Adaptive Sliding-Mode Position Control for Piezo-Actuated Stage

Chen, Xinkai; Hisayama, Takeshi

doi:10.1109/tie.2008.926768

Cited by 157 publications

(59 citation statements)

References 26 publications

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“…Various modifications of the basic sliding mode control technique have also been reported for controlling PEAs for improved performance. For instance, adaptive methods are combined with sliding mode control to remove the requirement of model parameter estimation or lead to better compensation for the nonlinearities as in [115][116][117]. And in [118] the nonlinearities of the positioning mechanism driven by a PEA in additional to those of the PEA itself were considered in sliding mode controller design.…”

Section: Feedbackmentioning

confidence: 99%

A Survey of Modeling and Control of Piezoelectric Actuators

Peng¹,

Xiongbiao²

2013

MME

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Piezoelectric actuators (PEAs) have been widely used in micro-and nanopositioning applications due to their fine resolution, fast responses, and large actuating forces. However, the existence of nonlinearities such as hysteresis makes modeling and control of PEAs challenging. This paper reviews the recent achievements in modeling and control of piezoelectric actuators. Specifically, various methods for modeling linear and nonlinear behaviors of PEAs, including vibration dynamics, hysteresis, and creep, are examined; and the issues involved are identified. In the control of PEAs as applied to positioning, a review of various control schemes of both model-based and non-model-based is presented along with their limitations. The challenges associated with the control problem are also discussed. This paper is concluded with the emerging issues identified in modeling and control of PEAs for future research.

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Section: Feedbackmentioning

confidence: 99%

A Survey of Modeling and Control of Piezoelectric Actuators

Peng¹,

Xiongbiao²

2013

MME

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“…This, together with (13), recovers the state error defined in (4). In order to assure that the actual system (13) follows the reference model (2), H d (s) should be chosen close to zero.…”

Section: E(t)−(a M + K )E(t) = a M X(t)+ B M C(t)− K E(t)− A(t)x(t)−mentioning

confidence: 81%

“…For example, H ∞ control [1], genetic algorithmbased control [2], predictive control [3], adaptive control [4], sliding-mode cascaded control [5] and multirate control [6] approaches have been proposed and widely used. Quantitative feedback control [7] is another method to design robust controllers that are able to deal with parametric uncertainty in a non-conservative way while minimizing the cost of feedback.…”

Section: Introductionmentioning

confidence: 99%

Design of UDE‐based controllers from their two‐degree‐of‐freedom nature

Zhong

Kuperman

Stobart

2010

Intl J Robust & Nonlinear

178

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SUMMARYThe control algorithm based on the uncertainty and disturbance estimator (UDE) is a robust control strategy and has received wide attention in recent years. In this paper, the two-degree-of-freedom nature of UDE-based controllers is revealed. The set-point tracking response is determined by the reference model, whereas the disturbance response and robustness are determined by the error feedback gain and the filter introduced to estimate the uncertainty and disturbances. It is also revealed that the error dynamics of the system is determined by two filters, of which one is determined by the error feedback gain and the other is determined by the filter introduced to estimate the uncertainty and disturbances. The design of these two filters are decoupled in the frequency domain. Moreover, after introducing the UDE-based control, the Laplace transform can be applied to some time-varying systems for analysis and design because all the time-varying parts are lumped into a signal. It has been shown that, in addition to the known advantages over the time-delay control, the UDE-based control also brings better performance than the time-delay control under the same conditions. Design examples and simulation results are given to demonstrate the findings.

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“…However, in some applications like micro-robotics, feedback control is not always applicable because of the lack of convenient sensors to perform measurements. Many techniques have been developed to feedback control PEA based on cantilever structures, for instance: simple tuning based techniques [37][38][39], H ∞ based robust techniques [8,11,29,[40][41][42], interval based robust techniques [43][44][45], adaptive, sliding and iterative techniques [26,[46][47][48][49][50][51].…”

Section: Pea Controlmentioning

confidence: 99%

Getting Started with PEAs-Based Flapping-Wing Mechanisms for Micro Aerial Systems

et al. 2016

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This paper introduces recent advances on flapping-wing Micro and Nano Aerial Vehicles (MAVs and NAVs) based on Piezoelectric Actuators (PEA). Therefore, this work provides essential information to address the development of such bio-inspired aerial robots. PEA are commonly used in micro-robotics and precise positioning applications (e.g., micro-positioning and micro-manipulation), whereas within the Unmanned Aerial Vehicles (UAVs) domain, motors are the classical actuators used for rotary or fixed-wing configurations. Therefore, we consider it pertinent to provide essential information regarding the modeling and control of piezoelectric cantilever actuators to accelerate early design and development stages of aerial microrobots based on flapping-wing systems. In addition, the equations describing the aerodynamic behavior of a flapping-wing configuration are presented.

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Adaptive Sliding-Mode Position Control for Piezo-Actuated Stage

Cited by 157 publications

References 26 publications

A Survey of Modeling and Control of Piezoelectric Actuators

A Survey of Modeling and Control of Piezoelectric Actuators

Design of UDE‐based controllers from their two‐degree‐of‐freedom nature

Getting Started with PEAs-Based Flapping-Wing Mechanisms for Micro Aerial Systems

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