1 citations · 1 across the 4 of their papers we have counts for
4 papers · 1 filter
Learning-Enabled Iterative Convex Optimization for Safety-Critical Model Predictive Control
Shuo Liu, Zhe Huang, Jun Zeng +2
Safety remains a central challenge in control of dynamical systems, particularly when the boundaries of unsafe sets are complex (e.g., nonconvex, nonsmooth) or unknown. This paper…
Enhancing Feasibility and Safety of Nonlinear Model Predictive Control with Discrete-Time Control Barrier Functions
Jun Zeng, Zhongyu Li, Koushil Sreenath
Safety is one of the fundamental problems in robotics. Recently, one-step or multi-step optimal control problems for discrete-time nonlinear dynamical system were formulated to off…
Rule-Based Safety-Critical Control Design using Control Barrier Functions with Application to Autonomous Lane Change
Suiyi He, Jun Zeng, Bike Zhang +1
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 state…
Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function
Jun Zeng, Bike Zhang, Koushil Sreenath
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 oper…