Smart Actuator Systems for Precision Robotic Applications
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DOI:
https://doi.org/10.67228/30715725/IJIARE-2019PII4P6CPublished 11-05-2019
Smart Actuators, Precision Robotics, Piezoelectric Actuators, Shape Memory Alloys, Electroactive Polymers, Adaptive Control, MEMS, Robotics Automation Issue
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ArticlesHow to Cite
[1]M. Krishnan, “Smart Actuator Systems for Precision Robotic Applications”, IJIARE, vol. 2, no. 2, pp. 01–12, Nov. 2019, doi: 10.67228/30715725/IJIARE-2019PII4P6C.Abstract
Smart actuator systems are vital to precision robotics, enabling enhanced control, adaptability, and performance in complex environments. Prior to 2018, major advancements in actuator technologies—such as piezoelectric, shape memory alloy (SMA), electroactive polymers (EAP), and magnetostrictive actuators—allowed integration of sensing, actuation, and control within a single system for real-time, precise motion. Driven by applications in aerospace, medical robotics, manufacturing, and MEMS, research has focused on actuator principles, material properties, control strategies, and system integration. Key challenges identified include nonlinearity, hysteresis, thermal effects, and energy efficiency. Control methods like PID, adaptive, and model-based approaches have been explored to improve performance. This work outlines a systematic design methodology involving material selection, modeling, sensor integration, and feedback control. Performance is evaluated based on accuracy, response time, force, and efficiency, with comparative analysis highlighting strengths and limitations of different actuator types. Results indicate that smart actuators significantly enhance robotic precision and stability when combined with advanced control systems. Future research directions include hybrid actuators, improved materials, and advanced control algorithms.
References
K. Uchino, Piezoelectric Actuators and Ultrasonic Motors, Boston, MA, USA: Springer, 2010.
[2] S. O. R. Moheimani and A. J. Fleming, Piezoelectric Transducers for Vibration Control and Damping, London, U.K.: Springer, 2006.
[3] D. J. Leo, Engineering Analysis of Smart Material Systems, Hoboken, NJ, USA: Wiley, 2007.
[4] Y. Bar-Cohen, “Electroactive polymers as artificial muscles—Reality and challenges,” Proc. SPIE, vol. 4695, pp. 1–7, 2002.
[5] M. Kohl, Shape Memory Microactuators, Berlin, Germany: Springer, 2004.
[6] T. Duerig, K. Melton, D. Stöckel, and C. Wayman, Engineering Aspects of Shape Memory Alloys, London, U.K.: Butterworth-Heinemann, 1990.
[7] G. Song, N. Ma, and H.-N. Li, “Applications of shape memory alloys in civil structures,” Eng. Struct., vol. 28, no. 9, pp. 1266–1274, 2006.
[8] R. C. Smith, Smart Material Systems: Model Development, Philadelphia, PA, USA: SIAM, 2005.
[9] S. Priya and S. Inman, Energy Harvesting Technologies, New York, NY, USA: Springer, 2009.
[10] J. J. Dosch, D. J. Inman, and E. Garcia, “A self-sensing piezoelectric actuator for collocated control,” J. Intell. Mater. Syst. Struct., vol. 3, no. 1, pp. 166–185, 1992.
[11] K. K. Ahn and D. Q. Truong, “Online tuning fuzzy PID controller using genetic algorithm for a robot manipulator,” Int. J. Control Autom. Syst., vol. 7, no. 1, pp. 1–9, 2009.
[12] E. F. Camacho and C. Bordons, Model Predictive Control, London, U.K.: Springer, 2007.
[13] I. Mayergoyz, Mathematical Models of Hysteresis, New York, NY, USA: Springer, 2003.
[14] G. Gu, L. Zhu, C. Su, H. Ding, and S. Fatikow, “Modeling and control of piezo-actuated nanopositioning stages: A survey,” IEEE Trans. Autom. Sci. Eng., vol. 13, no. 1, pp. 313–332, 2016.
[15] X. Tan and J. S. Baras, “Modeling and control of hysteresis in magnetostrictive actuators,” Automatica, vol. 40, no. 9, pp. 1469–1480, 2004.
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How to Cite
[1]M. Krishnan, “Smart Actuator Systems for Precision Robotic Applications”, IJIARE, vol. 2, no. 2, pp. 01–12, Nov. 2019, doi: 10.67228/30715725/IJIARE-2019PII4P6C.
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