Non-linear Control Strategies for Robot-Environment Interaction Simulation in Complex Environments

Fenglin Hu

doi:10.47297/taposatWSP2633-456913.20250609

PDF
Export
Share
Collection
Album

Non-linear Control Strategies for Robot-Environment Interaction Simulation in Complex Environments

Volume 6, Issue 9

Views: 0 Downloads: 2

Non-linear Control Strategies for Robot-Environment Interaction Simulation in Complex Environments
Non-linear Control Strategies for Robot-Environment Interaction Simulation in Complex Environments
Institute of Problem Solving, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu, 215000, China
Vol. 6, Issue 9, Pages: 66-70(2025)
Published：30 September 2025
DOI：10.47297/taposatWSP2633-456913.20250609
Accepted：

Scan QR Code

Hu Fenglin.Non-linear Control Strategies for Robot-Environment Interaction Simulation in Complex Environments[J].Theory and Practice of Science and Technology,2025,06(09):66-70.
DOI：

Hu Fenglin.Non-linear Control Strategies for Robot-Environment Interaction Simulation in Complex Environments[J].Theory and Practice of Science and Technology,2025,06(09):66-70. DOI： 10.47297/taposatWSP2633-456913.20250609.

摘要

Abstract

This paper presents a scheme for the design and verification of a nonlinear control strategy based on Lyapunov theory

aiming to enhance the robot's interaction ability in the environment by comparing linear and nonlinear systems. The proposed scheme focuses on improving the stability and adaptability of the robot control system

while optimizing sensor data processing to achieve real-time perception and task tracking. Simulation experiments were conducted in the ROS + Gazebo environment

and various scenarios were evaluated. Comparative experiments show that the proposed nonlinear control strategy is expected to outperform the traditional PID controller in terms of tracking accuracy

response delay

and task completion rate

demonstrating its potential for practical deployment in industrial and logistics applications.

关键词

Keywords

references

Rodrigues, H.M., Solà-Morales, J. (2024) New examples on Lyapunov stability. J. Dyn. Differ. Equ., 36 Suppl. 1:65–75.

Hart, J., et al. (2022) Sensitivity-based experimental design to facilitate control o f dynamic systems. arXiv preprint arXiv: 2202.03312 .

Zhang, C., Jia, Z., Wang, H., Lin, Y., Zhang, W. (2024) Trajectory tracking for wheeled mobile robot using Lyapunov-based model predictive control. In: China Automation Congress (CAC). Qingdao, China, pp. 4240 – 4245 .

Francis X. Govers III; Dr. Kamesh Namuduri, Artificial Intelligence for Robotics: Build intelligent robots using ROS 2, Python, OpenCV, and AI/ML techniques for real-world tasks , Packt Publishing, 2024.

Megalingam, R.K., A.R., HemaTejaAnirudhBabu, D., Sriram, G., YashwanthAvvari, V.S. (2022) Implementation of a person following robot in ROS-Gazebo platform. In: International Conference for Advancement in Technology (ICONAT). Goa, India, pp. 1 – 5 .

Leiva Calvanapón, O.J., Rebuzzi Vellasco, M.M., Caarls, W., Tanscheit, R. (2024) Implementation of PID and Fuzzy controllers using RGB camera in real robotic vehicle and ROS2-Gazebo platform. In: International Conference on Intelligent Control and Computing (IC & C). Guangzhou, China, pp. 74 – 79 .

Dürschmid, T. (2024) Towards automatic inference of behavioral component models for ROS-based robotics systems. In: IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion). Lisbon, Portugal, pp. 247 – 251 .

Hwang, G.H., Lee, S.W., Jeon, J. (2024) ROS2 implementation of object detection and distance estimation using camera and 2D LiDAR fusion in autonomous vehicle. In: IEEE 33rd International Symposium on Industrial Electronics (ISIE). Ulsan, Korea, pp. 1 – 5 .

Ying, L. (2018) The simulation design of PID control based on LabVIEW and MATLAB. In: International Symposium in Sensing and Instrumentation in IoT Era (ISSI). Shanghai, China, pp. 1 – 8 .

Wee-Kiat, N.D., Ban-Hoe, K., Siow Cheng, C., Goh, C.-H. (2024) 3D printed differential drive robot with LiDAR and 3D depth camera. In: 9th International Conference on Control and Robotics Engineering (ICCRE). Osaka, Japan, pp. 1 – 5 .

Alert me when the article has been cited

Submit

No data

Related Author

No data

Related Institution

No data

⁰