Model-free control of an electro-active polymer actuator

Sancak, Caner; Yamaç, Fatma; İtik, Mehmet; Alıcı, Gürsel

doi:10.1088/2053-1591/ab0220

Cited by 5 publications

(4 citation statements)

References 38 publications

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“…Result: Increasing the control gain, width ratio, and bias magnetic field as well as decreasing the angular velocity and pre-stress could reduce the attenuation time. [105], and PID control was directly used without any modification [104,106,107]. One of reasons why the composite controller, which has been frequently used for MRE and MRF actuators, has not been applied to EAP actuator is that its soft property with low actuating force is difficult for the realization in practical environment.…”

Section: Msm Beammentioning

confidence: 99%

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A state-of-the-art on smart materials actuators over the last decade: control aspects for diverse applications

Phu

Choi

2022

Smart Mater. Struct.

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A comprehensive review of smart materials actuators is presented from control aspects for various applications. The smart materials actuators considered in this work are actively being applied to diverse control systems: magnetorheological elastomer, magnetorheological fluid, piezoelectric material, shape memory alloy, magneto-strictive material, and electro-active polymer. Both classical and modern control approaches, which have been realized for diverse applications over the last 10 years, are surveyed and discussed using the tables and figures. In order to quickly visualize and compare the different control characteristics of each actuator, the table summarizes several aspects of the specific device/system, controller type, control target, control criterion, inherent property, and outcome. The total publications and appearance of these actuators in every year are shown through the figures to understand the research status and trend easily. In addition, the featured structures of control schemes are analyzed and discussed to provide future research direction for the enhancement of control performance. It is no doubt that this review analysis significantly supports full information so that researchers related to smart materials can understand and compare the salient characteristic of each actuator and hence develop a new or advanced one for a certain control application.

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Section: Msm Beammentioning

confidence: 99%

“…+ The output of the control was the voltage. + The control using the Preisach model and the dynamic nonlinear autoregressive exogenous (NARX) fuzzy model based adaptive particle swarm optimization identification algorithm [104,106,107].…”

mentioning

confidence: 99%

A state-of-the-art on smart materials actuators over the last decade: control aspects for diverse applications

Phu

Choi

2022

Smart Mater. Struct.

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“…They also have some drawbacks such as drift, hysteresis, and degradation in actuation performance due to solvent evaporation, influencing their performance negatively and making their modeling and control insufficient (Yao et al, 2008). As the performance of the model-based controller reduces due to the reasons mentioned above, model-free adaptive controllers are considered as a potential solution instead (Beyhan and Itik, 2016;Druitt and Alici, 2013;Sancak et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Although the results were successful, the designed controller could not provide the same performance for a long-time working period of the CPA and different CPA sample, due to the dynamic behavior change. Another work of authors shows that a model-free controller also called intelligent PID (i-PID) represents clear performance improvements in controlling the CPAs' position for long-time usage in air conditions (Sancak et al, 2019). However, there is still room for investigating the proposed controller's effect on the CPAs' blocking-force control.…”

Section: Introductionmentioning

confidence: 99%

Force control of electro-active polymer actuators using model-free intelligent control

Sancak

Yamaç

İtik

et al. 2021

Journal of Intelligent Material Systems and Structures

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In this paper, a model-free control framework is proposed to control the tip force of a cantilevered trilayer CPA and similar cantilevered smart actuators. The proposed control method eliminates the requirement of modeling the CPAs in controller design for each application, and it is based on the online local estimation of the actuator dynamics. Due to the fact that the controller has few parameters to tune, this control method provides a relatively easy design and implementation process for the CPAs as compared to other model-free controllers. Although it is not vital, in order to optimize the controller performance, a meta-heuristic particle swarm optimization (PSO) algorithm, which utilizes an initial baseline model that approximates the CPAs dynamics, is used. The performance of the optimized controller is investigated in simulation and experimentally. Successful results are obtained with the proposed controller in terms of control performance, robustness, and repeatability as compared with a conventional optimized PI controller.

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Machine Learning and Control Engineering: The Model-Free Case

Fliesś

Join

2020

Advances in Intelligent Systems and Computing

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This paper states that Model-Free Control (MFC), which must not be confused with Model-Free Reinforcement Learning, is a new tool for Machine Learning (ML). MFC is easy to implement and should be substituted in control engineering to ML via Artificial Neural Networks and/or Reinforcement Learning. A laboratory experiment, which was already investigated via today's ML techniques, is reported in order to confirm this viewpoint.

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Model-free control of an electro-active polymer actuator

Cited by 5 publications

References 38 publications

A state-of-the-art on smart materials actuators over the last decade: control aspects for diverse applications

A state-of-the-art on smart materials actuators over the last decade: control aspects for diverse applications

Force control of electro-active polymer actuators using model-free intelligent control

Machine Learning and Control Engineering: The Model-Free Case

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