An active disturbance rejection controller with hysteresis compensation for piezoelectric actuators

Piezoelectric Actuators (PEAs) are the key components in nano-positioning. However, the inherent hysteresis nonlinearity of PEAs is seriously affected the control precision. In this paper, an active disturbance rejection controller is proposed to deal with the tracking control of PEAs. First, the hysteresis nonlinearity is reformulated as a disturbance of the closed-loop system. With this idea, a disturbance-based model is derived from the comprehensive model of PEAs. Then the so-called extended state observer is introduced to real-time estimate the hysteresis nonlinearity. With the extended state observer, the model of hysteresis or its inversion are all no longer needed. To verify the performance of the proposed method, some experiments are conducted on a commercial PEA. And the experiment results show that the proposed controller is an effective way to deal with the tracking control of PEAs.

[1]  Shaocheng Tong,et al.  Adaptive Fuzzy Control for a Class of Nonlinear Discrete-Time Systems With Backlash , 2014, IEEE Transactions on Fuzzy Systems.

[2]  Zhiqiang Gao,et al.  An Active Disturbance Rejection Control solution for hysteresis compensation , 2008, 2008 American Control Conference.

[3]  Yanling Tian,et al.  A Novel Direct Inverse Modeling Approach for Hysteresis Compensation of Piezoelectric Actuator in Feedforward Applications , 2013, IEEE/ASME Transactions on Mechatronics.

[4]  Y. Cao,et al.  An Inversion-Based Model Predictive Control With an Integral-of-Error State Variable for Piezoelectric Actuators , 2013, IEEE/ASME Transactions on Mechatronics.

[5]  Junzhi Yu,et al.  An Inversion-Free Predictive Controller for Piezoelectric Actuators Based on a Dynamic Linearized Neural Network Model , 2016, IEEE/ASME Transactions on Mechatronics.

[6]  R. Ben Mrad,et al.  On the classical Preisach model for hysteresis in piezoceramic actuators , 2003 .

[7]  Li-Min Zhu,et al.  Modeling and Compensation of Asymmetric Hysteresis Nonlinearity for Piezoceramic Actuators With a Modified Prandtl–Ishlinskii Model , 2014, IEEE Transactions on Industrial Electronics.

[8]  Tingwen Huang,et al.  An Adaptive Takagi–Sugeno Fuzzy Model-Based Predictive Controller for Piezoelectric Actuators , 2017, IEEE Transactions on Industrial Electronics.

[9]  Leonardo Riccardi,et al.  Design of Linear Feedback Controllers for Dynamic Systems With Hysteresis , 2014, IEEE Transactions on Control Systems Technology.

[10]  Peng Yan,et al.  Flexure-hinges guided nano-stage for precision manipulations: Design, modeling and control , 2015 .

[11]  Ming-Yang Cheng,et al.  Development of a Recurrent Fuzzy CMAC With Adjustable Input Space Quantization and Self-Tuning Learning Rate for Control of a Dual-Axis Piezoelectric Actuated Micromotion Stage , 2013, IEEE Transactions on Industrial Electronics.

[12]  Peiyue Li,et al.  A simple fuzzy system for modelling of both rate-independent and rate-dependent hysteresis in piezoelectric actuators , 2013 .

[13]  Bum-Jae You,et al.  A piezoelectric actuator with a motion-decoupling amplifier for optical disk drives , 2010 .

[14]  Sergej Fatikow,et al.  Modeling and Control of Piezo-Actuated Nanopositioning Stages: A Survey , 2016, IEEE Transactions on Automation Science and Engineering.

[15]  Xinkai Chen,et al.  Adaptive Control for Uncertain Continuous-Time Systems Using Implicit Inversion of Prandtl-Ishlinskii Hysteresis Representation , 2010, IEEE Transactions on Automatic Control.

[16]  Junzhi Yu,et al.  Neural-Network-Based Nonlinear Model Predictive Control for Piezoelectric Actuators , 2015, IEEE Transactions on Industrial Electronics.

[17]  Qingze Zou,et al.  Iterative Control Approach to Compensate for Both the Hysteresis and the Dynamics Effects of Piezo Actuators , 2007, IEEE Transactions on Control Systems Technology.

[18]  John S. Baras,et al.  Adaptive identification and control of hysteresis in smart materials , 2005, IEEE Transactions on Automatic Control.

[19]  Hongming Wang,et al.  An inversion-free fuzzy predictive control for piezoelectric actuators , 2015, The 27th Chinese Control and Decision Conference (2015 CCDC).

[20]  Yangmin Li,et al.  Development and Active Disturbance Rejection Control of a Compliant Micro-/Nanopositioning Piezostage With Dual Mode , 2014, IEEE Transactions on Industrial Electronics.

[21]  Long Cheng,et al.  Neural-network based model predictive control for piezoelectric-actuated stick-slip micro-positioning devices , 2016, 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM).

[22]  Jingqing Han,et al.  From PID to Active Disturbance Rejection Control , 2009, IEEE Trans. Ind. Electron..

[23]  Pengbo Liu,et al.  Modeling and active disturbance rejection control for a piezoelectric-actuator driven nanopositioner , 2014, Proceedings of the 33rd Chinese Control Conference.

[24]  M. S. Rana,et al.  Nonlinearity Effects Reduction of an AFM Piezoelectric Tube Scanner Using MIMO MPC , 2015, IEEE/ASME Transactions on Mechatronics.

[25]  Yonghong Tan,et al.  Modeling hysteresis in piezoelectric actuators using NARMAX models , 2009 .

[26]  Long Cheng,et al.  An inversion-free model predictive control with error compensation for piezoelectric actuators , 2015, 2015 American Control Conference (ACC).

[27]  Qing Zheng,et al.  A disturbance rejection based control approach for hysteretic systems , 2010, 49th IEEE Conference on Decision and Control (CDC).

[28]  Thanh Nho Do,et al.  A survey on hysteresis modeling, identification and control , 2014 .