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A Comprehensive Review on Control Barrier Functions: Uncertainty Handling, Design Optimization, and Feasibility
Control barrier functions (CBFs) ensure safety in control-affine systems. This review details advancements in handling uncertainty, optimizing structure, and assuring feasibility for practical CBF applications in robotics and autonomous systems.
Area of Science:
- Control Theory
- Robotics
- Autonomous Systems
Background:
- Control barrier functions (CBFs) offer a formal safety guarantee for control-affine systems.
- Real-world deployment of CBFs is hindered by practical challenges.
Purpose of the Study:
- To review recent advancements in Control Barrier Function (CBF) methodologies.
- To analyze progress in uncertainty handling, structural optimization, and feasibility assurance for CBFs.
Main Methods:
- Categorization of uncertainty handling strategies (robust, learning-based).
- Examination of structural design choices (class-K functions, parameter tuning).
- Analysis of CBF-Quadratic Programming (CBF-QP) infeasibility causes and solutions.
Main Results:
- Diverse methods exist for robustly and adaptively handling uncertainty in CBF design.
- Optimized structural parameters and class-K functions reduce conservatism and improve feasibility.
- Strategies like constraint relaxation and redesign address CBF-QP infeasibility.
Conclusions:
- Synthesizing progress reveals interdependencies between uncertainty, structure, and feasibility.
- Future research should focus on advancing CBF-based safety-critical control for complex systems.
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