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

arXiv: https://arxiv.org/abs/2609.11815 · HTML: https://mlsanity.com/paper/2609.11815/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL theory and sample complexity](https://mlsanity.com/deeprl/rl-theory-and-sample-complexity/)

## Builds on (most cited first, 6 of 6)

- [Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control](https://mlsanity.com/paper/1611.03537/) (2016)
- [SafEDMD: A Koopman-based data-driven controller design framework for nonlinear dynamical systems](https://mlsanity.com/paper/2402.03145/) (2024)
- [When Koopman Meets Hamilton and Jacobi](https://mlsanity.com/paper/2504.07346/) (2025)
- [Fixed-Time Integral Reinforcement Learning for Saturated Nonlinear Multi-Agent Systems Under FDI Attacks](https://mlsanity.com/paper/2609.06163/) (2026)
- [Enhancing the settling time estimation of a class of fixed-time stable systems](https://mlsanity.com/paper/1809.07012/) (2018)
- [A Lyapunov-like Characterization of Predefined-Time Stability](https://mlsanity.com/paper/1910.14604/) (2019)

## 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.