Accelerating Adaptive Systems via Normalized Parameter Estimation Laws

TL;DR

This paper introduces normalized parameter estimation laws that accelerate adaptive system convergence by ensuring finite integrability of a fractional power of the system state norm, promoting faster decay without relying on high gains or persistent excitation.

Contribution

The paper proposes a novel class of normalized estimation laws that enhance convergence speed and sparsity promotion, applicable to a broad range of systems without requiring persistent excitation or high gains.

Findings

Guarantee finite integrability of the r-th root of the squared norm of the system state.

Promote faster convergence and sparsity in system response.

Applicable to systems with matched and unmatched uncertainties, with higher-order extensions.

Abstract

In this paper, we propose a new class of parameter estimation laws for adaptive systems, called \emph{normalized parameter estimation laws}. A key feature of these estimation laws is that they accelerate the convergence of the system state, $\mathit{x(t)}$, to the origin. We quantify this improvement by showing that our estimation laws guarantee finite integrability of the $\mathit{r}$-th root of the squared norm of the system state, i.e., \( \mathit{\|x(t)\|}_2^{2/\mathit{r}} \in \mathcal{L}_1, \) where $\mathit{r} \geq 1$ is a pre-specified parameter that, for a broad class of systems, can be chosen arbitrarily large. In contrast, standard Lyapunov-based estimation laws only guarantee integrability of $\mathit{\|x(t)\|}_2^2$ (i.e., $\mathit{r} = 1$). We motivate our method by showing that, for large values of $r$, this guarantee serves as a sparsity-promoting mechanism in the time domain, meaning that it penalizes prolonged signal duration and slow decay, thereby promoting faster convergence of $\mathit{x(t)}$. The proposed estimation laws do not rely on time-varying or high adaptation gains and do not require persistent excitation. Moreover, they can be applied to systems with matched and unmatched uncertainties, regardless of their dynamic structure, as long as a control Lyapunov function (CLF) exists. Finally, they are compatible with any CLF-based certainty equivalence controllers. We further develop higher-order extensions of our estimation laws by incorporating momentum into the estimation dynamics. We illustrate the performance improvements achieved with the proposed scheme through various numerical experiments.