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Prof. Sivanesan Subramanian

Anna University, India

 

Prof. Hassan Karimi-Maleh

University of Electronic Science
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Home > Archives > Vol. 8 No. 1 (2025): Vol. 8 No. 1(Publishing) > Original Research Article
ACE-5585

Published

2025-02-19

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Vol. 8 No. 1 (2025): Vol. 8 No. 1(Publishing)

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Original Research Article

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Copyright (c) 2025 Xinmei Peng, Xinyuan Cai, Weijie Zhou, Wei Li

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How to Cite

Peng, X., Cai, X., Zhou, W., & Li, W. (2025). Trajectory tracking of chemical engineering robotic arms based on improved nonlinear active disturbance rejection control. Applied Chemical Engineering, 8(1), ACE-5585. https://doi.org/10.59429/ace.v8i1.5585
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Trajectory tracking of chemical engineering robotic arms based on improved nonlinear active disturbance rejection control

Xinmei Peng

School of Mechanical Engineering and Transportation, Southwest Forestry University,Kunming, 650224, China

Xinyuan Cai

School of Mechanical Engineering and Transportation, Southwest Forestry University,Kunming, 650224, China

Weijie Zhou

School of Mechanical Engineering and Transportation, Southwest Forestry University,Kunming, 650224, China

Wei Li

School of Mechanical Engineering and Transportation, Southwest Forestry University,Kunming, 650224, China


DOI: https://doi.org/10.59429/ace.v8i1.5585



Abstract

With the increasing demand for automation and precision in chemical engineering processes, robotic arms play a crucial role in enhancing production efficiency and product quality. Traditional control methods often struggle to cope with the complex dynamic environments and unpredictable disturbances inherent in chemical engineering applications. This study presents an improved Nonlinear Active Disturbance Rejection Control (NLADRC) method for dynamic trajectory tracking of chemical engineering robotic arms. Leveraging the support of the Yunnan Province Major Science and Technology Project, the proposed NLADRC framework integrates an enhanced disturbance observer and adaptive control strategies to effectively mitigate unknown disturbances and parameter variations. Experimental results demonstrate that the NLADRC method significantly outperforms traditional PID and standard ADRC controllers in terms of tracking accuracy, response speed, and robustness. The findings provide a robust theoretical foundation and practical guidelines for the deployment of advanced control strategies in chemical engineering robotic systems.


References

[1]. Zhang, S., Li, F. (2020). A study on trajectory tracking control of robotic arms based on ADRC. Control Engineering, 27(4), 123-130.

[2]. Li, F., Wang, W. (2021). Application of Nonlinear ADRC in robotic arm control systems. Journal of Mechanical Engineering, 57(2), 456-465.

[3]. Wang, W., Zhao, L. (2019). Overview of Active Disturbance Rejection Control methods and applications. Journal of Automation Science, 45(6), 789-800.

[4]. Brown, A., Green, R. (2019). A comparative study of machine learning algorithms in healthcare applications. Medical Informatics Journal, 28(2), 45-56.

[5]. Zhang, Y., et al. (2021). Predicting surgical outcomes with machine learning models: A case study in a large hospital. Artificial Intelligence in Medicine, 39(1), 67-78.

[6]. Singh, A. V., et al. (2019). Bottom-UP assembly of nanorobots: extending synthetic biology to complex material design. Frontiers in Nanoscience and Nanotechnology, 5(1).

[7]. Singh, A. V., et al. (2021). Emerging application of nanorobotics and artificial intelligence to cross the BBB: advances in design, controlled maneuvering, and targeting of the barriers. ACS chemical neuroscience, 12(11), 1835-1853.

[8]. International Federation of Robotics (2020). World Robotics Report 2020.

[9]. Impact of Chemical Engineering: About Us: Department of Chemical Engineering: University of Rochester.

[10]. National Research Council., et al. (2004). Health and Medicine: Challenges for the Chemical Sciences in the 21st Century.

[11]. Zhou, W., Guo, S., Guo, J., et al. (2021). ADRC-based control method for the vascular intervention master–slave surgical robotic system. Micromachines, 12(12), 1439.

[12]. Wang, G., Fang, S. (2024). The Precision Improvement of Robot Integrated Joint Module Based on a New ADRC Algorithm. Machines, 12(10), 712.

[13]. Feng, X., Liu, S., Yuan, Q., et al. (2023). Research on wheel-legged robot based on LQR and ADRC. Scientific reports, 13(1), 15122.

[14]. Lin, K. H. (2023). Advances in Nonlinear Model Predictive Control and Their Applications in Chemical Engineering (Doctoral dissertation, Carnegie Mellon University).

[15]. What Are the Opportunities for Nonlinear Control in Process Industry Applications? Available online: https://blog.isa.org/opportunities-nonlinear-control-process-industry-applications. (accessed on 14 February 2025).

[16]. Iqbal, J., Ullah, M., Khan, S. G., et al. (2017). Nonlinear control systems-A brief overview of historical and recent advances. Nonlinear Engineering, 6(4), 301-312.

[17]. Siciliano, B., Khatib, O., & Kröger, T. (Eds.). (2008). Springer handbook of robotics (Vol. 200). Berlin: springer.

[18]. Chandrasekar, V., et al. (2025). Quantitative prediction of toxicological points of departure using two-stage machine learning models: A new approach methodology (NAM) for chemical risk assessment. Journal of Hazardous Materials, 487, 137071.

[19]. Singh, A. V., et al. (2024). AI and ML-based risk assessment of chemicals: predicting carcinogenic risk from chemical-induced genomic instability. Frontiers in Toxicology, 6, 1461587.



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