Segment-Safe Control Barrier Functions for Model Predictive Control

Model Predictive Control (MPC) is a widely adopted advanced control strategy renowned for its ability to handle complex system dynamics, constraints, and multi-objective optimization over a receding horizon. Concurrently, Control Barrier Functions (CBFs) have emerged as a robust mathematical framework for guaranteeing safety in dynamic systems by ensuring that the system state remains within a predefined safe set. The integration of safety-critical constraints into MPC formulations is a significant challenge, particularly when dealing with systems that exhibit distinct operational segments or hybrid behaviors. This research explores the development and application of Segment-Safe Control Barrier Functions within an MPC framework. The proposed approach aims to enhance the safety guarantees of MPC

by systematically incorporating segment-specific safety constraints, thereby ensuring safe operation across various modes or phases of a system's trajectory. This involves formulating CBFs that adapt to or define safety for discrete operational segments, which are then integrated into the MPC optimization problem. The objective is to achieve optimal performance while rigorously adhering to safety specifications, even in the presence of uncertainties or disturbances. Such a methodology offers a promising direction for designing resilient and safe control systems applicable to a broad spectrum of engineering disciplines.

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