Advanced Control Strategies for Autonomous Engineering Systems

  • Authors

    • Dr. Andrew Collins Professor, University of Oxford, UK Author
    • Dr. Emma Roberts Associate Professor, University of Cambridge, UK Author

    DOI:

    https://doi.org/10.67228/30716357/IJMRSE-2023PII4U9N

    Published 09-02-2023

  • Autonomous Engineering Systems, Adaptive Control, Robust Control, Intelligent Systems, Model Predictive Control, Fuzzy Logic Control, Sliding Mode Control, Robotics, Autonomous Navigation, Hybrid Control Systems, Artificial Intelligence, Industrial Automation

    Issue

    Vol. 6 No. 2 (2023)

    Section

    Articles

    How to Cite

    Advanced Control Strategies for Autonomous Engineering Systems. (2023). International Journal of Modern Research in Science & Engineering, 6(2), 01-16. https://doi.org/10.67228/30716357/IJMRSE-2023PII4U9N

  • Abstract

    Autonomous engineering systems are a major technological advancement in modern industry and science, operating with minimal human intervention while ensuring high accuracy, reliability, adaptability, and efficiency. Before 2019, developments in control engineering, artificial intelligence, robotics, and embedded systems led to advanced autonomous control architectures. Applications such as intelligent transportation, industrial automation, aerospace systems, smart manufacturing, underwater exploration, and unmanned aerial vehicles increased the demand for advanced control strategies capable of handling nonlinearities, uncertainties, and environmental disturbances. Traditional methods like PID control were effective for linear systems but limited in dynamic environments. Therefore, advanced techniques such as adaptive control, robust control, model predictive control, fuzzy logic, neural network-based control, sliding mode control, and hybrid intelligent control became widely used. These methods improved stability, trajectory tracking, fault tolerance, and decision-making. This study reviews advanced control strategies for autonomous engineering systems up to 2019, covering theoretical concepts, mathematical models, architectures, and practical applications in robotics, aerospace, industrial automation, and intelligent transportation. It also highlights challenges including sensor uncertainty, communication delays, computational complexity, and environmental disturbances. Comparative analysis shows that advanced control methods outperform traditional techniques in accuracy, robustness, energy efficiency, and disturbance rejection. The study concludes that these strategies are the foundation of next-generation autonomous systems, with future research focusing on adaptive optimization, distributed intelligence, deep reinforcement learning, and collaborative robotics.

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