Jul 2026· 2026 5th International Symposium on Control Engineering and Robotics (ISCER)· pp. 114-119· 0 citations· 16 references
Abstract
This paper presents a compact Certified Robust Control Barrier Function (CR-CBF) framework for safe highspeed robotic control under model uncertainty. Conventional control barrier functions depend on accurate dynamics, while robust barriers often impose fixed conservative margins that reduce motion efficiency. The proposed method combines a Proximal Policy Optimization policy, a concurrent learning uncertainty estimator, and a quadratic-programming safety shield. The estimator separates learnable structural mismatch from bounded disturbances and updates a dynamic safety margin in real time. A lightweight Gaussian Process uncertainty check and warm-started QP implementation keep the control loop below one millisecond. In 1,000 Monte Carlo simulations of a sixdegree-of-freedom manipulator under a sudden 5-30 kg payload shift and wind shear, the CR-CBF achieved zero collisions, matched the survival of static robust CBF, recovered 88% of the lost speed, and adapted within 0.8 s.
Control barrier functions are an effective model-based tool to formally certify the safety of a system. However, transferring their theoretical guarantees to real-world robotics systems requires high model fidelity. For example, payloads or wind disturbances can cause significant model perturbations to an aerial vehicl...
Li-Shuo Pan, Mattia Catellani, An Cao et al.· 0 citations
This work develops two new control barrier function (CBF) formulations: drift-measurement-robust (DMR)-CBFs and neural measurement-robust (NMR)-CBFs and provides theoretical analysis of the DMR-CBF along with numerical results on a planar double integrator and a 12D quadrotor.
Nicholas Rober, Yi-Xuan Jia, Jonathan P. How· 1 citation
Aligning autonomous-vehicle control with human intent remains challenging when safety-related constraints must be considered in closed-loop motion control. This paper proposes a Large Language Model (LLM)-assisted parameterization framework for motion control based on Model Predictive Control (MPC) and Control Barrier...
Yong-Li Li, Yun-Long Song, Xu Zhao et al.· Journal of King Saud Univers...· 0 citations
Although conventional controllers and disturbance observers (DOBs) are the standard for precision tracking in manipulators, they suffer from parameter uncertainty, nonlinear friction, and compound disturbances. This study proposes a residual reinforcement learning DOB framework that pairs an analytical observer with an...
Jihong Kim, Joonhyuk Kwon, H. Kim et al.· 0 citations
We develop a robust safety filter for input-constrained underactuated linear systems subject to unknown bounded-rate disturbances and structured model uncertainty entering through known distribution channels. A disturbance observer provides an online disturbance estimate and a dynamic estimation-error radius that defin...
Control barrier functions (CBFs) certify commands through an assumed dynamics model, so an abrupt, unmeasured regime change can undermine the certificate exactly when safety matters most. We present Look-Back Adaptive Control Barrier Functions (LBA-CBF), which rank a finite bank of candidate dynamics by recent predicti...
Maitham Al-Sunni, Timeea-Andreea Radu, Hassan Almubarak et al.· 1 citation
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