핵심 개념
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.
초록
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.
통계
"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."
인용구
"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."