Author(s)

Qi Chen ¹, Shishuai Zhao ¹, Zhuo Wang ¹

Affiliation(s)

¹ School of Mechanical Engineering, University of Shanghai for Science and Technology, Jungong Road No.516, Shanghai, China

Corresponding Author

Zhuo Wang

ABSTRACT

The integration of Virtual Laboratories (VL) and Artificial Intelligence (AI) is revolutionizing PLC education by enabling scalable and personalized learning. This shift also offers a strategic opportunity to embed Ideological and Political Education (IPE). This paper contends that VL and AI can address both traditional teaching constraints and serve as a vehicle for instilling engineering ethics, cultural confidence, and a sense of national mission in students. For instance, learning efficiency is improved by 40% and AI boosts course completion by 25%. This approach not only enhances technical skills but also cultivates social responsibility and technological self-reliance. Ultimately, this model supports the development of ethically grounded, technically skilled engineers. An advanced module on flexible manipulator control—integrating artificial potential field–based MPC and improved meta-heuristic optimization—exemplifies how AI-enhanced simulation can merge control theory with ideological education, improving both technical and value-based competencies.

KEYWORDS

Ideological and Political Education (IPE); Virtual Laboratory (VL); Artificial Intelligence (AI); PLC Education; Flexible Manipulator; Model Predictive Control (MPC)

CITE THIS PAPER

Qi Chen, Shishuai Zhao, Zhuo Wang, AI-Driven Virtual Laboratory Framework for Advanced Control Education: Integrating Ideological and Political Competence through Flexible Manipulator Simulation. Transactions on Comparative Education (2025) Vol. 7: 6-10. DOI: http://dx.doi.org/10.23977/trance.2025.070302.

REFERENCES

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[2] Smith, J., & Lee, K. (2022). Virtual Laboratories in Engineering Education: A Meta-Analysis. Journal of Technical Education, 15(3), 45-60.
[3] Müller, A., et al. (2023). Open-Source Platforms for PLC Simulation: A Case Study of OpenPLC. International Conference on Engineering Education, 78-85.
[4] Gupta, R., & Wang, L. (2021). AI-Driven Adaptive Learning Systems for Industrial Automation Training. IEEE Transactions on Education, 64(4), 512-520.
[5] T. Zhu et al., Real-Time Dynamic Obstacle Avoidance for Robot Manipulators Based on Cascaded Nonlinear MPC with Artificial Potential Field, IEEE Trans. Ind. Electron., 2024.
[6] Y. Dai et al., A Novel Whale Optimization Algorithm of Path Planning Strategy for Mobile Robots, Appl. Intelligence, 2023.
[7] IEEE. (2023). Ethical Guidelines for AI in Education. Piscataway: IEEE Standards Association.

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