mlsanityData to 14 Oct 2026

Predefined-Time Resilient Integral Reinforcement Learning for Input-Constrained Unknown Nonlinear Systems Under FDI Attacks and Disturbances: A Fully Data-Driven Approach

arXiv 2609.11815 ↗First posted 10 Sep 2026eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL theory and sample complexity

Abstract (arXiv)

This paper investigates optimal control for nonlinear systems with unknown dynamics, input constraints, disturbances, and adversarial signals. The objective is to develop a learning-based control method that allows the designer to prescribe the desired convergence time in advance. An integral reinforcement-learning framework is proposed to avoid requiring exact knowledge of the system dynamics while ensuring that the control input always satisfies the actuator constraints. Current and recorded data are combined to train the critic without requiring persistent excitation. The learning gain is selected directly from the prescribed convergence deadline. Lyapunov analysis is then used to establish practical predefined-time convergence of the coupled state-critic system in the presence of disturbances and adversarial channels. The effectiveness of the proposed method is validated through the stabilization control of a two-link robot manipulator.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 40 references that are in the map. First posted on arXiv 10 Sep 2026.

Builds on

6 of 40 references.

References · 6

  1. 2016Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive controlcited by 140
  2. 2024SafEDMD: A Koopman-based data-driven controller design framework for nonlinear dynamical systemscited by 5
  3. 2025When Koopman Meets Hamilton and Jacobicited by 4
  4. 2026Fixed-Time Integral Reinforcement Learning for Saturated Nonlinear Multi-Agent Systems Under FDI Attackscited by 2
  5. 2018Enhancing the settling time estimation of a class of fixed-time stable systemscited by 1
  6. 2019A Lyapunov-like Characterization of Predefined-Time Stabilitycited by 1

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  1. 2026Fixed-Time Resilient Integral Reinforcement Learning for Input-Constrained Unknown Nonlinear Systems Under FDI Attacks and Disturbances: A Data-Driven Admissible Warm Start
  2. 2026Predefined-Time Integral Reinforcement Learning for Saturated Unknown Nonlinear Multi-Agent Systems Under FDI Attacks and Disturbances ↗
  3. 2023Neural Differentiable Integral Control Barrier Functions for Unknown Nonlinear Systems with Input Constraints
  4. 2025Optimal Regulation of Nonlinear Input-Affine Systems via an Integral Reinforcement Learning-Based State-Dependent Riccati Equation Approach
  5. 2024Stochastic Reinforcement Learning with Stability Guarantees for Control of Unknown Nonlinear Systems
  6. 2024Learning Robust Policies via Interpretable Hamilton-Jacobi Reachability-Guided Disturbances
  7. 2025Dyna-Style Reinforcement Learning Modeling and Control of Non-linear Dynamics
  8. 2024Deep Reinforcement Learning Control for Disturbance Rejection in a Nonlinear Dynamic System with Parametric Uncertainty