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

Engineering notes

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为 VLA、具身智能、人形机器人控制、机器人操作等高价值论文补充中文说明。

Original abstract

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.

Links and sources

• Official / arXiv page
• PDF from original source

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