Control Barrier Functions for Sampled-Data Systems with Input Delays
| Autor: | Andrew Singletary, Aaron D. Ames, Yuxiao Chen |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Dynamical systems theory Computer science 020208 electrical & electronic engineering Context (language use) Systems and Control (eess.SY) 02 engineering and technology Invariant (physics) Electrical Engineering and Systems Science - Systems and Control Set (abstract data type) 020901 industrial engineering & automation Optimization and Control (math.OC) Control theory Control system FOS: Electrical engineering electronic engineering information engineering FOS: Mathematics 0202 electrical engineering electronic engineering information engineering State (computer science) Affine transformation Invariant (mathematics) Mathematics - Optimization and Control Numerical stability Sampled data systems |
| Zdroj: | CDC |
| Popis: | This paper considers the general problem of transitioning theoretically safe controllers to hardware. Concretely, we explore the application of control barrier functions (CBFs) to sampled-data systems: systems that evolve continuously but whose control actions are computed in discrete time-steps. While this model formulation is less commonly used than its continuous counterpart, it more accurately models the reality of most control systems in practice, making the safety guarantees more impactful. In this context, we prove robust set invariance with respect to zero-order hold controllers as well as state uncertainty, without the need to explicitly compute any control invariant sets. It is then shown that this formulation can be exploited to address input delays in this system, with the result being CBF constraints that are affine in the input. The results are demonstrated in a high-fidelity simulation of an unstable Segway robotic system in real-time. 6 pages, submitted to CDC 2020 |
| Databáze: | OpenAIRE |
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