Low-dimensional observer design for stable linear systems by model reduction
By: M. F. Shakib, M. Khalil, R. Postoyan
Potential Business Impact:
Reconstructs system states with tiny accurate observer
This paper presents a low-dimensional observer design for stable, single-input single-output, continuous-time linear time-invariant (LTI) systems. Leveraging the model reduction by moment matching technique, we approximate the system with a reduced-order model. Based on this reduced-order model, we design a low-dimensional observer that estimates the states of the original system. We show that this observer establishes exact asymptotic state reconstruction for a given class of inputs tied to the observer's dimension. Furthermore, we establish an exponential input-to-state stability property for generic inputs, ensuring a bounded estimation error. Numerical simulations confirm the effectiveness of the approach for a benchmark model reduction problem.
Similar Papers
Learning-Enhanced Observer for Linear Time-Invariant Systems with Parametric Uncertainty
Machine Learning (CS)
Improves guessing of hidden system parts.
Learning Safety-Compatible Observers for Unknown Systems
Systems and Control
Makes robots safely guess what's happening.
Model reduction for fully nonlinear stochastic systems
Probability
Simplifies complex, unpredictable moving things.