Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function

by   Jun Zeng, et al.

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 operational constraints, however, safety still remains an open challenge for MPC as it needs to guarantee that the system stays within an invariant set. In order to obtain safe optimal performance in the context of set invariance, we present a safety-critical model predictive control strategy utilizing discrete-time control barrier functions (CBFs), which guarantees system safety and accomplishes optimal performance via model predictive control. We analyze the stability and the feasibility properties of our control design. We verify the properties of our method on a 2D double integrator model for obstacle avoidance. We also validate the algorithm numerically using a competitive car racing example, where the ego car is able to overtake other racing cars.



There are no comments yet.


page 1

page 2

page 3

page 4


Obstacle avoidance-driven controller for safety-critical aerial robots

The goal of this thesis is to propose the combination of Control-Barrier...

Enhancing Feasibility and Safety of Nonlinear Model Predictive Control with Discrete-Time Control Barrier Functions

Safety is one of the fundamental problems in robotics. Recently, one-ste...

Learning Differentiable Safety-Critical Control using Control Barrier Functions for Generalization to Novel Environments

Control barrier functions (CBFs) have become a popular tool to enforce s...

Safe Control with Neural Network Dynamic Models

Safety is critical in autonomous robotic systems. A safe control law ens...

Comparison between safety methods control barrier function vs. reachability analysis

This report aims to compare two safety methods: control barrier function...

Model-Free Safety-Critical Control for Robotic Systems

This paper presents a framework for the safety-critical control of robot...

Safety Embedded Differential Dynamic Programming using Discrete Barrier States

Certified safe control is a growing challenge in robotics, especially wh...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.