Reinforcement Learning-based Receding Horizon Control using Adaptive Control Barrier Functions for Safety-Critical Systems
- URL: http://arxiv.org/abs/2403.17338v1
- Date: Tue, 26 Mar 2024 02:49:08 GMT
- Title: Reinforcement Learning-based Receding Horizon Control using Adaptive Control Barrier Functions for Safety-Critical Systems
- Authors: Ehsan Sabouni, H. M. Sabbir Ahmad, Vittorio Giammarino, Christos G. Cassandras, Ioannis Ch. Paschalidis, Wenchao Li,
- Abstract summary: Optimal control methods provide solutions to safety-critical problems but easily become intractable.
We propose a Reinforcement Learning-based Receding Horizon Control approach leveraging Model Predictive Control.
We validate our method by applying it to the challenging automated merging control problem for Connected and Automated Vehicles.
- Score: 14.166970599802324
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Optimal control methods provide solutions to safety-critical problems but easily become intractable. Control Barrier Functions (CBFs) have emerged as a popular technique that facilitates their solution by provably guaranteeing safety, through their forward invariance property, at the expense of some performance loss. This approach involves defining a performance objective alongside CBF-based safety constraints that must always be enforced. Unfortunately, both performance and solution feasibility can be significantly impacted by two key factors: (i) the selection of the cost function and associated parameters, and (ii) the calibration of parameters within the CBF-based constraints, which capture the trade-off between performance and conservativeness. %as well as infeasibility. To address these challenges, we propose a Reinforcement Learning (RL)-based Receding Horizon Control (RHC) approach leveraging Model Predictive Control (MPC) with CBFs (MPC-CBF). In particular, we parameterize our controller and use bilevel optimization, where RL is used to learn the optimal parameters while MPC computes the optimal control input. We validate our method by applying it to the challenging automated merging control problem for Connected and Automated Vehicles (CAVs) at conflicting roadways. Results demonstrate improved performance and a significant reduction in the number of infeasible cases compared to traditional heuristic approaches used for tuning CBF-based controllers, showcasing the effectiveness of the proposed method.
Related papers
- Domain Adaptive Safety Filters via Deep Operator Learning [5.62479170374811]
We propose a self-supervised deep operator learning framework that learns the mapping from environmental parameters to the corresponding CBF.
We demonstrate the effectiveness of the method through numerical experiments on navigation tasks involving dynamic obstacles.
arXiv Detail & Related papers (2024-10-18T15:10:55Z) - Reinforcement Learning with Model Predictive Control for Highway Ramp Metering [14.389086937116582]
This work explores the synergy between model-based and learning-based strategies to enhance traffic flow management.
The control problem is formulated as an RL task by crafting a suitable stage cost function.
An MPC-based RL approach, which leverages the MPC optimal problem as a function approximation for the RL algorithm, is proposed to learn to efficiently control an on-ramp.
arXiv Detail & Related papers (2023-11-15T09:50:54Z) - Safe Neural Control for Non-Affine Control Systems with Differentiable
Control Barrier Functions [58.19198103790931]
This paper addresses the problem of safety-critical control for non-affine control systems.
It has been shown that optimizing quadratic costs subject to state and control constraints can be sub-optimally reduced to a sequence of quadratic programs (QPs) by using Control Barrier Functions (CBFs)
We incorporate higher-order CBFs into neural ordinary differential equation-based learning models as differentiable CBFs to guarantee safety for non-affine control systems.
arXiv Detail & Related papers (2023-09-06T05:35:48Z) - Learning Feasibility Constraints for Control Barrier Functions [8.264868845642843]
We employ machine learning techniques to ensure the feasibility of Quadratic Programs (QPs)
We propose a sampling-based learning approach to learn a new feasibility constraint for CBFs.
We demonstrate the advantages of the proposed learning approach to constrained optimal control problems.
arXiv Detail & Related papers (2023-03-10T16:29:20Z) - Pointwise Feasibility of Gaussian Process-based Safety-Critical Control
under Model Uncertainty [77.18483084440182]
Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs) are popular tools for enforcing safety and stability of a controlled system, respectively.
We present a Gaussian Process (GP)-based approach to tackle the problem of model uncertainty in safety-critical controllers that use CBFs and CLFs.
arXiv Detail & Related papers (2021-06-13T23:08:49Z) - Learning Robust Hybrid Control Barrier Functions for Uncertain Systems [68.30783663518821]
We propose robust hybrid control barrier functions as a means to synthesize control laws that ensure robust safety.
Based on this notion, we formulate an optimization problem for learning robust hybrid control barrier functions from data.
Our techniques allow us to safely expand the region of attraction of a compass gait walker that is subject to model uncertainty.
arXiv Detail & Related papers (2021-01-16T17:53:35Z) - Enforcing robust control guarantees within neural network policies [76.00287474159973]
We propose a generic nonlinear control policy class, parameterized by neural networks, that enforces the same provable robustness criteria as robust control.
We demonstrate the power of this approach on several domains, improving in average-case performance over existing robust control methods and in worst-case stability over (non-robust) deep RL methods.
arXiv Detail & Related papers (2020-11-16T17:14:59Z) - Reinforcement Learning for Safety-Critical Control under Model
Uncertainty, using Control Lyapunov Functions and Control Barrier Functions [96.63967125746747]
Reinforcement learning framework learns the model uncertainty present in the CBF and CLF constraints.
RL-CBF-CLF-QP addresses the problem of model uncertainty in the safety constraints.
arXiv Detail & Related papers (2020-04-16T10:51:33Z) - Learning Control Barrier Functions from Expert Demonstrations [69.23675822701357]
We propose a learning based approach to safe controller synthesis based on control barrier functions (CBFs)
We analyze an optimization-based approach to learning a CBF that enjoys provable safety guarantees under suitable Lipschitz assumptions on the underlying dynamical system.
To the best of our knowledge, these are the first results that learn provably safe control barrier functions from data.
arXiv Detail & Related papers (2020-04-07T12:29:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.