# Online Output-Feedback Parameter and State Estimation for Second Order Linear Systems

arXiv: https://arxiv.org/abs/1609.05879 · HTML: https://mlsanity.com/paper/1609.05879/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2016.

## Built on by (most cited first, 3 of 3)

- [Inverse reinforcement learning in continuous time and space](https://mlsanity.com/paper/1801.07663/) (2018)
- [Online Observer-Based Inverse Reinforcement Learning](https://mlsanity.com/paper/2011.02057/) (2020)
- [Online inverse reinforcement learning with limited data](https://mlsanity.com/paper/2008.08972/) (2020)

## Abstract (arXiv)

In this paper, a concurrent learning based adaptive observer is developed for a class of second-order linear time-invariant systems with uncertain system matrices. The developed technique yields an exponentially convergent state estimator and an exponentially convergent parameter estimator. As opposed to persistent excitation required for parameter convergence in traditional adaptive methods, excitation over a finite time-interval is sufficient for the developed technique to achieve exponential convergence. Simulation results in both noise-free and noisy environments are presented to validate the design.