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Abstract
The problem of differentiating a function with bounded second derivative in the presence of bounded measurement noise is considered in both continuous-time and sampled-data settings. Fundamental performance limitations of causal differentiators, in terms of the smallest achievable worst-case differentiation error, are shown. A robust exact differentiator is then constructed via the adaptation of a single parameter of a linear differentiator. It is demonstrated that the resulting differentiator exhibits a combination of properties that outperforms existing continuous-time differentiators: it is robust with respect to noise, it instantaneously converges to the exact derivative in the absence of noise, and it attains the smallest possible—hence optimal—upper bound on its differentiation error under noisy measurements. For sample-based differentiators, the concept of quasi-exactness is introduced to classify differentiators that achieve the lowest possible worst-case error based on sampled measurements in the absence of noise. A straightforward sample-based implementation of the proposed linear adaptive continuous-time differentiator is shown to achieve quasi-exactness after a single sampling step as well as a theoretically optimal differentiation error bound that, in addition, converges to the continuous-time optimal one as the sampling period becomes arbitrarily small. A numerical simulation illustrates the presented formal results.
Original language | English |
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Article number | 110725 |
Number of pages | 13 |
Journal | Automatica |
Volume | 148 |
DOIs | |
Publication status | Published - Feb 2023 |
Keywords
- Differentiation
- Optimal worst-case accuracy
- Discrete-time implementation
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Control and Systems Engineering
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- 1 Invited talk
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Optimal Robust Exact Differentiation via Linear Adaptive Techniques
Richard Seeber (Speaker)
24 Nov 2021Activity: Talk or presentation › Invited talk › Science to science