spørsmål

How can the proposed approach be extended to handle non-homogeneous traffic scenarios

To extend the proposed approach to handle non-homogeneous traffic scenarios, we can introduce individualized controllers for each type of vehicle in the system. This would involve training separate RL agents for different types of vehicles, each learning optimal control parameters based on their specific dynamics and constraints. By incorporating different sets of parameters for different types of vehicles, the system can adapt to the varying characteristics and behaviors of non-homogeneous traffic scenarios. Additionally, the use of coordination mechanisms between different types of vehicles can be explored to ensure safe and efficient interactions in mixed traffic environments.

What are the implications of reducing infeasible cases in safety-critical systems

Reducing infeasible cases in safety-critical systems has significant implications for the overall performance and reliability of the system. Infeasible cases can lead to system failures, safety violations, and disruptions in operations, especially in safety-critical applications such as autonomous vehicles. By minimizing infeasible cases, the proposed approach enhances the robustness and effectiveness of the control system, ensuring that safety constraints are consistently met. This results in improved system performance, increased operational efficiency, and enhanced safety measures, ultimately leading to a more reliable and dependable system.

How can the use of RL in control systems impact the future development of autonomous vehicles

The use of Reinforcement Learning (RL) in control systems has the potential to revolutionize the development of autonomous vehicles in several ways. Firstly, RL enables autonomous vehicles to learn optimal control strategies in complex and dynamic environments, allowing them to adapt to changing conditions and make real-time decisions. This adaptive capability enhances the autonomy and intelligence of the vehicles, improving their overall performance and safety.
Secondly, RL can facilitate the development of more efficient and effective control algorithms for autonomous vehicles. By leveraging RL techniques to optimize control parameters and strategies, vehicles can navigate challenging scenarios, optimize energy consumption, and enhance overall system performance. This leads to more sustainable and cost-effective operations for autonomous vehicles.
Furthermore, the integration of RL in control systems fosters continuous learning and improvement in autonomous vehicles. As vehicles gather data and experience from their interactions with the environment, RL algorithms can update and refine control policies, leading to continuous enhancement in decision-making and behavior. This iterative learning process contributes to the evolution and advancement of autonomous vehicle technology, paving the way for more sophisticated and intelligent autonomous systems.