The optimal performance of robotic systems is usually achieved near the limit of state and
input bounds. Model predictive control (MPC) is a prevalent strategy to handle these …

This paper develops a new control design for guaranteeing a vehicle's safety during lane
change maneuvers in a complex traffic environment. The proposed method uses a finite …

When designing a controller for the autonomous vehicle system, safety and trajectory
tracking performance are two major concerns. This letter proposes a novel control design for …

Autonomous vehicles face tremendous challenges while interacting with human drivers in
different kinds of scenarios. Develo** control methods with safety guarantees while …

We propose a novel class of risk-aware control barrier functions (RA-CBFs) for the control of
stochastic safety-critical systems. Leveraging a result from the stochastic level-crossing …

Deep Reinforcement Learning (DRL) has been successfully applied to learn policies for
safety-critical systems with unknown model dynamics in simulation. DRL controllers though …

Safety is one of the fundamental challenges in control theory. Recently, multi-step optimal
control problems for discrete-time dynamical systems were formulated to enforce stability …

This paper presents a distributed model predictive control (DMPC) algorithm to achieve the
combined longitudinal and lateral control of automated vehicle platoon on curved roads. To …

This work presents a unified approach for collision avoidance using Collision-Cone Control
Barrier Functions (CBFs) in both ground (UGV) and aerial (UAV) unmanned vehicles. We …

Control Barrier Functions (CBF) are widely used to enforce the safety-critical constraints on
nonlinear systems. Recently, these functions are being incorporated into a path planning …