A study on the control of an IPMC actuator using an adaptive fuzzy algorithm

Oh, Sinjong; Kim, Hunmo

doi:10.1007/bf03028784

Cited by 12 publications

(6 citation statements)

References 13 publications

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“…Over the settling time, the response of the control system is stable and the difference between the commanded reference and the output response is remarkably reduced. Also, it can be seen that the response by the ANFC is faster than results of previous studied methods, which used in reference papers [4,5,6] being only valid for shorttime control. With the ANFC controller, the time response of step-signals is faster with same desired signals and is controlled within long time.…”

Section: Fig 2 Experiments Setup For Feedback Control System Of Ipmcmentioning

confidence: 68%

Adaptive Neuro-Fuzzy Control for Ionic Polymer Metal Composite Actuators

Thinh

Dang

2014

Robot Intelligence Technology and Applications 2

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Electroactive polymers (EAPs) have many attractive characteristics for applications, especially for biomimetics robots and bio-medical devices. Among the electroactive polymers, the ionic polymer metal composite (IPMC) is the commonly used EAPs. The IPMC is new generation of smart materials with significant potential in producing biomimetic robots and smart structures, and for medical applications. Ionic polymer metal composites (IPMC) have attracted great attention in the past years due to its large strain. IPMC materials have quite an unpredictable behavior due to several critical aspects that produce a change in their dynamic response. For modeling of controlling the IPMC, it is required to find suitable algorithm. In order to avoid difficult problems in control, a controller based Adaptive Neuro-Fuzzy Inference System (ANFIS), which combines the merits of fuzzy logic and neural network, is used for tracking position of IPMC actuator. This paper describes the using of controller based on Neuro and Fuzzy for controlling an IPMC actuator under water to improve tracking ability for an IPMC actuator like as biomimetics and biomedical devices. The results showed that ANFIS algorithm is reliable in controlling IPMC actuator. In addition, experimental results show that the ANFIS performed better than the pure fuzzy controller (PFC). Present results show that the current adaptive neuro-fuzzy controller can be successfully applied to the real-time control of the ionic polymer metal composite actuator for which the performance degrades under long-term actuation.

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Section: Fig 2 Experiments Setup For Feedback Control System Of Ipmcmentioning

confidence: 68%

Adaptive Neuro-Fuzzy Control for Ionic Polymer Metal Composite Actuators

Thinh

Dang

2014

Robot Intelligence Technology and Applications 2

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“…Based on dynamic modeling studies, an IPMC actuator was positioned using the self-adaptive fuzzy algorithm for location control of an IPMC micro-pump [26]. The hysteresis characteristics of the IPMC actuator were analyzed using the discrete Prandtl-Ishlinskii model, the creep model of the IPMC actuator was obtained, and an inverse hysteresis model was developed by modifying the creep model of piezoelectric materials, followed by the development of a self-adaptive inverse control strategy for the displacement of the IPMC actuator [27].…”

Section: Controlmentioning

confidence: 99%

Modeling and Control of IPMC Actuators Based on LSSVM-NARX Paradigm

Huang

Zhao³

et al. 2019

Mathematics

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Ionic polymer-metal composites are electrically driven intelligent composites that are readily exposed to bending deformations in the presence of external electric fields. Owing to their advantages, ionicpolymer-metal composites are promising candidates for actuators. However, ionicpolymer-metal composites exhibit strong nonlinear properties, especially hysteresis characteristics, resulting in severely reduced control accuracy. This study proposes an ionic polymer-metal composite platform and investigates its modeling and control. First, the hysteresis characteristics of the proposed Pt-electrode ionic polymer-metal composite are tested. Based on the hysteresis characteristics, ionic polymer-metal composites are modeled using the Prandtl-Ishlinskii model and the least squares support vector machine-nonlinear autoregressive model, respectively. Then, the ionic polymer-metal composite is driven by a random sinusoidal voltage, and the LSSVM-NARX model is established on the basis of the displacement data obtained. In addition, an artificial bee colony algorithm is proposed for accuracy optimization of the model parameters. Finally, an inverse controller based on the least squares support vector machine-nonlinear autoregressive model is proposed to compensate the hysteresis characteristics of the ionic polymer-metal composite. A hybrid PID feedback controller is developed by combining the inverse controller with PID feedback control, followed by simulation and testing of its actual position control on the ionic polymer-metal composite platform. The results show that the hybrid PID feedback control system can effectively eliminate the effects of the hysteresis characteristics on ionic polymer-metal composite control.

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“…However, the IPMC shows mainly nonlinear behaviors in characteristics of large strain and stress, moreover, the control performance is affected by the parameter variations and various disturbances easily, it is difficult to obtain a precise mathematical model. So, in order to improve the control performance and achieve robust tracking, some approaches such as adaptive control scheme, neural network control method, and fuzzy control algorithm have been designed in precision position control and achieved some results [4,5]. But adaptive control scheme demands real-time seriously and has limitations in the uncertainties of system structure.…”

Section: Introductionmentioning

confidence: 99%

Robust nonlinear control design for ionic polymer metal composite based on sliding mode approach

Wang

Zhang

Wang

2014

2014 UKACC International Conference on Control (CONTROL)

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In this article, a robust nonlinear tracking control design for ionic polymer metal composite (IPMC) with uncertainties is presented by using particle swarm optimization (PSO) based sliding mode approach. In detail, for a nonlinear dynamic model with uncertainties, an IPMC artificial muscles position tracking control system based on sliding mode control approach is presented, where, a saturation function is used in the sliding mode control law design to suppress chattering, and the control parameters of sliding mode control are optimized by using particle swarm optimization to obtain quick convergence. The robust stability can be guaranteed. Finally, the effectiveness of the proposed method is confirmed by simulation results.

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A study on the control of an IPMC actuator using an adaptive fuzzy algorithm

Cited by 12 publications

References 13 publications

Adaptive Neuro-Fuzzy Control for Ionic Polymer Metal Composite Actuators

Adaptive Neuro-Fuzzy Control for Ionic Polymer Metal Composite Actuators

Modeling and Control of IPMC Actuators Based on LSSVM-NARX Paradigm

Robust nonlinear control design for ionic polymer metal composite based on sliding mode approach

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