This paper presents an approach to use the adaptive controller for a class of uncertain systems in the presence of unknown Preisach-type hysteresis in input, unknown time-varying parameters, and unknown time-varying disturbances. The hysteresis operator can be transformed into an equivalent linear time-varying (LTV) system with uncertainties, which means that the effect of the hysteresis can be considered as general uncertainties to the system. Without constructing the inverse hysteresis function, the adaptive control is used to handle the uncertainties introduced by the hysteresis, as well as system dynamics. The adaptive controller presented in this paper ensures uniformly bounded transient and tracking performance for uncertain hysteretic systems. The performance bounds can be systematically improved by increasing the adaptation rate. Simulation results with Preisach-type hysteresis are provided to verify the theoretical findings.
Skip Nav Destination
Article navigation
January 2014
Research-Article
Adaptive Control for Uncertain Hysteretic Systems
Xiaotian Zou,
Xiaotian Zou
Department of Biomedical Engineering,
e-mail: Xiaotian_Zou@student.uml.edu
University of Massachusetts
,Lowell, MA 01854
e-mail: Xiaotian_Zou@student.uml.edu
Search for other works by this author on:
Chengyu Cao
Chengyu Cao
1
Assistant Professor
Mem. ASME
e-mail: ccao@engr.uconn.edu
Department of Mechanical Engineering,
Mem. ASME
e-mail: ccao@engr.uconn.edu
Department of Mechanical Engineering,
University of Connecticut
,Storrs, CT 06269
1Corresponding author.
Search for other works by this author on:
Xiaotian Zou
Department of Biomedical Engineering,
e-mail: Xiaotian_Zou@student.uml.edu
University of Massachusetts
,Lowell, MA 01854
e-mail: Xiaotian_Zou@student.uml.edu
Jie Luo
e-mail: jie.luo@engr.uconn.edu
Chengyu Cao
Assistant Professor
Mem. ASME
e-mail: ccao@engr.uconn.edu
Department of Mechanical Engineering,
Mem. ASME
e-mail: ccao@engr.uconn.edu
Department of Mechanical Engineering,
University of Connecticut
,Storrs, CT 06269
1Corresponding author.
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received November 30, 2012; final manuscript received August 1, 2013; published online October 7, 2013. Assoc. Editor: Qingze Zou.
J. Dyn. Sys., Meas., Control. Jan 2014, 136(1): 011011 (7 pages)
Published Online: October 7, 2013
Article history
Received:
November 30, 2012
Revision Received:
August 1, 2013
Citation
Zou, X., Luo, J., and Cao, C. (October 7, 2013). "Adaptive Control for Uncertain Hysteretic Systems." ASME. J. Dyn. Sys., Meas., Control. January 2014; 136(1): 011011. https://doi.org/10.1115/1.4025241
Download citation file:
Get Email Alerts
Cited By
On Using a Brushless Motor as a Passive Torque-Controllable Brake
J. Dyn. Sys., Meas., Control (September 2022)
Optimal Design and Command Filtered Backstepping Control of Exoskeleton With Series Elastic Actuator
J. Dyn. Sys., Meas., Control (September 2022)
A Note on the Robustness of the Disturbance Observer-Based Robust Control Systems
J. Dyn. Sys., Meas., Control (September 2022)
A Robust Time-Varying Riccati-Based Control for Uncertain Nonlinear Dynamical Systems
J. Dyn. Sys., Meas., Control
Related Articles
Multivariable Adaptive Control Method for Turbofan Engine With Dynamic and Input Uncertainties
J. Eng. Gas Turbines Power (July,2021)
Desired Compensation Adaptive Robust Control
J. Dyn. Sys., Meas., Control (November,2009)
Adaptive Control of Teleoperation Systems With Linearly and Nonlinearly Parameterized Dynamic Uncertainties
J. Dyn. Sys., Meas., Control (March,2012)
Modeling and Control of Combustion Phasing in Dual-Fuel Compression Ignition Engines
J. Eng. Gas Turbines Power (May,2019)
Related Chapters
Regression Target – Objective Function
Nonlinear Regression Modeling for Engineering Applications: Modeling, Model Validation, and Enabling Design of Experiments
Design of Experiments for Model Development and Validation
Nonlinear Regression Modeling for Engineering Applications: Modeling, Model Validation, and Enabling Design of Experiments
Applications for Operation
Pipeline System Automation and Control