Dynamic Obstacle Avoidance Using Nonlinear Model Predictive Control and Control Barrier Function for Ballbot Systems
Year: 2024
Type: article
Abstract: This research presents a tracking control system for a ballbot designed to operate in complex environments filled with both static and dynamic obstacles. The Nonlinear Model Predictive Control (NMPC) framework is formulated to predict the future positions of the ballbot and all surrounding obstacles. This predictive capability is crucial for effective navigation, as it allows the ballbot to anticipate potential collisions in the prediction horizon. The NMPC is integrated with an optimization problem that is enhanced by Control Barrier Function (CBF) constraints. These constraints ensure that the ballbot maintains a safe and consistent distance from every obstacle, thus preventing collisions. Additionally, an Extended State Observer (ESO) is implemented to observe and compensate for uncertain disturbances in the ballbot’s movements, as well as to estimate immeasurable variables that might affect its performance. Various simulation scenarios are conducted to thoroughly test and validate the effectiveness of this approach in achieving precise tracking control and reliable collision avoidance in environments with a large number of obstacles.
Source: Smart Systems and Devices
Authors: Minh Duc Pham, Duc Cuong Vu, Thi Thuy Hang Nguyen, Danh Huy Nguyen, Thi Van Anh Nguyen, Tung Lam Nguyen
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Funder: Trường Đại học Bách Khoa Hà Nội
Award: G2072565437
OpenAlex ID: W4402929794