A General Verification Framework for Dynamical and Control Models via Certificate Synthesis

• URL: http://arxiv.org/abs/2309.06090v2
• Date: Mon, 1 Jul 2024 11:08:14 GMT
• Title: A General Verification Framework for Dynamical and Control Models via Certificate Synthesis
• Authors: Alec Edwards, Andrea Peruffo, Alessandro Abate,
• Abstract summary: We provide a framework to encode system specifications and define corresponding certificates. We present an automated approach to formally synthesise controllers and certificates. Our approach contributes to the broad field of safe learning for control, exploiting the flexibility of neural networks.
• Score: 54.959571890098786
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: An emerging branch of control theory specialises in certificate learning, concerning the specification of a desired (possibly complex) system behaviour for an autonomous or control model, which is then analytically verified by means of a function-based proof. However, the synthesis of controllers abiding by these complex requirements is in general a non-trivial task and may elude the most expert control engineers. This results in a need for automatic techniques that are able to design controllers and to analyse a wide range of elaborate specifications. In this paper, we provide a general framework to encode system specifications and define corresponding certificates, and we present an automated approach to formally synthesise controllers and certificates. Our approach contributes to the broad field of safe learning for control, exploiting the flexibility of neural networks to provide candidate control and certificate functions, whilst using SMT-solvers to offer a formal guarantee of correctness. We test our framework by developing a prototype software tool, and assess its efficacy at verification via control and certificate synthesis over a large and varied suite of benchmarks.

Related papers

• Transfer of Safety Controllers Through Learning Deep Inverse Dynamics Model [4.7962647777554634]
  Control barrier certificates have proven effective in formally guaranteeing the safety of the control systems. Design of a control barrier certificate is a time-consuming and computationally expensive endeavor. We propose a validity condition that, when met, guarantees correctness of the controller.
  arXiv  Detail & Related papers  (2024-05-22T15:28:43Z)
• Fine-Tuning Language Models Using Formal Methods Feedback [53.24085794087253]
  We present a fully automated approach to fine-tune pre-trained language models for applications in autonomous systems. The method synthesizes automaton-based controllers from pre-trained models guided by natural language task descriptions. The results indicate an improvement in percentage of specifications satisfied by the controller from 60% to 90%.
  arXiv  Detail & Related papers  (2023-10-27T16:24:24Z)
• Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions [63.18590014127461]
  This paper introduces a model-uncertainty-aware reformulation of CBF-based safety-critical controllers. We study the feasibility of the resulting robust safety-critical controller. We then use these conditions to devise an event-triggered online data collection strategy.
  arXiv  Detail & Related papers  (2022-08-23T05:02:09Z)
• Joint Differentiable Optimization and Verification for Certified Reinforcement Learning [91.93635157885055]
  In model-based reinforcement learning for safety-critical control systems, it is important to formally certify system properties. We propose a framework that jointly conducts reinforcement learning and formal verification.
  arXiv  Detail & Related papers  (2022-01-28T16:53:56Z)
• Sparsity in Partially Controllable Linear Systems [56.142264865866636]
  We study partially controllable linear dynamical systems specified by an underlying sparsity pattern. Our results characterize those state variables which are irrelevant for optimal control.
  arXiv  Detail & Related papers  (2021-10-12T16:41:47Z)
• Safety Verification of Model Based Reinforcement Learning Controllers [7.407039316561176]
  We present a novel safety verification framework for model-based RL controllers using reachable set analysis. The proposed frame-work can efficiently handle models and controllers which are represented using neural networks.
  arXiv  Detail & Related papers  (2020-10-21T03:35:28Z)
• Learning Stability Certificates from Data [19.381365606166725]
  We develop algorithms for learning certificate functions only from trajectory data. We convert such generalization error bounds into global stability guarantees. We demonstrate empirically that certificates for complex dynamics can be efficiently learned.
  arXiv  Detail & Related papers  (2020-08-13T14:58:42Z)
• Certified Reinforcement Learning with Logic Guidance [78.2286146954051]
  We propose a model-free RL algorithm that enables the use of Linear Temporal Logic (LTL) to formulate a goal for unknown continuous-state/action Markov Decision Processes (MDPs) The algorithm is guaranteed to synthesise a control policy whose traces satisfy the specification with maximal probability.
  arXiv  Detail & Related papers  (2019-02-02T20:09:32Z)

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.