Robust tracking control for operator‐based uncertain micro‐hand actuator with Prandtl–Ishlinskii hysteresis

Li, Xiaoyong; Bu, Ni; Zhang, Yuyi

doi:10.1002/asjc.2951

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…To eliminate the influence of hysteresis, refs. [15,16] proposed a method using an inverse hysteresis model combined with RCF theory, and the results of their simulations proved the effectiveness.…”

Section: Introductionmentioning

confidence: 94%

Right Coprime Factorization-Based Simultaneous Control of Input Hysteresis and Output Disturbance and Its Application to Soft Robotic Finger

An,

Deng,

Morohoshi

2024

Electronics

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In a nonlinear control system, hysteresis exists usually as common characteristics. In addition, external output disturbances like modelling error, machine friction and so on also occur frequently. Both of them are considered to cause instability and unsatisfactory performance. In this paper, a practical nonlinear control system design is proposed so as to achieve the simultaneous control of input hysteresis and output disturbance. The system is based on RCF (right coprime factorization theory). Additionally, the proposed design has been applied to a soft robotic finger system and the results of simulations and practical experiments are exhibited, which show the effectiveness of the proposed system.

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Section: Introductionmentioning

confidence: 94%

Right Coprime Factorization-Based Simultaneous Control of Input Hysteresis and Output Disturbance and Its Application to Soft Robotic Finger

An,

Deng,

Morohoshi

2024

Electronics

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“…In order to mitigate the effect of hysteresis, researchers have proposed a number of methods to compensate for hysteretic nonlinearity, such as feedforward inverse compensation control, 12 PID compound control, adaptive compound control, [13][14][15] sliding mode control and neural network control and so forth. [16][17][18] The first step to deal with the hysteresis is to select an appropriate mathematical model, and then compensate the hysteresis through different control algorithms. In order to solve the hysteresis behavior, many nonlinear models are proposed to describe the hysteresis characteristics, which are roughly divided into static models and dynamic models, among them, Preisach model, 19 Krasnoselskii-Pokrovkii hysteresis 20 and PI hysteresis model 21 belong to the static models while Duhen model 22 and Bouc-Wen model 23 belong to the dynamic models.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust tracking control for uncertain micro‐hand actuator with Prandtl‐Ishlinskii hysteresis

Zhang

2023

Intl J Robust & Nonlinear

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This article is concerned with the robust tracking control for an uncertain micro‐hand actuator with Prandtl‐Ishlinskii hysteresis (PI hysteresis) by robust right coprime factorization (RRCF). Firstly, the PI hysteresis is modeled into the rubber micro‐hand actuator and a new hysteretic micro‐hand system is obtained. Secondly, by compensating the hysteresis part into the isomorphic subspace, the effect of hysteresis on micro‐hand system is transferred and the operator‐based robust controllers are designed to ensure that the robust stability of the whole system. Thirdly, the RRCF and terminal sliding mode control are combined for tracking control, and the nonsingular terminal sliding mode surface with integral terms is designed, which shorten the convergence time and improve the tracking performance. Finally, the effectiveness of the method is tested by the simulation of the micro‐hand system.

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Fractional order modeling and internal model control method for dielectric elastomer actuator

Xu,

Wu,

Wang

2024

Asian Journal of Control

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The dielectric elastomer actuator (DEA) is widely used in the field of soft robots due to its large deformation, light weight, fast response, and high‐energy conversion efficiency. The high‐precision control of the DEA is the precondition for soft robots to perform complicated tasks. In early studies, researchers usually employed integer order modeling and control methods to build the dynamic model of the DEA and to achieve its tracking control. However, these methods are not good at handling the complicated memory property of the DEA. In addition, the number of required parameters in integer order models and control methods is enormous, which hinders their practical applications. To solve these problems, the fractional order modeling method and fractional order internal model control method of the DEA are proposed in this paper. Firstly, a fractional order transfer function (FOTF) model of the DEA is built to depict its complicated memory property. Then, to achieve the computer control, an integer order approximation model (IOAM) of the FOTF model is built by using the Oustaloup filter. Considering that the order of the IOAM is too high, a reduced integer order approximation model is established by using the square root balance truncation algorithm to facilitate the system controller design. Next, a fractional order internal model controller is designed. Finally, tracking control experiments are exerted to demonstrate the effectiveness of the proposed method. Since the root‐mean‐square errors of all experimental results are less than 2%, the proposed modeling method and control method are superior from the perspective of the practical application.

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Robust tracking control for operator‐based uncertain micro‐hand actuator with Prandtl–Ishlinskii hysteresis

Cited by 4 publications

References 27 publications

Right Coprime Factorization-Based Simultaneous Control of Input Hysteresis and Output Disturbance and Its Application to Soft Robotic Finger

Right Coprime Factorization-Based Simultaneous Control of Input Hysteresis and Output Disturbance and Its Application to Soft Robotic Finger

Robust tracking control for uncertain micro‐hand actuator with Prandtl‐Ishlinskii hysteresis

Fractional order modeling and internal model control method for dielectric elastomer actuator

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