Adaptive novel MSGA-RBF neurocontrol for piezo-ceramic actuator suffering rate-dependent hysteresis

Liu, Dongbo; Fang, Yu; Wang, Haibin; Dong, Xiucheng

doi:10.1016/j.sna.2019.111553

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…The deformation displacement formula of each actuated mode can be presented in Table 1 in terms of the applied voltage U. PAs are widely applied in precision engineering due to the advantage of simplicity in structure, high resolution, fast response, and large drive force. However, the intrinsic hysteretic property of the PAs severely influences the positioning performance, which is sensitive to temperature and frequency (Al Janaideh et al, 2020;Liu et al, 2019;Manikantan et al, 2022). Due to such an external environment as in outer space, self-heating, heating of the nearby instrument, and other natural sources, the PA's temperature variation is inevitable, which affects the hysteretic effect (Ko¨hler and Rinderknecht, 2013;Liu et al, 2020;Sofla et al, 2012).…”

Section: Piezo-actuated Mechanismmentioning

confidence: 99%

A survey of Bouc-Wen hysteretic models applied to piezo-actuated mechanical systems: Modeling, identification, and control

Cai

Dong

Nagamune

2023

Journal of Intelligent Material Systems and Structures

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Hysteretic nonlinearity behavior ubiquitously occurs in mechanical systems, particularly in high-precision instruments, which severely degrades system output performance. Consequently, it is unavoidable to establish an accurate hysteretic model to describe the hysteretic characteristic of various mechanical systems and to compensate for the system error caused by hysteresis. The piezo-actuated mechanism is one of the most frequent mechanical systems that occurs hysteretic phenomenon, explained in detail in this survey. Bouc-Wen (BW) model has been popularly applied in modeling hysteretic attributes due to the outstanding advantage of simple structure and identification process. This paper presents the latest BW model applications and investigates different BW models, identification methods, and control strategies in light of various application demands based on the BW model systematically. In addition, this survey is meaningful in choosing an appropriate modeling approach and control means for system design according to the distinct requirements, and in providing future research direction.

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Section: Piezo-actuated Mechanismmentioning

confidence: 99%

A survey of Bouc-Wen hysteretic models applied to piezo-actuated mechanical systems: Modeling, identification, and control

Cai

Dong

Nagamune

2023

Journal of Intelligent Material Systems and Structures

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“…Therefore, there has been no widely accepted general model. More and more researchers have introduced other new piezoelectric ceramic feedforward control methods, such as using the Radial basis function(RBF) network [ 14 ] and neural network self-turning control [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Feedforward Control of Piezoelectric Ceramic Actuators Based on PEA-RNN

Xiong

Jia

Zhang

et al. 2022

Sensors

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Multilayer perceptron (MLP) has been demonstrated to implement feedforward control of the piezoelectric actuator (PEA). To further improve the control accuracy of the neural network, reduce the training time, and explore the possibility of online model updating, a novel recurrent neural network named PEA-RNN is established in this paper. PEA-RNN is a three-input, one-output neural network, including one gated recurrent unit (GRU) layer, seven linear layers, and one residual connection in the linear layers. The experimental results show that the displacement linearity error of piezoelectric ceramics reaches 8.96 μm in the open-loop condition. After using PEA-RNN compensation, the maximum displacement error of piezoelectric ceramics is reduced to 0.465 μm at the operating frequency of 10 Hz, which proves that PEA-RNN can accurately compensate piezoelectric ceramics’ dynamic hysteresis nonlinearity. At the same time, the training epochs of PEA-RNN are only 5% of the MLP, and fewer training epochs provide the possibility to realize online updates of the model in the future.

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“…Based on backstepping technique and neural networks, Chen developed an event-triggered adaptive control scheme with prescribed performance [11]. Liu combined a membrane structure genetic algorithm (MSGA) method with adaptive inverse neuro-control to identify the parameters of micropositioning system [12]. Nevertheless, the performance and robustness of such model-based control strategies are still limited by the precision of the dynamics model.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sliding Mode Disturbance Observer and Deep Reinforcement Learning based Robust Motion Control for Micropositioners

Liang¹,

Xi²,

Xiao³

et al. 2022

Preprint

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The robust control of high precision electromechanical systems, such as micropositioners, is challenging in terms of the inherent high nonlinearity, the sensitivity to external interference, and the complexity of accurate identification of the model parameters. To cope with these problems, this work investigates a disturbance observer-based deep reinforcement learning control strategy to realize high robustness and precise tracking performance. Reinforcement learning has shown great potential as optimal control scheme, however, its application in micropositioning systems is still rare. Therefore, embedded with the integral differential compensator (ID), deep deterministic policy gradient (DDPG) is utilized in this work with the ability to not only decrease the state error but also improves the transient response speed. In addition, an adaptive sliding mode disturbance observer (ASMDO) is proposed to further eliminate the collective effect caused by the lumped disturbances. The sterling performance is revealed with intensive tracking simulation experiments and demonstrates the improvement in the accuracy and response time of the controller.

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Adaptive novel MSGA-RBF neurocontrol for piezo-ceramic actuator suffering rate-dependent hysteresis

Cited by 8 publications

References 16 publications

A survey of Bouc-Wen hysteretic models applied to piezo-actuated mechanical systems: Modeling, identification, and control

A survey of Bouc-Wen hysteretic models applied to piezo-actuated mechanical systems: Modeling, identification, and control

Feedforward Control of Piezoelectric Ceramic Actuators Based on PEA-RNN

Adaptive Sliding Mode Disturbance Observer and Deep Reinforcement Learning based Robust Motion Control for Micropositioners

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