| Title | Finite-Time Performance Recovery Strategy-based NCE Adaptive Neural Control for Networked Nonlinear Systems against DoS Attack |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Yong, Kenan, Chen, Mou, Wu, Qingxian |
| Conference Name | 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) |
| Keywords | Adaptive systems, barrier Lyapunov function, control design, Cyber-physical systems, denial-of-service attack, disturbance observer, internal model principle, Interval observer, nonlinear systems, numerical simulation, pubcrawl, Regulation, resilience, Resiliency, System recovery, Uncertainty |
| Abstract | Networked control design is essential to enable normal operation and further accomplish performance improvement of the cyber-physical systems. In this work, a resilient control scheme is presented for the networked nonlinear system under the denial-of-service (DoS) attack and the system uncertainty. Through synthesizing a self regulation system, this scheme is capable of releasing the prescribed performance when attack is active and recovering that in finite-time after the attack is slept. Meanwhile, the neural network is employed to approximate the system uncertainty. Particularly, the update law possesses the non-certainty-equivalent (NCE) structure, and then the impact of the DoS attack is totally isolated. Finally, the numerical simulation is presented to illustrate the effectiveness and benefits of the estimation scheme and the control design. |
| DOI | 10.1109/ICPS49255.2021.9468231 |
| Citation Key | yong_finite-time_2021 |
Finite-Time Performance Recovery Strategy-based NCE Adaptive Neural Control for Networked Nonlinear Systems against DoS Attack