Modelling and Control of a Novel Soft Crawling Robot Based on a Dielectric Elastomer Actuator

Cao, Jianting; Liang, Wenyu; Ren, Qinyuan; Gupta, Ujjaval; Chen, Feifei; Zhu, Jian

doi:10.1109/icra.2018.8460784

Cited by 31 publications

(22 citation statements)

References 11 publications

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“…A worm-like soft actuator was created as well as a controller in the work of Cao et al [19]. For material parametrization they used a multi-layer Kelvin-Voigt model.…”

Section: Introductionmentioning

confidence: 99%

Position Control of the Dielectric Elastomer Actuator Based on Fractional Derivatives in Modelling and Control

Karner

Gotlih

2021

Actuators

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Successful control of a dielectric elastomer actuator (DEA) can be a challenging task, especially if no overshoot is desired. The work presents the first use of the PIλDμ control for a dielectric elastomer actuator to eliminate the overshoot. The mathematical model of the dielectric elastomer was established using the fractional Kelvin-Voigt model. Step responses are first tested in the Laplace domain, which gave the most satisfactory results. However, they did not represent the real model. It cannot have negative force acting on the dielectric elastomer actuator. Simulations in Matlab/Simulink were performed to obtain more realistic responses, where output of the PIλDμ controller was limited. Initial parameters for a PID control were obtained by the Wang–Juang–Chan algorithm for the first order plus death time function approximation to the step response of the model, and reused as the basis for the PIλDμ actuator control. A quasi-anti-windup method was introduced to the final control algorithm. Step responses of the PID and the PIλDμ in different domains were verified by simulation and validated by experiments. Experiments proved that the fractional calculus PIλDμ step responses exceeded performance of the basic PID controller for DEA in terms of response time, settling time, and overshoot elimination.

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“…A worm-like soft actuator was created as well as a controller in the work of Cao et al [19]. For material parametrization they used a multi-layer Kelvin-Voigt model.…”

Section: Introductionmentioning

confidence: 99%

Position Control of the Dielectric Elastomer Actuator Based on Fractional Derivatives in Modelling and Control

Karner

Gotlih

2021

Actuators

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“…Among these actuators, dielectric elastomer actuators (DEAs) which is one of the EAPs stand out in robotic applications due to their special properties including large deformation, fast response, high energy density, low noise and biological muscle similarities. The muscle-like properties and relatively simple actuation method of DEAs have contributed to developing several bio-inspired soft robots, such as worm-like crawling robots (Shian et al, 2015 ; Mihai Duduta and Wood, 2017 ; Cao et al, 2018a ), underwater robots (Guo et al, 2012 ; Li et al, 2017 ) and human-like robots (Carpi and Rossi, 2005 ; Liu et al, 2008 ; Wang and Zhu, 2016 ). There are two notable work Qin et al ( 2018 ) and Gu et al ( 2018 ) should be pointed out.…”

Section: Introductionmentioning

confidence: 99%

“…Although the DEA-based soft robots exhibit muscle-like motions in various environments, the studies presented in Qin et al ( 2018 ) and Gu et al ( 2018 ) mainly focus on the robot modality which is based on the open-loop control. Only a few studies on the robot control like (Cao et al, 2018a , b ) are available. In addition, there is a major practical challenge lies in the control issue due to the electromechanical coupling and viscoelasticity of the DE materials.…”

Section: Introductionmentioning

confidence: 99%

“…However, this control approach can not adjust the performance of the control outputs due to the lack of feedback information. In order to enhance the robustness of the control system, feedback control schemes have been adopted in several studies, the notable examples include the classical proportional-integral-derivative (PID) control scheme presented in Rizzello et al ( 2015 ) and Cao et al ( 2018a ) and the cerebellum-inspired adaptive controller proposed in Wilson et al ( 2016 ); Cao et al ( 2018b ). It should be pointed out that these previous researches on controlling DEA is primarily restricted with isolated actuators in simple geometries, and the control effect depends on model accuracy due to the model-based property (Rivera et al, 1986 ; Hong et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, since the DEA only performs open-loop control in each action cycle, for the sake of improving the performance of the robot's motion trajectory tracking, the input and output information of the DEA control system in past cycles is generally used to formulate the input signal for the next iteration which is totally repetitive tasks. Secondly, as pointed out by Cao et al ( 2018a , b ), one of the main reasons that deters us from controlling DEA-based soft robots is its difficulty of obtaining accurate models. Therefore, the model-free control methods can greatly reduce the workload, not to mention its excellent tracking performances.…”

Section: Introductionmentioning

confidence: 99%

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Iterative Learning Control for Motion Trajectory Tracking of a Circular Soft Crawling Robot

Chi

Liang

et al. 2019

Front. Robot. AI

Self Cite

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Soft robots have recently received much attention with their infinite degrees of freedoms and continuously deformable structures, which allow them to adapt well to the unstructured environment. A new type of soft actuator, namely, dielectric elastomer actuator (DEA) which has several excellent properties such as large deformation and high energy density is investigated in this study. Furthermore, a DEA-based soft robot is designed and developed. Due to the difficulty of accurate modeling caused by nonlinear electromechanical coupling and viscoelasticity, the iterative learning control (ILC) method is employed for the motion trajectory tracking with an uncertain model of the DEA. A D 2 type ILC algorithm is proposed for the task. Furthermore, a knowledge-based model framework with kinematic analysis is explored to prove the convergence of the proposed ILC. Finally, both simulations and experiments are conducted to demonstrate the effectiveness of the ILC, which results show that excellent tracking performance can be achieved by the soft crawling robot.

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Control Strategies for Soft Robot Systems

Wang

Chortos

2022

Advanced Intelligent Systems

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Soft robots have recently attracted increased attention because their characteristics of low‐cost fabrication, durability, and deformability make them uniquely suited for applications in bio‐integrated systems. Being fundamentally different from traditional rigid robots, soft robots exhibit properties of infinite degrees of freedom (DOF) and nonlinear materials properties that require innovations in control systems. With the rapid development of materials science, robotics, and artificial intelligence, the diversification of actuator mechanisms and algorithms has enabled a wide range of unique control strategies. This review summarizes the basics of actuator mechanisms and control strategies, including open‐loop control, closed‐loop control, and autonomous control, and discusses their implementation from diversified perspectives. Control strategies are evaluated based on their compatibility with materials sets, application goals, and implementation route. The emerging directions are forecasted from the perspectives of interfacing between controller and actuator, underactuated control strategies, and implementation of artificial intelligence (AI).

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Modelling and Control of a Novel Soft Crawling Robot Based on a Dielectric Elastomer Actuator

Cited by 31 publications

References 11 publications

Position Control of the Dielectric Elastomer Actuator Based on Fractional Derivatives in Modelling and Control

Position Control of the Dielectric Elastomer Actuator Based on Fractional Derivatives in Modelling and Control

Iterative Learning Control for Motion Trajectory Tracking of a Circular Soft Crawling Robot

Control Strategies for Soft Robot Systems

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