论文标题
强化基于学习的优化控制，用于跟踪具有对抗性攻击的非线性系统
Reinforcement learning-based optimised control for tracking of nonlinear systems with adversarial attacks
论文作者
论文摘要
本文介绍了使用神经网络的一类非线性系统的基于增强学习的跟踪控制方法。在这种方法中，在执行器和输出中考虑了对抗性攻击。这种方法结合了同时跟踪和优化过程。为了获得最佳的控制输入，有必要解决汉密尔顿 - 雅各比 - 贝尔曼方程（HJB），但是由于方程中强的非线性项，这很困难。为了找到HJB方程的解决方案，我们使用了增强学习方法。在这种在线自适应学习方法中，同时适应了三个神经网络：评论家神经网络，演员神经网络和对手神经网络。最终，提出了模拟结果，以证明对操纵器引入的方法的有效性。
This paper introduces a reinforcement learning-based tracking control approach for a class of nonlinear systems using neural networks. In this approach, adversarial attacks were considered both in the actuator and on the outputs. This approach incorporates a simultaneous tracking and optimization process. It is necessary to be able to solve the Hamilton-Jacobi-Bellman equation (HJB) in order to obtain optimal control input, but this is difficult due to the strong nonlinearity terms in the equation. In order to find the solution to the HJB equation, we used a reinforcement learning approach. In this online adaptive learning approach, three neural networks are simultaneously adapted: the critic neural network, the actor neural network, and the adversary neural network. Ultimately, simulation results are presented to demonstrate the effectiveness of the introduced method on a manipulator.
