Resilient-Learning Control of Cyber-Physical Systems Against Mixed-Type Network Attacks
针对由虚假数据注入和重放攻击组成的混合型网络攻击，提出一种基于三层神经网络学习的弹性控制策略，通过自适应律估计网络权重，保证系统最终有界和渐近稳定，并用垂直起降直升机模型验证。
This article develops a resilient-learning control strategy for a kind of cyber-physical system to mitigate the influence of a mixed-type of network attacks. Such an attack is composed of a false-data-injection attack and a replay attack, which can be represented comprehensively by using Markov jump signals. Note that the involved attacks are assumed to be uncertain, which requires a three-layer neural network to learn them. Based on attack approximations as the output from the neural network, a resilient and efficient controller is designed to defend against the mixed-type of network attacks, in which several adaptive laws are proposed to estimate the involved neural network weights. Under the designed controller, the ultimate boundness and asymptotical stability are discussed. Finally, a practical vertical taking-off and landing helicopter model is proposed to verify the developed controller.