Robust Control Theory
Synthesis of robust control systems. Co-design of sparse and limited (delayed, localized, quantized, saturating, noisy) sensing, communications, computing, and actuation using System Level Synthesis (SLS). Layering, localization, and distributed control. Interplay between automation, optimization, control, modeling and system identification, and machine learning. Computational scalability exploiting sparsity and structure. Start with linear input/output models (multi-state difference and differential equations). Stability, input/output norms. Uncertainty, including noise, disturbances, parametric uncertainty, unmodeled dynamics, and structured uncertainty (LTI/LTV). Tradeoffs, robustness versus efficiency, conservation laws and hard limits in time and frequency domain. Nonlinear dynamics and sum of squares, global stability, regions of attraction. Motivation throughout from case studies in tech, neuro, bio, and socioeconomic networks, as introduced in CDS 141, but not a prerequisite.
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