发表机构
Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT), Vietnam National University Ho Chi Minh City (VNU-HCM)（胡志明市工业大学机械工程学院，越南国立大学胡志明市分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种全数据驱动的积分强化学习控制方法，用于输入受限未知非线性系统，在FDI攻击和扰动下实现预定义时间收敛，并通过两连杆机器人验证有效性。
AI 中文摘要
本文研究了具有未知动力学、输入约束、扰动和对抗信号的非线性系统的最优控制问题。目标是开发一种基于学习的控制方法，使设计者能够预先指定期望的收敛时间。提出了一种积分强化学习框架，以避免需要系统动力学的精确知识，同时确保控制输入始终满足执行器约束。结合当前和记录的数据来训练评论者网络，而不需要持续激励。学习增益直接从预定义的收敛期限中选择。然后使用Lyapunov分析来建立耦合状态-评论者系统在存在扰动和对抗信道情况下的实际预定义时间收敛性。通过两连杆机器人机械臂的稳定控制验证了所提出方法的有效性。
英文摘要
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.
