Predictive Control Barrier Functions for Discrete-Time Linear Systems with Unmodeled Delays
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916983130292224 |
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| author | Salazar, Juan Augusto Paredes Usevitch, James Goel, Ankit |
| author_facet | Salazar, Juan Augusto Paredes Usevitch, James Goel, Ankit |
| contents | This paper introduces a predictive control barrier function (PCBF) framework for enforcing state constraints in discrete-time systems with unknown relative degree, which can be caused by input delays or unmodeled input dynamics. Existing discrete-time CBF formulations typically require the construction of auxiliary barrier functions when the relative degree is greater than one, which complicates implementation and may yield conservative safe sets. The proposed PCBF framework addresses this challenge by extending the prediction horizon to construct a CBF for an associated system with relative degree one. As a result, the superlevel set of the PCBF coincides with the safe set, simplifying constraint enforcement and eliminating the need for auxiliary functions. The effectiveness of the proposed method is demonstrated on a discrete-time double integrator with input delay and a bicopter system with position constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_01059 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predictive Control Barrier Functions for Discrete-Time Linear Systems with Unmodeled Delays Salazar, Juan Augusto Paredes Usevitch, James Goel, Ankit Systems and Control Robotics Optimization and Control This paper introduces a predictive control barrier function (PCBF) framework for enforcing state constraints in discrete-time systems with unknown relative degree, which can be caused by input delays or unmodeled input dynamics. Existing discrete-time CBF formulations typically require the construction of auxiliary barrier functions when the relative degree is greater than one, which complicates implementation and may yield conservative safe sets. The proposed PCBF framework addresses this challenge by extending the prediction horizon to construct a CBF for an associated system with relative degree one. As a result, the superlevel set of the PCBF coincides with the safe set, simplifying constraint enforcement and eliminating the need for auxiliary functions. The effectiveness of the proposed method is demonstrated on a discrete-time double integrator with input delay and a bicopter system with position constraints. |
| title | Predictive Control Barrier Functions for Discrete-Time Linear Systems with Unmodeled Delays |
| topic | Systems and Control Robotics Optimization and Control |
| url | https://arxiv.org/abs/2510.01059 |