Multi-Step Model Predictive Safety Filters: Reducing Chattering by Increasing the Prediction Horizon
This addresses safety and smoothness issues for learning-based controllers in robotics, but it is incremental as it builds on existing MPSF methods.
The paper tackles the problem of chattering in model predictive safety filters by extending the prediction horizon, resulting in a reduction of chattering by more than a factor of 4 in quadrotor experiments while maintaining safety guarantees.
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.