SEP-NMPC: Safety Enhanced Passivity-Based Nonlinear Model Predictive Control for a UAV Slung Payload System

• URL: http://arxiv.org/abs/2603.08860v1
• Date: Mon, 09 Mar 2026 19:20:08 GMT
• Title: SEP-NMPC: Safety Enhanced Passivity-Based Nonlinear Model Predictive Control for a UAV Slung Payload System
• Abstract summary: This paper introduces a Safety Enhanced Passivity-Based MPC (SEP-NMPC) that provides formal guarantees of stability and safety for a quadrotor transporting a payload through cluttered environments.<n> Stability is enforced by embedding a strict passivity inequality, which is derived from a shaped energy storage function with adaptive damping.<n>Safety is guaranteed through high-order control barrier functions (HOCBFs) that render user-defined clearance sets forward-invariant, obliging both the quadrotor and the swinging payload to maintain separation while interacting with static and dynamic obstacles.
• Score: 3.5382023072762188
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Model Predictive Control (MPC) is widely adopted for agile multirotor vehicles, yet achieving both stability and obstacle-free flight is particularly challenging when a payload is suspended beneath the airframe. This paper introduces a Safety Enhanced Passivity-Based Nonlinear MPC (SEP-NMPC) that provides formal guarantees of stability and safety for a quadrotor transporting a slung payload through cluttered environments. Stability is enforced by embedding a strict passivity inequality, which is derived from a shaped energy storage function with adaptive damping, directly into the NMPC. This formulation dissipates excess energy and ensures asymptotic convergence despite payload swings. Safety is guaranteed through high-order control barrier functions (HOCBFs) that render user-defined clearance sets forward-invariant, obliging both the quadrotor and the swinging payload to maintain separation while interacting with static and dynamic obstacles. The optimization remains quadratic-program compatible and is solved online at each sampling time without gain scheduling or heuristic switching. Extensive simulations and real-world experiments confirm stable payload transport, collision-free trajectories, and real-time feasibility across all tested scenarios. The SEP-NMPC framework therefore unifies passivity-based closed-loop stability with HOCBF-based safety guarantees for UAV slung-payload transportation.

Related papers

• Towards safe and optimal flight: Viability Kernel MPC for Fully Actuated Multirotor [2.8914742944205862]
  This paper presents a method for generating safe pose trajectories for fully actuated multirotors within a Model Predictive Control framework.<n>Obstacle avoidance is enforced through computed axis-aligned bounding boxes, providing formal safety guarantees without exhaustive offline reachability analysis.<n> Numerical simulations on a fully actuated tilted hexarotor validate the approach, demonstrating successful navigation in cluttered environments with real-time computational performance.
  arXiv  Detail & Related papers  (2026-08-26T07:26:11Z)
• A Neural Network Based Teleoperation for Remote Controlled Vehicles [98.38733381483934]
  Direct teleoperation of vehicles faces critical technical bottlenecks.<n> communication latency and the operator's inability to physically perceive unmodeled environmental disturbances.<n>We propose a tailored unilateral teleoperation framework.
  arXiv  Detail & Related papers  (2026-08-11T01:51:08Z)
• Safe Overtaking for Autonomous Racing Using Hierarchical Optimization and Learning-Based Control [14.05083688629287]
  This paper proposes a hierarchical overtaking framework that separates maneuver-level decision making from safety-certified trajectory control.<n>A reinforcement learning policy adapts the discrete-time CBF decay parameter online, enabling context-dependent modulation of safety margins without directly controlling vehicle inputs.
  arXiv  Detail & Related papers  (2026-07-15T00:19:14Z)
• From Prediction Uncertainty to Conformalized Distance Fields for Safe Motion Planning [9.50741434664352]
  Existing conformal approaches conformalize a scalar score that aggregates per-obstacle prediction errors.<n>We instead conformalize the entire predicted distance field at once.<n>This functional conformal prediction framework yields a distribution-free, field-level lower bound.
  arXiv  Detail & Related papers  (2026-07-01T11:02:51Z)
• Robust Integrated Planning and Control for Quadrotors in Dynamic Environments via NMPC with CBF Penalties [0.0]
  This paper presents a new robust integrated planning and control (IPC) strategy for uncrewed aerial vehicles.<n>We propose a nonlinear model predictive control (NMPC) formulation that embeds control barrier functions (CBFs) as exponential penalties.<n>This is the first hardware-validated NMPC-CBF IPC framework, offering a practical step toward safe quadrotor deployment in dynamic environments.
  arXiv  Detail & Related papers  (2026-05-31T06:08:05Z)
• A Closed-Form CLF-CBF Controller for Whole-Body Continuum Soft Robot Collision Avoidance [45.63674336069651]
  Soft continuum manipulators provide passive safety through mechanical compliance.<n>Existing approaches, such as sampling-based planning, are often computationally expensive and lack formal safety guarantees.<n>This paper presents a closed-form Control Lyapunov Function--Control Barrier Function (CLF--CBF) controller for real-time 3D obstacle avoidance.
  arXiv  Detail & Related papers  (2026-03-19T19:34:49Z)
• 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)
• BarrierSteer: LLM Safety via Learning Barrier Steering [83.12893815611052]
  BarrierSteer is a novel framework that formalizes safety by embedding learned non-linear safety constraints directly into the model's latent representation space.<n>We show that BarrierSteer substantially reduces adversarial success rates, decreases unsafe generations, and outperforms existing methods.
  arXiv  Detail & Related papers  (2026-02-23T18:19:46Z)
• V-OCBF: Learning Safety Filters from Offline Data via Value-Guided Offline Control Barrier Functions [8.042484673796137]
  We introduce Value-Guided Offline Control Barrier Functions (V-OCBF)<n>It learns a neural CBF entirely from offline demonstrations.<n>It yields substantially fewer safety violations than baseline methods.
  arXiv  Detail & Related papers  (2025-12-11T17:14:37Z)
• UpSafe$^\circ$C: Upcycling for Controllable Safety in Large Language Models [67.91151588917396]
  Large Language Models (LLMs) have achieved remarkable progress across a wide range of tasks, but remain vulnerable to safety risks such as harmful content generation and jailbreak attacks.<n>We propose UpSafe$circ$C, a unified framework for enhancing LLM safety through safety-aware upcycling.<n>Our results highlight a new direction for LLM safety: moving from static alignment toward dynamic, modular, and inference-aware control.
  arXiv  Detail & Related papers  (2025-10-02T16:43:33Z)
• Soft Actor-Critic-based Control Barrier Adaptation for Robust Autonomous Navigation in Unknown Environments [4.788163807490197]
  We propose a Soft Actor-Critic (SAC)-based policy for adapting Control Barrier Function (CBF) constraint parameters at runtime.<n>We demonstrate that our framework effectively adapts CBF constraints, enabling the robot to reach its final goal without compromising safety.
  arXiv  Detail & Related papers  (2025-03-11T14:33:55Z)
• Monocular Obstacle Avoidance Based on Inverse PPO for Fixed-wing UAVs [29.207513994002202]
  Fixed-wing Unmanned Aerial Vehicles (UAVs) are one of the most commonly used platforms for the Low-altitude Economy (LAE) and Urban Air Mobility (UAM)<n>Classical obstacle avoidance systems, which rely on prior maps or sophisticated sensors, face limitations in unknown low-altitude environments and small UAV platforms.<n>This paper proposes a lightweight deep reinforcement learning (DRL) based UAV collision avoidance system.
  arXiv  Detail & Related papers  (2024-11-27T03:03:37Z)
• Pareto Control Barrier Function for Inner Safe Set Maximization Under Input Constraints [50.920465513162334]
  We introduce the PCBF algorithm to maximize the inner safe set of dynamical systems under input constraints.<n>We validate its effectiveness through comparison with Hamilton-Jacobi reachability for an inverted pendulum and through simulations on a 12-dimensional quadrotor system.<n>Results show that the PCBF consistently outperforms existing methods, yielding larger safe sets and ensuring safety under input constraints.
  arXiv  Detail & Related papers  (2024-10-05T18:45:19Z)
• Custom Non-Linear Model Predictive Control for Obstacle Avoidance in Indoor and Outdoor Environments [0.0]
  This paper introduces a Non-linear Model Predictive Control (NMPC) framework for the DJI Matrice 100. The framework supports various trajectory types and employs a penalty-based cost function for control accuracy in tight maneuvers.
  arXiv  Detail & Related papers  (2024-10-03T17:50:19Z)
• 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)
• 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)

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.