High-Order Control Barrier Function-Based Robust Safety-Critical Control With Sampled-Data Input.

Area of Science:

• Control Theory
• Nonlinear Dynamical Systems
• Robotics

Background:

• Ensuring safety in nonlinear dynamical systems with model uncertainties is challenging.
• Sampled-data control introduces complexities due to discrete-time inputs.
• Robustness against model uncertainties is critical for real-world applications.

Purpose of the Study:

• To develop a robust control approach for sampled-data nonlinear systems with model uncertainties.
• To guarantee the forward invariance of safe sets under these conditions.
• To mitigate the impact of uncertainty observation errors on safety constraints.

Main Methods:

• Design of a continuous-time composite controller with uncertainty compensation and state feedback.
• Integration of a nonlinear observer for uncertainty estimation.
• Formulation of a sampled-data controller using a quadratic program (QP) with modified high order control barrier function (HOCBF) constraints.
• Derivation of sufficient conditions for robust forward invariance.

Main Results:

• The proposed controller ensures robust forward invariance of safe sets for uncertain sampled-data nonlinear systems.
• The method effectively mitigates the adverse effects of uncertainty observation errors.
• Simulation experiments validate the successful safety assurance of the proposed approach.

Conclusions:

• The developed sampled-data control strategy effectively guarantees system safety.
• The approach provides a robust solution for nonlinear dynamical systems with model uncertainties.
• This work contributes to the reliable operation of safety-critical systems with discrete-time control.