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Research on the inverse kinematics prediction of a soft biomimetic actuator via BP neural network
[article]
2022
arXiv
pre-print
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To overcome these intrinsic problems, we propose a back-propagation (BP) neural network learning the inverse kinetics of the soft biomimetic actuator moving in three-dimensional space. ...
to realize the inverse kinematic control of the soft biomimetic actuator. ...
These models may not accurately capture the complex nonlinear dynamics of soft robots. ...
arXiv:2110.13418v2
fatcat:ybvovx6kfzet5hnsiyxoo234om
High-bandwidth nonlinear control for soft actuators with recursive network models
[article]
2021
arXiv
pre-print
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This technique allows for reduced model sizes and increased control loop frequencies when compared with conventional RNN models. ...
We present a high-bandwidth, lightweight, and nonlinear output tracking technique for soft actuators that combines parsimonious recursive layers for forward output predictions and online optimization using ...
Killpack, “Learning
nonlinear dynamic models of soft robots for model predictive control with neural networks”,
2018 IEEE International Conference on Soft Robotics (RoboSoft), Livorno, 2018, ...
arXiv:2101.01139v1
fatcat:3dlvxlnjivb5lii3cfwlc7pqa4
Smith Predictor Type Control Architectures for Time Delayed Teleoperation
2006
The international journal of robotics research
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The proposed architectures consist of a novel pseudo two-channel nonlinear predictive controller and its variations that use neural networks for online estimation of the slave and environment dynamics ...
An early control methodology for time delayed plants is the Smith predictor, in which the plant model is utilized to predict the nondelayed output of the plant and move the delay out of the control loop ...
Acknowledgments This work was supported in part by Natural Sciences and Engineering Research Council of Canada (NSERC), Canada Foundation for Innovation (CFI) and Ontario Innovation Trust (OIT). ...
doi:10.1177/0278364906068393
fatcat:z6a3hp3j3bdfnafnzlxwl2naby
Nonlinear Systems Identification Using Deep Dynamic Neural Networks
[article]
2016
arXiv
pre-print
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This paper investigates the effectiveness of deep neural networks in the modeling of dynamical systems with complex behavior. ...
Recently, deep neural networks have proven to be very effective in pattern recognition, classification tasks and human-level control to model highly nonlinear realworld systems. ...
as in § VI-D with the soft-robot and glassfurnace network. ...
arXiv:1610.01439v1
fatcat:r233vl4fajcnfmx5vettogmdsm
Neural Model Extraction for Model-Based Control of a Neural Network Forward Model
2021
SN Computer Science
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In this research, we propose a new approach-i.e., neural model extraction, that enables model-based control for a feed-forward neural network trained for a nonlinear state equation. ...
Thus far, because neural networks are a radically different approach to mathematical modeling, control theory has not been applied to them, even if they approximate the nonlinear state equation of a control ...
We assume that the NN was trained to learn the nonlinear state equation of a robot whose dynamics are extremely difficult to mathematically model, such as for soft and bio-inspired robots. ...
doi:10.1007/s42979-021-00456-4
fatcat:x36obdlovvftdgrnn2bywmall4
LEARNING SOFT ROBOT AND SOFT ACTUATOR DYNAMICS USING DEEP NEURAL NETWORK
2021
International Journal of Engineering Applied Sciences and Technology
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In this paper, modelling of the soft robot using finite element modelling will be discussed in conjunction with deep neural network for the bending and control of the end effector. ...
Inspired by living organisms and being the forefront of robotics evolution, the research in soft robotics has been growing exponentially. ...
However, comparing with a rigid robot, modelling a soft robot can be challenging due to their complex dynamics and nonlinearity. ...
doi:10.33564/ijeast.2021.v05i09.003
fatcat:knpeztdk55hm7jywsbh5urzagy
Industrial applications of soft computing: a review
2001
Proceedings of the IEEE
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Fuzzy logic (FL), neural networks (NN), and evolutionary computation (EC) are the core methodologies of soft computing. ...
Soft computing (SC) is an evolving collection of methodologies, which aims to exploit tolerance for imprecision, uncertainty, and partial truth to achieve robustness, tractability, and low cost. ...
Chemical Process Industry McAvoy surveyed various kinds of neural networks applied to chemical processes for diagnosis, modeling, feedforward control, soft sensing, and nonlinear model prediction control ...
doi:10.1109/5.949483
fatcat:erzs7fqa35dixgm7mq2efptpai
Model-Based Control of Soft Actuators Using Learned Non-linear Discrete-Time Models
2019
Frontiers in Robotics and AI
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Deep neural networks are a powerful tool for modeling systems with complex dynamics such as the pneumatic, continuum joint, six degree-of-freedom robot shown in this paper. ...
Overall, our results show the potential of combining empirical modeling approaches with model-based control for soft robots and soft actuators. ...
Our specific contributions include the following: • Development of a non-linear neural network (NN) architecture for dynamic modeling of a 6 DoF pneumatic robot with soft actuators based on data from a ...
doi:10.3389/frobt.2019.00022
pmid:33501038
pmcid:PMC7805923
fatcat:unfm5zq4jzd6dj2fzqs3b2sjoa
Autonomous Tissue Manipulation via Surgical Robot Using Learning Based Model Predictive Control
[article]
2019
arXiv
pre-print
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Two AI learning based model predictive control algorithms using vision strategies are proposed and studied: (1) reinforcement learning and (2) learning from demonstration. ...
The complex dynamics of soft tissue as an unstructured environment is one of the main challenges in any attempt to automate the manipulation of it via a surgical robotic system. ...
As opposed to the reviewed model-free approaches using linearization, the proposed algorithms directly learn nonlinear dynamics of tissue in image space and control the robot with nonlinear optimization ...
arXiv:1902.01459v2
fatcat:ii4wxoyc2fcilkxl6d4b34dyfq
Review of machine learning methods in soft robotics
2021
PLoS ONE
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followed by a summary of the existing machine learning methods for soft robots. ...
However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity ...
Hyatt et al. proposed a predictive model based on the neural networks, and a learning method for the linearized discrete state space representation of soft robots [87, 88] . G. ...
doi:10.1371/journal.pone.0246102
pmid:33600496
pmcid:PMC7891779
fatcat:alu4zm72irespj6wydikzjb6ie
Neural Networks Predicting Microbial Fuel Cells Output for Soft Robotics Applications
2021
Frontiers in Robotics and AI
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The use of neural networks, namely a nonlinear autoregressive network with exogenous inputs was employed to predict the electrical output of an MFC, given its previous outputs and feeding volumes. ...
The development of biodegradable soft robotics requires an appropriate eco-friendly source of energy. ...
NONLINEAR AUTOREGRESSIVE NETWORK WITH EXOGENOUS INPUTS Some applications of NARX networks are predictors, nonlinear filters or models of nonlinear dynamic systems. ...
doi:10.3389/frobt.2021.633414
pmid:33748191
pmcid:PMC7969642
fatcat:ch3a6trvy5acrijrghd7sktnwa
SOFT ROBOT POSITIONING USING ARTIFICIAL NEURAL NETWORK
2019
Facta Universitatis Series Automatic Control and Robotics
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The experiment investigated the performance of an artificial neural network in solving the inverse kinematic problem of a soft robot. ...
The network was trained and tested using records collected at 200 randomly chosen robot positions. The relative testing error of positioning, about 5%, confirmed a predictable robot behavior. ...
The purpose of the experiment was to prove that an artificial neural network can be trained to perform a predictable soft robot control by approximating the inverse kinematic problem. ...
doi:10.22190/fuacr1901019k
fatcat:yq5cz63np5b7rdesvxn7zspima
Table of Contents
2022
IEEE/ASME transactions on mechatronics
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Tavakoli 645 Adaptive Neural Network Vibration Control for an Output-Tension-Constrained Axially Moving Belt System With Input Nonlinearity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Xu 800 Neural-Dynamics Optimization and Repetitive Learning Control for Robotic Leg Prostheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
doi:10.1109/tmech.2022.3162010
fatcat:eocxijxyovctpdf5iujxwgwpny
Soft robot perception using embedded soft sensors and recurrent neural networks
2019
Science Robotics
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This approach enables the development of force and deformation models for soft robotic systems, which can be useful for a variety of applications, including human-robot interaction, soft orthotics, and ...
However, both the soft sensors—and the encasing dynamical system—often exhibit nonlinear time-variant behavior, which makes them difficult to model. ...
Other data for this study can be found in the database (https://github.com/mlsensor/SciRo). ...
doi:10.1126/scirobotics.aav1488
pmid:33137762
fatcat:xr2se6p6t5dp7mro2c6trxc3da
Editorial: Advances in Modeling and Control of Soft Robots
2021
Frontiers in Robotics and AI
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They address dynamic modeling and related control of soft robots, starting from the current approaches based on statics, or second-order dynamics, and model predictive control (MPC), using basic lumped-parameters ...
The work by Hyatt et al. demonstrates that online model adaptation is key in soft robot dynamic modeling and shows their results with a model predictive control. It is based on Edited and reviewed by: ...
ACKNOWLEDGMENTS The authors wish to acknowledge the contribution of speakers and attendees of the IEEE/RSJ IROS 2019 Workshop on "Advances in Modeling and Control of Soft Robots". ...
doi:10.3389/frobt.2021.706514
pmid:34095241
pmcid:PMC8176095
fatcat:xavqtjeulfggzebmia5tf6jso4
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