Differential equation-driven intelligent control: Integrating AI, Quantum computing, and adaptive strategies for next-generation industrial automation

Yue Cheng; Cheng-Li  Luo; Chen  Zhong; Hong Lin; Dragan Marinkovic; Ji-Huan  He

doi:10.59400/adecp3096

Differential equation-driven intelligent control: Integrating AI, Quantum computing, and adaptive strategies for next-generation industrial automation

Yue Cheng School of Information Engineering, Yango University, Fuzhou 350015, China
Cheng-Li Luo School of Information Engineering, Yango University, Fuzhou 350015, China
Chen Zhong School of Information Engineering, Yango University, Fuzhou 350015, China
Hong Lin School of Information Engineering, Yango University, Fuzhou 350015, China
Dragan Marinkovic Fakultät V—Institut für Mechanik, FG Strukturmechanik und Strukturberechnung, Department of Structural Mechanics, Berlin Institute of Technology, D-10623 Berlin, Germany; Faculty of Mechanical Engineering, University of Nis, 18000 Nis, Serbia
Ji-Huan He School of Information Engineering, Yango University, Fuzhou 350015, China

Article ID: 3096

Keywords: differential equation-driven control; physics-informed neural networks (PINNs); quantum variational algorithms; symbolic regression; Equations as a Service (EaaS); explainable AI (XAI); self-evolving algorithm

Abstract

The increasing intricacy of industrial systems highlights the inadequacies of conventional control theories in the management of high-dimensional nonlinear dynamics, real-time coupling, and multi-scale modelling. This article introduces a transformative paradigm—differential equation-driven intelligent control—that synergizes artificial intelligence (AI), quantum computing, and adaptive strategies to redefine next-generation industrial automation. The following innovations are at the core of this paradigm: Physics-informed neural networks (PINNs) for solving partial differential equations (PDEs), Quantum-enhanced linear algebra for stochastic differential equation (SDE) optimization, and symbolic regression for automated discovery of fractional-order dynamic models. A case study on flexible robotic arm dynamics demonstrates the tunability of hybrid rigid-flexible systems via fractional-order parameters and adaptive Lyapunov-based control. The concept of Equations as a Service (EaaS) is proposed to democratize access to distributed computational solvers, enabling real-time optimization for applications such as drone swarm coordination and carbon-neutral manufacturing. A number of critical challenges are addressed in this text, including the interpretability of AI (for example, through the use of SHAP-based explainability tools), the reliability of hybrid quantum-classical solvers, and ethical governance frameworks. Through interdisciplinary collaboration, the vision for self-evolving factories by 2030 is outlined—where differential equations autonomously refine parameters using real-time sensor data. Examples include smart grids adapting to renewable energy fluctuations at millisecond scales and robotic assembly lines recalibrating dynamics to mitigate material defects. The overarching objective of this paradigm shift, termed EaaS, is to transition differential equations from their traditional role as static descriptors to that of self-optimizing assets. This transition is expected to lay the foundation for resilient, explainable, and sustainable ecosystems in the era of Industry 5.0.

Published

2025-04-24

How to Cite

Cheng, Y., Luo, C.-L., Zhong, C., Lin, H., Marinkovic, D., & He, J.-H. (2025). Differential equation-driven intelligent control: Integrating AI, Quantum computing, and adaptive strategies for next-generation industrial automation. Advances in Differential Equations and Control Processes, 32(1), 3096. https://doi.org/10.59400/adecp3096

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Issue

Vol. 32 No. 1 (2025)

Section

Editorial

This work is licensed under a Creative Commons Attribution 4.0 International License.

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