Multi-Step Model Predictive Safety Filters: Reducing Chattering by Increasing the Prediction Horizon

• URL: http://arxiv.org/abs/2309.11453v1
• Date: Wed, 20 Sep 2023 16:35:29 GMT
• Title: Multi-Step Model Predictive Safety Filters: Reducing Chattering by Increasing the Prediction Horizon
• Authors: Federico Pizarro Bejarano, Lukas Brunke, and Angela P. Schoellig
• Abstract summary: Safety, the satisfaction of state and input constraints, can be guaranteed by augmenting the learned control policy with a safety filter. Model predictive safety filters (MPSFs) are a common safety filtering approach based on model predictive control (MPC)
• Score: 7.55113002732746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning-based controllers have demonstrated superior performance compared to classical controllers in various tasks. However, providing safety guarantees is not trivial. Safety, the satisfaction of state and input constraints, can be guaranteed by augmenting the learned control policy with a safety filter. Model predictive safety filters (MPSFs) are a common safety filtering approach based on model predictive control (MPC). MPSFs seek to guarantee safety while minimizing the difference between the proposed and applied inputs in the immediate next time step. This limited foresight can lead to jerky motions and undesired oscillations close to constraint boundaries, known as chattering. In this paper, we reduce chattering by considering input corrections over a longer horizon. Under the assumption of bounded model uncertainties, we prove recursive feasibility using techniques from robust MPC. We verified the proposed approach in both extensive simulation and quadrotor experiments. In experiments with a Crazyflie 2.0 drone, we show that, in addition to preserving the desired safety guarantees, the proposed MPSF reduces chattering by more than a factor of 4 compared to previous MPSF formulations.

Related papers

• A Safety Modulator Actor-Critic Method in Model-Free Safe Reinforcement Learning and Application in UAV Hovering [6.529120583320167]
  This paper proposes a safety modulator actor-critic (SMAC) method to address safety constraint and overestimation mitigation in model-free safe reinforcement learning (RL) Both simulation and real-world scenarios experiments on Unmanned Aerial Vehicles (UAVs) hovering confirm that the SMAC can effectively maintain safety constraints and outperform mainstream baseline algorithms.
  arXiv  Detail & Related papers  (2024-10-09T13:07:24Z)
• Leveraging Approximate Model-based Shielding for Probabilistic Safety Guarantees in Continuous Environments [63.053364805943026]
  We extend the approximate model-based shielding framework to the continuous setting. In particular we use Safety Gym as our test-bed, allowing for a more direct comparison of AMBS with popular constrained RL algorithms.
  arXiv  Detail & Related papers  (2024-02-01T17:55:08Z)
• Modular Control Architecture for Safe Marine Navigation: Reinforcement Learning and Predictive Safety Filters [0.0]
  Reinforcement learning is increasingly used to adapt to complex scenarios, but standard frameworks ensuring safety and stability are lacking. Predictive Safety Filters (PSF) offer a promising solution, ensuring constraint satisfaction in learning-based control without explicit constraint handling. We apply this approach to marine navigation, combining RL with PSF on a simulated Cybership II model. Results demonstrate the PSF's effectiveness in maintaining safety without hindering the RL agent's learning rate and performance, evaluated against a standard RL agent without PSF.
  arXiv  Detail & Related papers  (2023-12-04T12:37:54Z)
• CaRT: Certified Safety and Robust Tracking in Learning-based Motion Planning for Multi-Agent Systems [7.77024796789203]
  CaRT is a new hierarchical, distributed architecture to guarantee the safety and robustness of a learning-based motion planning policy. We show that CaRT guarantees safety and the exponentialness of the trajectory tracking error, even under the presence of deterministic and bounded disturbance. We demonstrate the effectiveness of CaRT in several examples of nonlinear motion planning and control problems, including optimal, multi-spacecraft reconfiguration.
  arXiv  Detail & Related papers  (2023-07-13T21:51:29Z)
• Meta-Learning Priors for Safe Bayesian Optimization [72.8349503901712]
  We build on a meta-learning algorithm, F-PACOH, capable of providing reliable uncertainty quantification in settings of data scarcity. As core contribution, we develop a novel framework for choosing safety-compliant priors in a data-riven manner. On benchmark functions and a high-precision motion system, we demonstrate that our meta-learned priors accelerate the convergence of safe BO approaches.
  arXiv  Detail & Related papers  (2022-10-03T08:38:38Z)
• Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments [84.3830478851369]
  We propose a safe reinforcement learning approach that can jointly learn the environment and optimize the control policy. Our approach can effectively enforce hard safety constraints and significantly outperform CMDP-based baseline methods in system safe rate measured via simulations.
  arXiv  Detail & Related papers  (2022-09-29T20:49:25Z)
• Differentiable Safe Controller Design through Control Barrier Functions [8.283758049749782]
  Learning-based controllers can show high empirical performance but lack formal safety guarantees. Control barrier functions (CBFs) have been applied as a safety filter to monitor and modify the outputs of learning-based controllers. We propose a safe-by-construction NN controller which employs differentiable CBF-based safety layers.
  arXiv  Detail & Related papers  (2022-09-20T23:03:22Z)
• Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions [60.26921219698514]
  We introduce a model-uncertainty-aware reformulation of CBF-based safety-critical controllers. We then present the pointwise feasibility conditions of the resulting safety controller. We use these conditions to devise an event-triggered online data collection strategy.
  arXiv  Detail & Related papers  (2022-08-23T05:02:09Z)
• Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning [72.97229770329214]
  We introduce a general approach for seeking high dimensional non-linear optimization problems in which maintaining safety during learning is crucial. Our approach called LBSGD is based on applying a logarithmic barrier approximation with a carefully chosen step size. We demonstrate the effectiveness of our approach on minimizing violation in policy tasks in safe reinforcement learning.
  arXiv  Detail & Related papers  (2022-07-21T11:14:47Z)
• 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)

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.