A review on control strategies for compensation of hysteresis and creep on piezoelectric actuators based micro systems

Sabarianand, D. V.; Karthikeyan, P.; Muthuramalingam, T.

doi:10.1016/j.ymssp.2020.106634

Cited by 174 publications

(69 citation statements)

References 89 publications

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“…Piezoelectric actuators (PEAs) have been widely used in precision positioning [ 1 ], energy harvesting [ 2 , 3 ] and tracking applications owing to their advantages of fast dynamic response, high electrical mechanical coupling efficiency and high positioning accuracy. The major drawback of PEA is internal hysteresis nonlinearity [ 4 ], which deteriorates their position/tracking performance. Several differential equation-based models, such as the Bouc–Wen model [ 5 ] and the Duhem model [ 6 ], as well as operator-based models, such as the Preisach model [ 7 ], the Prandtl–Ishlinskii (PI) model [ 8 ], the Krasnosel–skiiPokrovskii (KP) model [ 9 ] and the Jiles–Atherton model [ 10 ], have modeled and characterized the hysteresis behavior of PEAs.…”

Section: Introductionmentioning

confidence: 99%

Duhem Model-Based Hysteresis Identification in Piezo-Actuated Nano-Stage Using Modified Particle Swarm Optimization

Ahmed

Yan

Li³

2021

Micromachines

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This paper presents modeling and parameter identification of the Duhem model to describe the hysteresis in the Piezoelectric actuated nano-stage. First, the parameter identification problem of the Duhem model is modeled into an optimization problem. A modified particle swarm optimization (MPSO) technique, which escapes the problem of local optima in a traditional PSO algorithm, is proposed to identify the parameters of the Duhem model. In particular, a randomness operator is introduced in the optimization process which acts separately on each dimension of the search space, thus improving convergence and model identification properties of PSO. The effectiveness of the proposed MPSO method was demonstrated using different benchmark functions. The proposed MPSO-based identification scheme was used to identify the Duhem model parameters; then, the results were validated using experimental data. The results show that the proposed MPSO method is more effective in optimizing the complex benchmark functions as well as the real-world model identification problems compared to conventional PSO and genetic algorithm (GA).

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Section: Introductionmentioning

confidence: 99%

Duhem Model-Based Hysteresis Identification in Piezo-Actuated Nano-Stage Using Modified Particle Swarm Optimization

Ahmed

Yan

Li³

2021

Micromachines

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“…Piezoelectric (PZT) actuators have received a great deal of attention over the last few decades in nano-positioning applications due to their high speed, stiffness, and fast response [1]. PZT actuators are widely used in applications such as robotic systems [2] [3], positioning of microscope stage [4] etc.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Identification of Rate Dependent Hysteresis in Piezoelectric Actuated Nano-Stage: A Gray Box Neural Network Based Approach

Ahmed

Yan

2021

IEEE Access

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A modeling and parameter identification method for rate dependent hysteresis of piezoelectric actuated nano-stage is presented in this work. A system level quasi-static hysteresis model is employed to construct a neural network. To better describe the rate dependent behavior of hysteresis in piezoelectric actuated stage, a Nonlinear AutoRegressive Moving Average with eXogenous input (NARMAX) based dynamic model is incorporated with the quasi-static hysteresis model, where the weights of specifically designed neural network corresponds to the model parameters. To handle the multivalued problem of hysteresis, generalized input gradient is proposed to convert multivalued mapping of hysteresis into one-toone mapping. The parameters of the nonlinear rate dependent hysteresis in piezoelectric actuated stage is identified by neural network training, taking advantage of their universal function approximation capabilities. The proposed scheme is also compared with conventional black box and particle swarm optimization identification (PSO) based methods, where simulation and experimental results demonstrate significant performance improvement with the proposed method.

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“…Feedforward compensation techniques are highly dependent on model accuracy and are consequently not robust to system parameter uncertainty, [19]. Repetitive and iterative control schemes are significantly complex when compared to the performance they deliver as they generally require accurate models for not only the linear dynamics but also that of hysteresis and at times, creep [20]. Non‐linear control strategies are dominated by sliding‐mode control schemes that are significantly complicated and encounter unwanted input chattering if poorly designed or deviated from the nominal system model [21].…”

Section: Introductionmentioning

confidence: 99%

Experimental validation of the simultaneous damping and tracking controller design strategy for high‐bandwidth nanopositioning – a PAVPF approach

Babarinde

Zhu

et al. 2020

IET Control Theory & Appl

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For scanning applications, damping and tracking controllers are employed in a dual‐loop fashion. Whilst these damping and tracking controllers are designed sequentially in literature (damping first, tracking later), it has been found that the tracking controller (typically integral or proportional–integral) influences the ‘desired’ pole locations (and thereby its damping performance) achieved by the positive acceleration, velocity and position feedback (PAVPF) damping controller. This work starts by first highlighting this unwanted effect that results in low positioning bandwidth. To address this drawback, this work presents the design, analysis and experimental validation of the simultaneous design method for the PAVPF control‐based combined damping and tracking scheme, aimed at achieving accurate, high‐bandwidth nanopositioning. It also details a recursive analytical method to simultaneously optimise the damping and tracking controller parameters resulting in almost a three‐fold increase in closed‐loop bandwidth when compared with the traditional sequential method. To further confirm the advantages of the proposed simultaneous design method, comparative experimental results conducted on one axis of a piezo‐actuated nanopositioner are presented. Significant improvements in the steady‐state positioning as well as transient response are noted. These improvements combined, result in significant gains in the raster scanning performance of nanopositioning stages.

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A review on control strategies for compensation of hysteresis and creep on piezoelectric actuators based micro systems

Cited by 174 publications

References 89 publications

Duhem Model-Based Hysteresis Identification in Piezo-Actuated Nano-Stage Using Modified Particle Swarm Optimization

Duhem Model-Based Hysteresis Identification in Piezo-Actuated Nano-Stage Using Modified Particle Swarm Optimization

Modeling and Identification of Rate Dependent Hysteresis in Piezoelectric Actuated Nano-Stage: A Gray Box Neural Network Based Approach

Experimental validation of the simultaneous damping and tracking controller design strategy for high‐bandwidth nanopositioning – a PAVPF approach

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