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Neural Certificates for Safe Control Policies

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arxiv 2006.08465 v1 pith:TN5NSIGA submitted 2020-06-15 eess.SY cs.LGcs.SY

Wanxin Jin , Zhaoran Wang , Zhuoran Yang , Shaoshuai Mou This is my paper · ORCID
classification eess.SYcs.LGcs.SY
keywords goal-reachingsafepolicysystemfunctionlearnneuralpolicies
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper develops an approach to learn a policy of a dynamical system that is guaranteed to be both provably safe and goal-reaching. Here, the safety means that a policy must not drive the state of the system to any unsafe region, while the goal-reaching requires the trajectory of the controlled system asymptotically converges to a goal region (a generalization of stability). We obtain the safe and goal-reaching policy by jointly learning two additional certificate functions: a barrier function that guarantees the safety and a developed Lyapunov-like function to fulfill the goal-reaching requirement, both of which are represented by neural networks. We show the effectiveness of the method to learn both safe and goal-reaching policies on various systems, including pendulums, cart-poles, and UAVs.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Neural Event-Triggered Control with Optimal Scheduling

    math.OC 2025-07 conditional novelty 6.0 of 10

    Event-triggered neural controllers that stabilize nonlinear systems with far fewer control updates, trained to maximize the gap between triggers.

  2. Data-Driven Certificate Synthesis

    eess.SY 2025-02 reject novelty 6.0 of 10

    A data-driven algorithm synthesizes reachability, safety, and reach-while-avoid certificates with PAC-style bounds, using a compression set built during non-convex loss minimization.

  3. Data-Driven Formal Methods for Complex Dynamical Systems: A Survey

    eess.SY 2026-07 accept novelty 2.0 of 10

    A taxonomy and survey of data-driven formal verification and controller synthesis, organized around abstraction-based, functional-certificate, and compositional methods with PAC, Lipschitz, and structural-property guarantees.

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