This paper proposes a novel cognitive control strategy for overcoming the impacts of time-varying delay and data packet losses in networked control system. The Bernoulli distribution is used to characterize the packet losses and time-varying delay. Then, the information entropy is employed for computing the corresponding uncertainties and describe the information gap in the cognitive control. With Q-learning, PID and adaptive RBF neural networks, an improved cognitive control is designed, which is composed of three sub-controllers, i.e., cognitive controller A, PID controller and cognitive controller B. Cognitive controller A is designed with Q-learning, and cognitive controller B is designed by blending adaptive RBF neural networks with Q-learning. For an extensive analysis, the presented control methodology is compared to Q-learning-PID. The simulations show that the proposed cognitive control scheme has better robustness to packet losses and time delay than Q-learning-PID.

本文提出了一种新的认知控制策略，以克服网络控制系统中时变延迟和数据包丢失的影响。伯努利分布用于表征数据包丢失和时变延迟。然后，利用信息熵计算相应的不确定性，描述认知控制中的信息缺口。结合Q-learning、PID和自适应RBF神经网络，设计了一种改进的认知控制，它由三个子控制器组成，即认知控制器A、PID控制器和认知控制器B。认知控制器A采用Q-learning设计，而认知控制器 B 是通过将自适应 RBF 神经网络与 Q 学习相结合而设计的。为了进行广泛的分析，将所提出的控制方法与 Q-learning-PID 进行比较。