# New Results on Parameter Estimation via Dynamic Regressor Extension and Mixing: Continuous and Discrete-time Cases

arXiv: https://arxiv.org/abs/1908.05125 · HTML: https://mlsanity.com/paper/1908.05125/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2019.

## Built on by (most cited first, 5 of 5)

- [System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach](https://mlsanity.com/paper/2505.10678/) (2025)
- [New Algorithms for Discrete-Time Parameter Estimation](https://mlsanity.com/paper/2103.16653/) (2021)
- [Nonlinear Discrete-time System Identification without Persistence of Excitation: Finite-time Concurrent Learning Methods](https://mlsanity.com/paper/2112.07765/) (2021)
- [Partial Excitation in Parameter Learning](https://mlsanity.com/paper/2503.02235/) (2025)
- [Adaptive MPC-based quadrupedal robot control under periodic disturbances](https://mlsanity.com/paper/2505.12361/) (2025)

## Abstract (arXiv)

We present some new results on the dynamic regressor extension and mixing parameter estimators for linear regression models recently proposed in the literature. This technique has proven instrumental in the solution of several open problems in system identification and adaptive control. The new results include: (i) a unified treatment of the continuous and the discrete-time cases; (ii) the proposal of two new extended regressor matrices, one which guarantees a quantifiable transient performance improvement, and the other exponential convergence under conditions that are strictly weaker than regressor persistence of excitation; and (iii) an alternative estimator ensuring parameter estimation in finite-time that retains its alertness to track time-varying parameters. Simulations that illustrate our results are also presented.