Intelligent Motion Compensation Methods for Industrial Robotic Arms

  • Authors

    • Jean Bartik Computer Programmer, ENIAC Project, United States. Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2021PII2T6F

    Published 07-04-2021

  • Industrial Robotics, Motion Compensation, Intelligent Control, Robot Manipulators, Adaptive Control, Machine Learning, Reinforcement Learning, Sensor Fusion, Digital Twin, Industry 4.0, Predictive Control, Trajectory Optimization

    Issue

    Vol. 4 No. 2 (2021)

    Section

    Articles

    How to Cite

    [1]
    J. Bartik, “Intelligent Motion Compensation Methods for Industrial Robotic Arms”, IJIARE, vol. 4, no. 2, pp. 01–15, Jul. 2021, doi: 10.67228/30715725/IJIARE-2021PII2T6F.

  • Abstract

    Industrial robotic arms are essential to smart manufacturing, performing high-speed and high-precision tasks in industries such as automotive, aerospace, electronics, and pharmaceuticals. However, dynamic payloads, disturbances, mechanical wear, and environmental uncertainties reduce positioning accuracy and trajectory performance. Traditional control methods are often inadequate under changing conditions. This paper proposes an intelligent motion compensation framework that integrates multi-sensor data, digital twins, artificial intelligence, reinforcement learning, neural networks, and model predictive control to estimate and compensate for motion errors in real time. The framework enhances trajectory tracking, vibration suppression, energy efficiency, payload adaptability, predictive maintenance, and operational safety while supporting Industry 4.0 and autonomous manufacturing. Overall, it provides a scalable architecture for next-generation intelligent industrial robotic systems.

  • References

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