Communication Dans Un Congrès Année : 2025

Reachability under Bounded Variations for Safety in Control Loops with Reinforcement Learning enhancing Fault Accommodation

Résumé

Ensuring both safety and fault tolerance in reinforcement learning (RL)-based control systems is critical for their deployment in aerospace systems. In this context, the aim of this work is twofold. First, a new zonotopic reachability algorithm is proposed to address the case of bounded inputs with bounded variations. It is obtained from a reformulation based on bounded delayed exogenous inputs. Second, this algorithm is used for offline and training-independent safety verification of control loops hybridizing a baseline model-based controller with a constrained reinforcement learning (CRL) agent. The agent is then trained not only to improve given performance metrics under unmodeled environmental dynamics, but also to achieve incipient fault accommodation. A numerical example based on aircraft flight dynamics illustrates the main results and shows the efficiency of the proposed scheme.

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Dates et versions

hal-05176192 , version 1 (22-07-2025)

Identifiants

  • HAL Id : hal-05176192 , version 1

Citer

Josué Nkaya-Nkaya, Christophe Combastel, Jérôme Cieslak. Reachability under Bounded Variations for Safety in Control Loops with Reinforcement Learning enhancing Fault Accommodation. 6th IEEE International Conference on Control and Fault-Tolerant Systems (SysTol), IEEE, Oct 2025, Ayia Napa, Cyprus. ⟨hal-05176192⟩
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