This paper investigates a novel nonlinear positioning control methodology for piezoelectric stack actuators. Piezoelectric devices become very common recently for precise positioning, primarily due to the fact that they are solid state and can be accurately controlled by a voltage or current input. However, hysteresis decreases positioning accuracy and could lead to instability. The ultimate goal is to reduce it so that the piezoelectric device has a nearly linear relationship between the input field and output strain. The main purpose of this research is the reduction of the hysteresis utilizing a hysteresis model and a nonlinear model-based controller. A novel control method called model predictive sliding mode control (MPSMC) will be utilized on an actuator using a nonlinear energy-based hysteresis model. The idea of MPSMC is to implement model predictive control techniques to improve sliding mode control by forcing the system to reach the sliding surface in an optimal manner. Simulations and experiments were conducted to verify the technique.
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ASME 2008 Conference on Smart Materials, Adaptive Structures and Intelligent Systems
October 28–30, 2008
Ellicott City, Maryland, USA
Conference Sponsors:
- Aerospace Division
ISBN:
978-0-7918-4332-1
PROCEEDINGS PAPER
Nonlinear Position Control of Smart Actuators Using Model Predictive Sliding Mode Control Available to Purchase
Byeongil Kim,
Byeongil Kim
The Ohio State University, Columbus, OH
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Gregory N. Washington
Gregory N. Washington
The Ohio State University, Columbus, OH
Search for other works by this author on:
Byeongil Kim
The Ohio State University, Columbus, OH
Gregory N. Washington
The Ohio State University, Columbus, OH
Paper No:
SMASIS2008-602, pp. 511-522; 12 pages
Published Online:
July 13, 2009
Citation
Kim, B, & Washington, GN. "Nonlinear Position Control of Smart Actuators Using Model Predictive Sliding Mode Control." Proceedings of the ASME 2008 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. Smart Materials, Adaptive Structures and Intelligent Systems, Volume 2. Ellicott City, Maryland, USA. October 28–30, 2008. pp. 511-522. ASME. https://doi.org/10.1115/SMASIS2008-602
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