An experimental comparison of proportional-integral, sliding mode, and robust adaptive control for piezo-actuated nanopositioning stages

Gu, Guoying; Zhu, Limin

doi:10.1063/1.4876596

Cited by 16 publications

(9 citation statements)

References 25 publications

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“…However, the knowledge of parameter variation range of the system model is usually needed to ensure stability and satisfy reaching conditions, which may involve infinite-gain feedback. To overcome the drawback of the sliding mode control, adaptive control [133] is generally introduced to estimate the system parameters. In this sense, robust adaptive controllers integrating both the sliding mode control and adaptive control approaches guarantee a superior performance in terms of both the transient error and the final tracking accuracy in the presence of both parametric uncertainties and uncertain nonlinearities.…”

Section: B Feedback Controlmentioning

confidence: 99%

“…The first approach is to treat the term as a new hysteresis nonlinearity, and a cascaded structure with a linear dynamic model preceded by a hysteresis model [as depicted in Fig. 6(b)] is obtained [20], [49], [72], [105], [133]- [136]. The second one is by assuming ; the complete model is described by with a nonlinear hysteresis term [81], [137]- [140].…”

Section: Comprehensive Dynamic Modelingmentioning

confidence: 99%

“…In fact, development of feedback controllers for dynamics systems with hysteresis nonlinearity is challenging [47], [69], [77], [108], [133], [154]- [160], especially when the hysteresis is unknown, which is a typical case in practical applications. Through the literature review, there are two methods to design the feedback controllers for piezo-actuated stages, which can be distinguished by whether the hysteresis models are utilized in the control design.…”

Section: B Feedback Controlmentioning

confidence: 99%

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Modeling and Control of Piezo-Actuated Nanopositioning Stages: A Survey

Zhu

et al. 2016

IEEE Trans. Automat. Sci. Eng.

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502

216

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Piezo-actuated stages have become more and more promising in nanopositioning applications due to the excellent advantages of the fast response time, large mechanical force, and extremely fine resolution. Modeling and control are critical to achieve objectives for high-precision motion. However, piezo-actuated stages themselves suffer from the inherent drawbacks produced by the inherent creep and hysteresis nonlinearities and vibration caused by the lightly damped resonant dynamics, which make modeling and control of such systems challenging. To address these challenges, various techniques have been reported in the literature. This paper surveys and discusses the progresses of different modeling and control approaches for piezo-actuated nanopositioning stages and highlights new opportunities for the extended studies.Note to Practitioners-Piezo-actuated nanopositioning stages featuring fast response, large force, and fine resolution appear poised to play an increasingly important role in fields requiring micro/nano positioning. Such stages, however, exhibit complex piezoelectric behavior, which is often neglected, including: frequency response, nonlinear electric field dependence, creep, aging, and thermal behavior. The presence of such a behavior observed in the stages poses challenges in realizing precise micro/nanopositioning performance. This paper presents an overview of the piezo-actuated nanopositioning stages emphasizing the key role of modeling and control techniques, where high accuracy is important.

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Section: B Feedback Controlmentioning

confidence: 99%

Section: Comprehensive Dynamic Modelingmentioning

confidence: 99%

Section: B Feedback Controlmentioning

confidence: 99%

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Modeling and Control of Piezo-Actuated Nanopositioning Stages: A Survey

Zhu

et al. 2016

IEEE Trans. Automat. Sci. Eng.

Self Cite

502

216

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“…Based on the conducted research works, nonlinearities play an important role in the failure of dynamic systems (Alsaleem and Younis, 2010). Hence, studying the control behaviors and enhancing the efficiency, accuracy, and stability of electrostatic MEMS have been a matter of interest of many researchers (Gu and Zhu, 2014; Radgolchin and Moeenfard, 2018). Several control methods such as sliding mode control (SMC) (Dastaviz and Binazadeh, 2019; Vahidi-Moghaddam et al, 2018), fuzzy control (Tooranjipour et al, 2019; Zou et al, 2019), and backstepping control (Meng et al, 2019) have been vastly used to control the nonlinear mechanical systems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fractional-order backstepping sliding mode controller design for an electrostatically actuated size-dependent microplate

Karami

Kazemi

Vatankhah

et al. 2020

Journal of Vibration and Control

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In this article, adaptive backstepping sliding mode controller and adaptive fractional backstepping sliding mode controller methods are proposed to control an electrostatic microplate with a piezoelectric layer. Based on the modified couple stress theory, a size-dependent mathematical model is proposed, in which the microplate is modeled using the Kirchhoff plate theory. To take into account the geometric nonlinearities, the von Kármán nonlinear strains are considered in the mathematical model. The Hamilton’s principle is used to obtain the nonlinear equation of motion of the system, which is then converted into a nonlinear ordinary differential equation via the Galerkin technique. The validity of the results obtained from the proposed reduced-order model is checked through direct numerical simulation of the partial differential equation using the finite element method. Finally, two Lyapunov-based control approaches that are adaptive backstepping sliding mode and adaptive fractional backstepping sliding mode are applied to the system. In the adaptive fractional backstepping sliding mode controller method, the adaptive control law is used to evaluate the upper bound of uncertainties and random disturbances. The fractional-order form of sliding surface is used to reduce the amount of chattering and also to improve the tracking error. In the results section, first, numerical studies are conducted to study the effect of system parameters, such as material length scale parameter, aspect ratio, fractional order, and robustness of the controller, on the performance of the system. In addition, the performances of the two control methods are compared, and the merits and demerits of each method are discussed.

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“…In addition, feedforward combined with close-loop feedback control was adopted for a preferable precision motion tracking [27,28,29]. Considering the nonlinearity as a disturbance or an uncertainty, several attempts have also been employed to apply the feedback control without modeling the inverse hysteresis, such as robust control [30,31], sliding model control [32,33] and robust adaptive control [34].…”

Section: Introductionmentioning

confidence: 99%

Positioning Error Analysis and Control of a Piezo-Driven 6-DOF Micro-Positioner

Lin

Zheng

et al. 2019

Micromachines

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This paper presents a positioning error model and a control compensation scheme for a six-degree-of-freedom (6-DOF) micro-positioner based on a compliant mechanism and piezoelectric actuators (PZT). The positioning error model is established by means of the kinematic model of the compliant mechanism and complete differential coefficient theory, which includes the relationships between three typical errors (hysteresis, machining and measuring errors) and the total positioning error. The quantitative analysis of three errors is demonstrated through several experimental studies. Afterwards, an inverse Presiach model-based feedforward compensation of the hysteresis nonlinearity is employed by the control scheme, combined with a proportional-integral-derivative (PID) feedback controller for the compensation of machining and measuring errors. Moreover, a back propagation neural network PID (BP-PID) controller and a cerebellar model articulation controller neural network PID (CMAC-PID) controller are also adopted and compared to obtain optimal control. Taking the translational motion along the X axis as an example, the positioning errors are sharply reduced by the inverse hysteresis model with the maximum error of 12.76% and a root-mean-square error of 4.09%. In combination with the CMAC-PID controller, the errors are decreased to 0.63% and 0.23%, respectively. Hence, simulated and experimental results reveal that the proposed approach can improve the positioning accuracy of 6-DOF for the micro-positioner.

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An experimental comparison of proportional-integral, sliding mode, and robust adaptive control for piezo-actuated nanopositioning stages

Cited by 16 publications

References 25 publications

Modeling and Control of Piezo-Actuated Nanopositioning Stages: A Survey

Modeling and Control of Piezo-Actuated Nanopositioning Stages: A Survey

Adaptive fractional-order backstepping sliding mode controller design for an electrostatically actuated size-dependent microplate

Positioning Error Analysis and Control of a Piezo-Driven 6-DOF Micro-Positioner

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