Improved nonlinear model‐free adaptive iterative learning control in DoS attack environment

• Published in: IET control theory & applications Vol. 18; no. 7; pp. 825 - 833
• Main Authors: Li, Yanni, Li, Xiuying
• Format: Journal Article
• Language: English
• Published: Wiley 01.04.2024
• Subjects: adaptive control; compensation; iterative methods; networked control systems; nonlinear control systems
• ISSN: 1751-8644; 1751-8652
• DOI: 10.1049/cth2.12549

This paper investigates the design of a model‐free adaptive iterative learning controller(MFAILC) based on sampled‐data under the presence of denial‐of‐service(DoS) attacks in nonlinear networked control systems. First, the MFAILC is presented only using I/O data, where a compensation mechanism for DoS attacks is proposed. With dynamic linearization techniques, the nonlinear system is transformed into a linear system in the iteration domain. Then an improved MFAILC is designed to actively compensate the lost data caused by DoS attacks, where the estimation of pseudo‐partial derivative (PPD) is improved by establishing the AR model. The proposed algorithm can weaken the adverse effects of the DoS attacks and ensure the excellent tracking performance of the system. Finally, the stability of the method is proved, and the effectiveness of the proposed algorithm is demonstrated by a numerical example. By combining the characteristics of MFAC and ILC, we proposed the MFAILC algorithm for the system subject to DoS attacks. An innovative approach to PPD estimation by introducing AR model in the DoS attacks environment and a new compensation algorithm based on PID idea in the iterative domain is proposed.