Resilient Learning-Based Control for Partially Observable Systems Under DoS Attacks
Sayan Chakraborty, Zhong-Ping Jiang · IFAC-PapersOnLine · 2025
This paper addresses the challenge of designing resilient control systems under Denial of Service (DoS) attacks for discrete-time systems. A learning-based framework is proposed to reconstruct lost measurements and compute optimal controllers using input-output data, eliminating the need for a complete system model. By leveraging state reconstruction techniques, the framework estimates missing information during DoS periods, ensuring robust control performance. Two algorithms, based on policy iteration (PI) and value iteration (VI), are developed to learn the optimal feedback control policy. The effectiveness of the proposed methodology is illustrated via a numerical example.