IEEE Philadelphia Chapter of CSS/CASS/SMCS
Date: November 18, 2025
Time: 7:00 PM EST to 0:00 PM
Location: Virtual
Optimal Control and Learning for Dynamical Systems under Adversarial Attacks
The robust learning of dynamical systems is crucial for safety-critical applications, such as power systems and autonomous systems. The control theory has a rich literature on system identification and optimal control in the case when the system is subject to non-adversarial and mostly Gaussian disturbance. However, there is a pressing need to develop learning and control techniques for systems whose inputs are under adversarial attacks, meaning that the system operates in a hostile environment. The main challenge is that the classic results relying on closed-form solutions for least-square estimators and LQR/LQG are no longer valid, and it is essential to design non-smooth estimators and controllers with no closed-form solutions in presence of adversarial attacks. In this talk, we discuss the recent advances in the area and focus on the problem of learning an unknown nonlinear dynamical system subject to adversarial disturbance/input. We develop a non-smooth estimator and show that the correct dynamics of the system can be learned in finite time no matter how severe the attack is as long as the learning period is longer than some threshold. We then study optimal control for systems in hostile environments.
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https://events.vtools.ieee.org/m/502766