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A Data-Driven Krasovskii-Based Approach for Safety Controller Design of Time-Delayed Uncertain Polynomial Systems

Omid Akbarzadeh, MohammadHossein Ashoori, Amy Nejati, Abolfazl Lavaei

TL;DR

This work develops a data-driven framework to guarantee safety for discrete-time uncertain polynomial systems with unknown dynamics, disturbances, and time delays by extending control barrier certificates to Krasovskii functionals (RK-CBC). The method synthesizes RK-CBCs and robust safety controllers (R-SC) directly from finite input–state data via a sum-of-squares (SOS) optimization, reducing computational complexity relative to model-based delay formulations. The approach is validated on three case studies (Academic, Jet Engine Compressor, Spacecraft), demonstrating robust infinite-horizon safety under unknown disturbances and delays. The contributions enable safe operation of safety-critical cyber-physical systems without explicit system models, with practical implications for networked control and delayed-feedback scenarios.

Abstract

We develop a data-driven framework for the synthesis of robust Krasovskii control barrier certificates (RK-CBC) and corresponding robust safety controllers (R-SC) for discrete-time input-affine uncertain polynomial systems with unknown dynamics, while explicitly accounting for unknown-but-bounded disturbances and time-invariant delays using only observed input-state data. Although control barrier certificates have been extensively studied for safety analysis of control systems, existing work on unknown systems with time delays, particularly in the presence of disturbances, remains limited. The challenge of safety synthesis for such systems stems from two main factors: first, the system's mathematical model is unavailable; and second, the safety conditions should explicitly incorporate the effects of time delays on system evolution during the synthesis process, while remaining robust to unknown disturbances. To address these challenges, we develop a data-driven framework based on Krasovskii control barrier certificates, extending the classical CBC formulation for delay-free systems to explicitly account for time delays by aggregating delayed components within the barrier construction. The proposed framework relies solely on input-state data collected over a finite time horizon, enabling the direct synthesis of RK-CBC and R-SC from observed trajectories without requiring an explicit system model. The synthesis is cast as a data-driven sum-of-squares (SOS) optimization program, yielding a structured design methodology. As a result, robust safety is guaranteed in the presence of unknown disturbances and time delays over an infinite time horizon. The effectiveness of the proposed method is demonstrated through three case studies, including two physical systems.

A Data-Driven Krasovskii-Based Approach for Safety Controller Design of Time-Delayed Uncertain Polynomial Systems

TL;DR

This work develops a data-driven framework to guarantee safety for discrete-time uncertain polynomial systems with unknown dynamics, disturbances, and time delays by extending control barrier certificates to Krasovskii functionals (RK-CBC). The method synthesizes RK-CBCs and robust safety controllers (R-SC) directly from finite input–state data via a sum-of-squares (SOS) optimization, reducing computational complexity relative to model-based delay formulations. The approach is validated on three case studies (Academic, Jet Engine Compressor, Spacecraft), demonstrating robust infinite-horizon safety under unknown disturbances and delays. The contributions enable safe operation of safety-critical cyber-physical systems without explicit system models, with practical implications for networked control and delayed-feedback scenarios.

Abstract

We develop a data-driven framework for the synthesis of robust Krasovskii control barrier certificates (RK-CBC) and corresponding robust safety controllers (R-SC) for discrete-time input-affine uncertain polynomial systems with unknown dynamics, while explicitly accounting for unknown-but-bounded disturbances and time-invariant delays using only observed input-state data. Although control barrier certificates have been extensively studied for safety analysis of control systems, existing work on unknown systems with time delays, particularly in the presence of disturbances, remains limited. The challenge of safety synthesis for such systems stems from two main factors: first, the system's mathematical model is unavailable; and second, the safety conditions should explicitly incorporate the effects of time delays on system evolution during the synthesis process, while remaining robust to unknown disturbances. To address these challenges, we develop a data-driven framework based on Krasovskii control barrier certificates, extending the classical CBC formulation for delay-free systems to explicitly account for time delays by aggregating delayed components within the barrier construction. The proposed framework relies solely on input-state data collected over a finite time horizon, enabling the direct synthesis of RK-CBC and R-SC from observed trajectories without requiring an explicit system model. The synthesis is cast as a data-driven sum-of-squares (SOS) optimization program, yielding a structured design methodology. As a result, robust safety is guaranteed in the presence of unknown disturbances and time delays over an infinite time horizon. The effectiveness of the proposed method is demonstrated through three case studies, including two physical systems.
Paper Structure (12 sections, 4 theorems, 74 equations, 4 figures, 1 table, 1 algorithm)

This paper contains 12 sections, 4 theorems, 74 equations, 4 figures, 1 table, 1 algorithm.

Key Result

Theorem 1

Given a dt-IAUPS-td, let $\mathcal{B}$ be an RK-CBC for $\Upsilon$ as defined in Definition def: CBC, with $\gamma \delta$ satisfying eq: climit. Then, for any initial sequence $\mathbf{x}_0 \in \mathcal{X}^{h+1}_a$ and $k \in \mathbb{N}$ under input and disturbance signals $u(\cdot)$ and $w(\cdot)$

Figures (4)

  • Figure 1: Architecture of the proposed framework, highlighting the key components for data-driven safety controller synthesis, with $X_{-}$ and $X_{h}$ corresponding to the collected data sets (cf. \ref{['eq: datarep1']}).
  • Figure 2: Academic system. Plot (a) illustrates the open-loop trajectories, while plot (b) displays the trajectories under the designed safety controller in \ref{['Cont']}, starting from different initial conditions in $\mathcal{X}_a \subset [-1,1]^2$, with the initial and unsafe bounds in \ref{['Th:con1']} and \ref{['Th:con2']} indicated by and , respectively. The simulations are generated using $15$ different arbitrary disturbance trajectories satisfying \ref{['eq:Wdelta']}, indicating the robustness of the proposed framework to disturbances. Plot (c) depicts the trajectories over $50$ time steps, demonstrating compliance with the specified safety property $\Theta$.
  • Figure 3: Jet engine compressor. Plot (a) shows trajectories under a random controller, while plot (b) presents trajectories with the designed safety controller in \ref{['Cont1']}, initialized from $\mathcal{X}_a \subset [-2,2]^2$, with the initial and unsafe bounds in \ref{['Th:con1']} and \ref{['Th:con2']} indicated by and , respectively. Results are obtained using $25$ disturbance realizations satisfying \ref{['eq:Wdelta']}, demonstrating robustness to disturbances. Plot (c) depicts the state evolution over $100$ time steps, confirming satisfaction of the safety specification $\Theta$.
  • Figure 4: Spacecraft. Plot (a) presents trajectories under a random controller, whereas plot (b) shows trajectories with the synthesized safety controller in \ref{['safety-c-3']}, initialized from $\mathcal{X}_a \subset [-2,2]^3$, with the initial and unsafe bounds in \ref{['Th:con1']} and \ref{['Th:con2']} marked by and , respectively. The results are acquired using $25$ disturbance realizations fulfilling \ref{['eq:Wdelta']}, illustrating robustness to disturbances. Plot (c) depicts the state evolution over $100$ time steps, confirming adherence to the safety specification $\Theta$.

Theorems & Definitions (16)

  • Definition 1: dt-IAUPS-td
  • Definition 2: Infinite Robust Safety Property
  • Remark 1
  • Definition 3: RK-CBC
  • Theorem 1: Infinite Robust Safety Guarantee
  • proof
  • Remark 2
  • Lemma 1: Parameterization of dt-IAUPS-td
  • proof
  • Remark 3
  • ...and 6 more