The paper provides the first controllability and observability analysis for structured state-space models, enabling LMI-based controller synthesis via contraction theory and a separation principle for observers and state feedback.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
SP-ICL integrates L1 regularization with integral concurrent learning using sliding modes to recover sparse parameters online and proves ultimate boundedness of closed-loop trajectories via non-smooth Lyapunov analysis.
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Controller Design for Structured State-space Models via Contraction Theory
The paper provides the first controllability and observability analysis for structured state-space models, enabling LMI-based controller synthesis via contraction theory and a separation principle for observers and state feedback.
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Adaptive Control with Sparse Identification of Nonlinear Dynamics
SP-ICL integrates L1 regularization with integral concurrent learning using sliding modes to recover sparse parameters online and proves ultimate boundedness of closed-loop trajectories via non-smooth Lyapunov analysis.