MPC as a Copilot: A Predictive Filter Framework with Safety and Stability Guarantees

• URL: http://arxiv.org/abs/2603.27893v1
• Date: Sun, 29 Mar 2026 22:17:39 GMT
• Title: MPC as a Copilot: A Predictive Filter Framework with Safety and Stability Guarantees
• Abstract summary: This paper introduces the Predictive Safety-Stability Filter (PS2F), a unified predictive filter framework that guarantees constraint satisfaction and stability within a single architecture.<n>The PS2F framework comprises two cascaded optimal control problems: a nominal model predictive control (MPC) layer that serves solely as a copilot, and a secondary filtering layer that adjusts external command to remain within a provably safe and stable region.
• Score: 6.019901994880397
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Ensuring both safety and stability remains a fundamental challenge in learning-based control, where goal-oriented policies often neglect system constraints and closed-loop state convergence. To address this limitation, this paper introduces the Predictive Safety--Stability Filter (PS2F), a unified predictive filter framework that guarantees constraint satisfaction and asymptotic stability within a single architecture. The PS2F framework comprises two cascaded optimal control problems: a nominal model predictive control (MPC) layer that serves solely as a copilot, implicitly defining a Lyapunov function and generating safety- and stability-certified predicted trajectories, and a secondary filtering layer that adjusts external command to remain within a provably safe and stable region. This cascaded structure enables PS2F to inherit the theoretical guarantees of nominal MPC while accommodating goal-oriented external commands. Rigorous analysis establishes recursive feasibility and asymptotic stability of the closed-loop system without introducing additional conservatism beyond that associated with the nominal MPC. Furthermore, a time-varying parameterisation allows PS2F to transition smoothly between safety-prioritised and stability-oriented operation modes, providing a principled mechanism for balancing exploration and exploitation. The effectiveness of the proposed framework is demonstrated through comparative numerical experiments.

Related papers

• Robust Conformal CBF and CLF Controllers via Iterative Policy Updates [10.455279488679523]
  Conformal prediction (CP) has been used to obtain probabilistic bounds on the error between a learned dynamics model and the true but unknown system.<n>Such CP bounds can then be embedded into robust control Lyapunov function (CLF) and control barrier function (CBF) frameworks.<n>However, such an approach does not retain stability/safety guarantees because of the distribution shift between the closed-loop trajectory distribution.<n>We propose an episodic framework that iteratively updates the robust CLF/CBF policy while maintaining stability/safety guarantees.
  arXiv  Detail & Related papers  (2026-06-13T16:03:22Z)
• Cooptimizing Safety and Performance Using Safety Value-Constrained Model Predictive Control [14.944399584182017]
  We propose a safety-value function-based terminal constraint that enforces membership in a control-invariant safe set at the end of each planning horizon.<n>This formulation enables real-time synthesis of trajectories that are both high-performing and provably safe.<n>We validate the proposed framework through simulation and hardware experiments on a Flexiv Rizon 10s manipulator.
  arXiv  Detail & Related papers  (2026-04-26T20:10:03Z)
• Safety-Guaranteed Imitation Learning from Nonlinear Model Predictive Control for Spacecraft Close Proximity Operations [39.146761527401424]
  This paper presents a safety-guaranteed, runtime-efficient imitation learning framework for spacecraft close proximity control.<n>We leverage Control Barrier Functions (CBFs) for safety certificates and Control Lyapunov Functions (CLFs) for stability as unified design principles across data generation, training, and deployment.<n>A runtime analysis demonstrates real-time feasibility on a commercial off-the-shelf processor, supporting onboard deployment for safety-critical on-orbit servicing.
  arXiv  Detail & Related papers  (2026-03-19T13:47:18Z)
• Response-Aware Risk-Constrained Control Barrier Function With Application to Vehicles [0.0]
  This paper proposes a unified control framework based on Response-Aware Risk-Constrained Control Barrier Function for dynamic safety boundary control of vehicles.<n>The framework fuses nominal dynamics priors with direct vehicle body responses to provide a baseline direction for control gradients.<n>It also reformulates traditional deterministic safety constraints into probabilistic constraints on the tail risk of barrier function derivatives.
  arXiv  Detail & Related papers  (2026-03-13T02:38:17Z)
• How to Train Your Latent Control Barrier Function: Smooth Safety Filtering Under Hard-to-Model Constraints [21.03977709777739]
  We show that reachability value functions can be adapted to be control barrier functions (CBFs) for smooth optimization-based filtering.<n>We propose the LatentCBF, which addresses both challenges through gradient penalties that lead to smooth margin functions without additional labeling.<n>Experiments on simulated benchmarks and hardware with a vision-based manipulation policy demonstrate that LatentCBF enables smooth safety filtering.
  arXiv  Detail & Related papers  (2025-11-23T20:15:28Z)
• Boundary-to-Region Supervision for Offline Safe Reinforcement Learning [56.150983204962735]
  Boundary-to-Region (B2R) is a framework that enables asymmetric conditioning through cost signal realignment.<n>B2R redefines CTG as a boundary constraint under a fixed safety budget, unifying the cost distribution of all feasible trajectories.<n> Experimental results show that B2R satisfies safety constraints in 35 out of 38 safety-critical tasks.
  arXiv  Detail & Related papers  (2025-09-30T03:38:20Z)
• Rectified Robust Policy Optimization for Model-Uncertain Constrained Reinforcement Learning without Strong Duality [53.525547349715595]
  We propose a novel primal-only algorithm called Rectified Robust Policy Optimization (RRPO)<n>RRPO operates directly on the primal problem without relying on dual formulations.<n>We show convergence to an approximately optimal feasible policy with complexity matching the best-known lower bound.
  arXiv  Detail & Related papers  (2025-08-24T16:59:38Z)
• Wasserstein Distributionally Robust Control Barrier Function using Conditional Value-at-Risk with Differentiable Convex Programming [4.825619788907192]
  Control Barrier functions (CBFs) have attracted extensive attention for designing safe controllers for real-world safety-critical systems. We present distributional robust CBF to achieve resilience under distributional shift. We also provide an approximate variant of DR-CBF for higher-order systems.
  arXiv  Detail & Related papers  (2023-09-15T18:45:09Z)
• 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)
• Robust Stability of Neural-Network Controlled Nonlinear Systems with Parametric Variability [2.0199917525888895]
  We develop a theory for stability and stabilizability of a class of neural-network controlled nonlinear systems. For computing such a robust stabilizing NN controller, a stability guaranteed training (SGT) is also proposed.
  arXiv  Detail & Related papers  (2021-09-13T05:09:30Z)
• 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)
• Closing the Closed-Loop Distribution Shift in Safe Imitation Learning [80.05727171757454]
  We treat safe optimization-based control strategies as experts in an imitation learning problem. We train a learned policy that can be cheaply evaluated at run-time and that provably satisfies the same safety guarantees as the expert.
  arXiv  Detail & Related papers  (2021-02-18T05:11:41Z)
• Learning Stabilizing Controllers for Unstable Linear Quadratic Regulators from a Single Trajectory [85.29718245299341]
  We study linear controllers under quadratic costs model also known as linear quadratic regulators (LQR) We present two different semi-definite programs (SDP) which results in a controller that stabilizes all systems within an ellipsoid uncertainty set. We propose an efficient data dependent algorithm -- textsceXploration -- that with high probability quickly identifies a stabilizing controller.
  arXiv  Detail & Related papers  (2020-06-19T08:58:57Z)

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.