Design of Energy-Efficient Actuation Systems for Industrial Robots

  • Authors

    • Amanda Davis Business Development Manager, Nexus Solutions, USA Author
    • Kevin Taylor Senior Project Manager, Prime Innovations, USA Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2019PII7T2W

    Published 08-05-2019

  • Industrial Robots, Energy-Efficient Actuators, Servo Motors, Intelligent Motion Control, Regenerative Braking, Robotic Energy Optimization, Harmonic Drives, Industrial Automation, Mechatronics, Sustainable Robotics

    Issue

    Vol. 2 No. 2 (2019)

    Section

    Articles

  • Abstract

    Industrial robotics plays a vital role in modern manufacturing by improving productivity, precision, repeatability, and safety. However, the growing use of robots in industries such as automotive, aerospace, electronics, pharmaceuticals, logistics, and heavy engineering has increased industrial energy consumption. Actuation systems are the major energy-consuming components in robots because they generate motion. Conventional actuators often experience inefficiencies such as friction losses, thermal dissipation, inefficient motors, and poor control strategies, resulting in excessive power usage. This paper reviews energy-efficient actuation systems for industrial robots before 2019, focusing on actuator designs, architectures, optimization techniques, and technological advancements. It examines electric, hydraulic, pneumatic, servo, harmonic drive, and hybrid actuators, highlighting the importance of energy-efficient motors, lightweight materials, regenerative braking, intelligent motion control, and adaptive drive systems in reducing energy consumption while maintaining performance. The study also discusses actuator efficiency optimization, trajectory planning, friction compensation, regenerative energy recovery, and AI-based predictive control methods. A systematic framework for designing energy-efficient actuators is presented, including modular architectures, optimized transmissions, intelligent servo control, and real-time energy monitoring. Mathematical models for actuator dynamics, torque, power consumption, and efficiency evaluation are also included. The review concludes that optimized actuation systems can significantly reduce energy usage while improving accuracy, reliability, thermal stability, and lifespan, making them essential for sustainable industrial automation and future smart robotic systems.

  • References

    [1] B. Siciliano and O. Khatib, Springer Handbook of Robotics, 2nd ed. Berlin, Germany: Springer, 2016.

    [2] M. W. Spong, S. Hutchinson, and M. Vidyasagar, Robot Modeling and Control. New York, NY, USA: Wiley, 2006.

    [3] J. J. Craig, Introduction to Robotics: Mechanics and Control, 3rd ed. Upper Saddle River, NJ, USA: Pearson, 2005.

    [4] A. De Luca and W. Book, “Robots with flexible elements,” in Springer Handbook of Robotics, Berlin, Germany: Springer, 2016, pp. 243–282.

    [5] H. Asada and J.-J. E. Slotine, Robot Analysis and Control. New York, NY, USA: Wiley, 1986.

    [6] Y. Sakagami, R. Watanabe, C. Aoyama, S. Matsunaga, N. Higaki, and K. Fujimura, “The intelligent ASIMO: System overview and integration,” in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2002, pp. 2478–2483.

    [7] S. Haddadin, A. Albu-Schäffer, and G. Hirzinger, “Requirements for safe robots: Measurements, analysis and new insights,” Int. Journal of Robotics Research, vol. 28, no. 11–12, pp. 1507–1527, 2009.

    [8] M. Paryanto, M. Brossog, J. Bornschlegl, and J. Franke, “Reducing the energy consumption of industrial robots in manufacturing systems,” International Journal of Advanced Manufacturing Technology, vol. 78, no. 5–8, pp. 1315–1328, 2015.

    [9] P. Ferreira, A. Crisostomo, and A. Coimbra, “Energy-efficient trajectory planning for industrial robot manipulators,” Industrial Robot: An International Journal, vol. 41, no. 5, pp. 425–433, 2014.

    [10] M. Pellicciari, G. Berselli, F. Leali, and A. Vergnano, “A method for reducing the energy consumption of industrial robots in manufacturing systems,” Procedia CIRP, vol. 7, pp. 418–423, 2013.

    [11] J. Lee, B. Bagheri, and H.-A. Kao, “A cyber-physical systems architecture for Industry 4.0-based manufacturing systems,” Manufacturing Letters, vol. 3, pp. 18–23, 2015.

    [12] K. Erkorkmaz and Y. Altintas, “High speed CNC system design. Part I: Jerk limited trajectory generation and quintic spline interpolation,” International Journal of Machine Tools and Manufacture, vol. 41, no. 9, pp. 1323–1345, 2001.

    [13] S. Chiaverini, B. Siciliano, and O. Egeland, “Review of the damped least-squares inverse kinematics with experiments on an industrial robot manipulator,” IEEE Transactions on Control Systems Technology, vol. 2, no. 2, pp. 123–134, 1994.

    [14] F. Piltan, N. Sulaiman, M. H. Marhaban, and R. Ramli, “Design artificial robust control of robot manipulator based on fuzzy logic inference,” International Journal of Robotics and Automation, vol. 2, no. 3, pp. 96–107, 2011.

    [15] E. F. Camacho and C. Bordons, Model Predictive Control, 2nd ed. London, U.K.: Springer, 2007.

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