AI-Driven Intelligent Control Strategies for Industrial ‎Robotics: A Reinforcement Learning Approach

  • Authors

    • Kishore Kunal Professor of Business Analytics, Loyola Institute of Business Administration, Chennai, TamilNadu, India https://orcid.org/0000-0003-4154-690X
    • M. Kathiravan Professor, Dapartment of Computer Science, Saveetha Institute of Medical and Technical Sciences ( SIMATS),TamilNadu, India https://orcid.org/0000-0002-5377-7871
    • Vairavel Madeshwaren Department of Agriculture Engineering, Dhanalakshmi Srinivasan College of Engineering, Coimbatore, TamilNadu, India
    • T. Chandrakala Assistant Professor, Department of Computer Science and Applications,‎ Jawahar Science College, Neyveli, TamilNadu, India
    • Veeramani Ganesan Professor, Department of Management and Business Administration, Jeppiaar Institute of Technology, Sunguvarchatram, Sriperumbudur, ‎TamilNadu, India https://orcid.org/0009-0003-2242-2167
    • Sheifali Gupta Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India https://orcid.org/0000-0001-5692-418X
    https://doi.org/10.14419/1dy21h49

    Received date: May 15, 2025

    Accepted date: June 22, 2025

    Published date: June 27, 2025

  • Adaptive Control; Reinforcement Learning; Edge-AI; Robotic Manipulation; Deep Neural Networks; Industrial Automation

  • Abstract

    This study proposes an AI-driven adaptive control strategy to enhance the learning, adaptability, and autonomous performance of robotic ‎manipulators in dynamic and unstructured industrial environments. Moving beyond the limitations of conventional model-based controllers, ‎the research introduces a self-learning framework that integrates real-time sensor data from LiDAR and stereo vision cameras. This data ‎continuously informs and optimizes the robot’s motion trajectories in both simulated and real-world tasks. The system’s core innovation lies ‎in combining Reinforcement Learning (RL) with Deep Neural Networks (DNNs) for adaptive trajectory planning and error compensation. ‎Specifically, the Proximal Policy Optimization (PPO) algorithm is employed to fine-tune control strategies based on real-time sensory ‎feedback, allowing the robotic system to autonomously adapt to variations in object positions and unexpected disturbances. An Edge-AI ‎module is embedded into the architecture to enhance decision-making speed and reduce latency during task execution. Experimental ‎validation, including scenarios like arc welding and sealant dispensing, shows the proposed system outperforms traditional PID-based ‎adaptive controllers. The AI-driven solution demonstrated improved precision, faster convergence, and superior adaptability under complex ‎and fluctuating manufacturing conditions. The study also opens pathways for future integration of hybrid AI techniques—such as fuzzy ‎logic and genetic algorithms—for even more intelligent and responsive robotic systems‎.

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  • How to Cite

    Kunal, K. ., Kathiravan, M., Madeshwaren, V., Chandrakala, . T. ., Ganesan, V. ., & Gupta, S. . (2025). AI-Driven Intelligent Control Strategies for Industrial ‎Robotics: A Reinforcement Learning Approach. International Journal of Basic and Applied Sciences, 14(2), 429-440. https://doi.org/10.14419/1dy21h49