mlsanityData to 14 Oct 2026

Decomposition of Reachable Sets and Tubes for a Class of Nonlinear Systems

arXiv 1611.00122 ↗First posted 1 Nov 2016math.OCcited by 18

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Abstract (arXiv)

Reachability analysis provides formal guarantees for performance and safety properties of nonlinear control systems. Here, one aims to compute the backward reachable set (BRS) or tube (BRT) -- the set of states from which the system can be driven into a target set at a particular time or within a time interval, respectively. The computational complexity of current approaches scales exponentially, making application to high-dimensional systems intractable. We propose a technique that decomposes the dynamics of a general class of nonlinear systems into subsystems which may be coupled through common states, controls, and disturbances. Despite this coupling, BRSs and BRTs can be computed efficiently using our technique without incurring additional approximation errors and without the need for linearizing dynamics or approximating sets as polytopes. Computations of BRSs and BRTs now become orders of magnitude faster, and for the first time BRSs and BRTs for many high-dimensional nonlinear control systems can be computed using the Hamilton-Jacobi (HJ) formulation. In situations involving bounded adversarial disturbances, our proposed method can obtain slightly conservative results. We demonstrate our theory by numerically computing BRSs and BRTs using the HJ formulation for several systems, including the 6D Acrobatic Quadrotor and the 10D Near-Hover Quadrotor.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Nov 2016.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 2; 2023: 3; 2024: 4; 2025: 3; 2026: 3.

18 papers.

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  1. 2023The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systemscited by 88
  2. 2023Iterative Reachability Estimation for Safe Reinforcement Learningcited by 37
  3. 2024Hamilton-Jacobi Reachability in Reinforcement Learning: A Surveycited by 16
  4. 2021Scalable Learning of Safety Guarantees for Autonomous Systems using Hamilton-Jacobi Reachabilitycited by 14
  5. 2021Safe Autonomous Racing via Approximate Reachability on Ego-visioncited by 13
  6. 2021Risk-averse autonomous systems: A brief history and recent developments from the perspective of optimal controlcited by 12
  7. 2022Infinite-Horizon Reach-Avoid Zero-Sum Games via Deep Reinforcement Learningcited by 6
  8. 2024Decomposing Control Lyapunov Functions for Efficient Reinforcement Learningcited by 3
  9. 2024TTT: A Temporal Refinement Heuristic for Tenuously Tractable Discrete Time Reachability Problemscited by 3
  10. 2024Learning Maximal Safe Sets Using Hypernetworks for MPC-based Local Trajectory Planning in Unknown Environmentscited by 3
  11. 2025Residual Neural Terminal Constraint for MPC-based Collision Avoidance in Dynamic Environmentscited by 3
  12. 2025Certified Approximate Reachability (CARe): Formal Error Bounds on Deep Learning of Reachable Setscited by 2
6 more
  1. 2022Verification of safety critical control policies using kernel methodscited by 1
  2. 2026Certifying Hamilton-Jacobi Reachability Learned via Reinforcement Learningcited by 1
  3. 2023Physical Backdoor Trigger Activation of Autonomous Vehicle using Reachability Analysiscited by 0
  4. 2025Reach-Avoid Differential game with Reachability Analysis for UAVs: A decomposition approachcited by 0
  5. 2026Unifying Hamilton-Jacobi Reachability and Reinforcement Learningcited by 0
  6. 2026V-MORALS: Visual Morse Graph-Aided Estimation of Regions of Attraction in a Learned Latent Spacecited by 0