Table of Contents
Fetching ...

Identifiability and Observability of Nonsmooth Systems via Taylor-like Approximations

Peter Stechlinski, Sameh Eisa, Hesham Abdelfattah

Abstract

New sensitivity-based methods are developed for determining identifiability and observability of nonsmooth input-output systems. More specifically, lexicographic calculus is used to construct nonsmooth sensitivity rank condition (SERC) tests, which we call lexicographic SERC (L-SERC) tests. The introduced L-SERC tests are: (i) practically implementable and amenable to large-scale problems; (ii) accurate since they directly treat the nonsmoothness while avoiding, e.g., smoothing approximations; and (iii) analogous to (and indeed recover) their smooth counterparts. To accomplish this, a first-order Taylor-like approximation theory is developed using lexicographic differentiation to directly treat nonsmooth functions. A practically implementable algorithm is proposed that determines partial structural identifiability or observability, a useful characterization in the nonsmooth setting. Lastly, the theory is illustrated through an application in climate modeling.

Identifiability and Observability of Nonsmooth Systems via Taylor-like Approximations

Abstract

New sensitivity-based methods are developed for determining identifiability and observability of nonsmooth input-output systems. More specifically, lexicographic calculus is used to construct nonsmooth sensitivity rank condition (SERC) tests, which we call lexicographic SERC (L-SERC) tests. The introduced L-SERC tests are: (i) practically implementable and amenable to large-scale problems; (ii) accurate since they directly treat the nonsmoothness while avoiding, e.g., smoothing approximations; and (iii) analogous to (and indeed recover) their smooth counterparts. To accomplish this, a first-order Taylor-like approximation theory is developed using lexicographic differentiation to directly treat nonsmooth functions. A practically implementable algorithm is proposed that determines partial structural identifiability or observability, a useful characterization in the nonsmooth setting. Lastly, the theory is illustrated through an application in climate modeling.
Paper Structure (7 sections, 5 theorems, 75 equations, 2 figures, 1 algorithm)

This paper contains 7 sections, 5 theorems, 75 equations, 2 figures, 1 algorithm.

Key Result

theorem 1

Let $X \subseteq \real^n$ be open and $\f:X \to \real^n$ L-smooth at $\xnot \in X$. Then,

Figures (2)

  • Figure 1: Trajectories of the state variables $T$, $V$, and the output $y$. The output $y$ crosses a nonsmooth threshold when $T(t)=T_{\min}$ (indicated by the first vertical dashed line). The second vertical dashed line corresponds to a nonsmooth threshold in the model being crossed when $T(t)=V(t)$.
  • Figure 2: This figure presents the output sensitivity function $\caps_{\y}^{\rm L}(t)=\mathrm{lshift}(\capy^*(t)) \in \real^{1 \times 3}$, with $\capy^*(t)$ from \ref{['eq:Stommel_X_dot']} for identifiability (left panel), and $\caps_{\y}^{\rm L}(t)=\mathrm{lshift}(\capy^*(t)) \in \real^{1 \times 2}$, with $\capy^*(t)$ from \ref{['eq:Stommel_X_dot_obs']} for observability (right panel). When computing $\caps_{\y}(t)$ for both the identifiability and observability, we can see that, as shown in Figure \ref{['fig:Stommel Solution vs Output']}, the nonsmoothness happens in the output when $T(t)=T_{\min}$ (first dashed line) and in the model when $T(t)=V(t)$ (second dashed line). The stars in this figure represent the sample times $\{t_k\}$ used to construct ${\pmb \Upsilon}_{\dd}$ in \ref{['LSERC_ID']}.

Theorems & Definitions (13)

  • theorem 1
  • corollary 1
  • remark 1
  • definition 1
  • definition 2
  • theorem 2
  • definition 3
  • definition 4
  • theorem 3
  • remark 2
  • ...and 3 more