Controller Design for Structured State-space Models via Contraction Theory
This paper presents an indirect data-driven output feedback controller synthesis for nonlinear systems, leveraging Structured State-space Models (SSMs) as surrogate models. SSMs have emerged as a compelling alternative in modelling time-series data and dynamical systems. They can capture long-term dependencies while maintaining linear computational complexity with respect to the sequence length, in comparison to the quadratic complexity of Transformer-based architectures. The contributions of this work are threefold. We provide the first analysis of controllability and observability of SSMs, which leads to scalable control design via Linear Matrix Inequalities (LMIs) that leverage contrac...
| Authors | Muhammad Zakwan, Vaibhav Gupta, Alireza Karimi et al. |
|---|---|
| Primary category | eess.SY |
| Published | 8 April 2026 |
| Last updated at source | 8 April 2026 |
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