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Selected Papers from The 11th International Conference on Advanced Mechatronic Systems (ICAMechS 2021)

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A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Automation and Control Systems".

Deadline for manuscript submissions: closed (15 September 2022) | Viewed by 14484

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Special Issue Editors

Prof. Dr. Mingcong Deng

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Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan
Interests: nonlinear safety control and fault detection; real time estimation of human arm impedance; smart material actuators; micro hands; wireless power transfer systems; micro reactors
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Prof. Dr. Takao Sato

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School of Engineering & Graduate School of Engineering, University of Hyogo, Hyogo 671-2280, Japan
Interests: automatic control; control education; data-driven control; mechatronic systems
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Dr. Changan Jiang

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Department of Robotics, Osaka Institute of Technology, Osaka 535-8585, Japan
Interests: robotics; smart material actuators; robust control
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Dr. Lin Meng

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Guest Editor

Department of Electronic and Computer Engineering, Ritsumeikan University, Kusatsu, Japan
Interests: processor architecture; high-performance computing; AI-based IoT; underwater drones; cultural heritage preservation and protection
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Special Issue Information

Dear Colleagues,

The 11th International Conference on Advanced Mechatronic Systems (ICAMechS 2021, http://web.tuat.ac.jp/~deng/ICAMechS2021/icamechs2021.html) will be held in Tokyo, Japan, December 9–12, 2021. It provides an international forum for professionals, academics, and researchers to present the latest developments from interdisciplinary theoretical studies, computational algorithm development and applications of mechatronic systems. It particularly welcomes those emerging methodologies and techniques which bridge theoretical studies and applications in all mechatronic systems. Novel quantitative engineering and science studies may be considered as well. Papers are solicited on all aspects of research and application, including but not limited to:

Intelligent mechatronics, robotics, automation, and control systems;
Control system modeling and simulation techniques and methodologies;
Biomedical and rehabilitation engineering, prosthetics and artificial organs;
Industrial automation, process control, manufacturing process and automation;
CAD/CAM/CAE/CAP and manufacturing systems, technologies and applications;
AI, intelligent control, fuzzy control and their applications;
Signal and image processing and pattern recognition in mechatronic systems;
Wind energy conversion systems, aerodynamics, simulation and optimization;
Grey systems, discrete event and hybrid systems, Petri nets;
Power systems (renewable energy, smart grids, micro grids, energy saving);
Energy and environment.

Prof. Dr. Mingcong Deng
Dr. Takao Sato
Dr. Changan Jiang
Prof. Dr. Lin Meng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (6 papers)

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Research

14 pages, 2125 KiB

Open AccessArticle

Parameter Estimation for Robotic Manipulator Systems

by Qianfeng Zhu, Zhihong Man, Zhenwei Cao, Jinchuan Zheng and Hai Wang

Machines 2022, 10(5), 392; https://doi.org/10.3390/machines10050392 - 19 May 2022

Viewed by 1928

Abstract

In this paper, a novel methodology for estimating the parameters of robotic manipulator systems is proposed. It can be seen that, for the purpose of parameter estimation, the input torque to each joint motor is designed as a linear combination of sinusoids. After the transient responses of joint angles exponentially converge to zero, the steady states of joint angle outputs can be extracted. Since the steady states of joint angles are the equivalent finite Fourier series, the coefficients of the steady state components of joint angles can be further extracted in a fundamental period. With the amazing finding that the steady states contain all dynamic information of manipulator systems, all unknown parameters of the system model can be accurately estimated with the extracted coefficients in finite frequency bands. The simulation results for a two-link manipulator are carried out to illustrate the effectiveness and robustness against measurement noise of the proposed method. Full article

(This article belongs to the Special Issue Selected Papers from The 11th International Conference on Advanced Mechatronic Systems (ICAMechS 2021))

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16 pages, 491 KiB

Open AccessArticle

Stability of Zeros for Sampled-Data Models with Triangle Sample and Hold Implemented by Zero-Order Hold

by Minghui Ou, Zhiyong Yang, Zhenjie Yan, Mingkun Ou, Shuanghong Liu, Shan Liang and Shengjiu Liu

Machines 2022, 10(5), 386; https://doi.org/10.3390/machines10050386 - 17 May 2022

Cited by 1 | Viewed by 1483

Abstract

This paper deals with the stability characteristics of zeros for sampled-data models with a class of triangle sample and hold realized by a traditional zero-order hold. For any controlled models in the modern industrial system, using a digital control strategy has been shown to provide the means to achieve the assigned objectives. In this process, one must utilize the sample and hold device to obtain the sampled-data models. Previous studies have shown that the triangle sample and hold can improve the stability properties of zeros of a sampled-data control system compared with zero-order hold. However, it is difficult to use triangle sample and hold in practice. In this paper, an approximated method of using triangle sample and hold is proposed. More importantly, on the basis of that method, we explicitly derive the corresponding accurate sampled-data model of controlled models. In addition, we also provide the expression for sampling zeros and the theorem for the stability of a linear control system in the fast sampling process. The results of this paper show that the proposed method has the same advantages as the accurate one. Finally, theoretical findings are validated through numerical simulations with different considerations. Full article

(This article belongs to the Special Issue Selected Papers from The 11th International Conference on Advanced Mechatronic Systems (ICAMechS 2021))

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18 pages, 985 KiB

Open AccessArticle

Operator & Fractional Order Based Nonlinear Robust Control for a Spiral Counter-Flow Heat Exchanger with Uncertainties and Disturbances

by Guanqiang Dong and Mingcong Deng

Machines 2022, 10(5), 335; https://doi.org/10.3390/machines10050335 - 4 May 2022

Cited by 1 | Viewed by 1343

Abstract

This paper studies operator and fractional order nonlinear robust control for a spiral counter-flow heat exchanger with uncertainties and disturbances. First, preliminary concepts are presented concerning fractional order derivative and calculus, fractional order operator theory. Then, the problem statement about nonlinear fractional order derivative equation with uncertainties is described. Third, the design of an operator fractional order controller and fractional order PID controller and determination of several related parameters is described. Simulations were performed to verify tracking and anti-disturbance performance by comparison to different control cases; verification is described and concluding remarks provided. Full article

(This article belongs to the Special Issue Selected Papers from The 11th International Conference on Advanced Mechatronic Systems (ICAMechS 2021))

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14 pages, 558 KiB

Open AccessArticle

Feedforward–Feedback Controller Based on a Trained Quaternion Neural Network Using a Generalised

HR

Calculus with Application to Trajectory Control of a Three-Link Robot Manipulator

by Kazuhiko Takahashi, Eri Tano and Masafumi Hashimoto

Machines 2022, 10(5), 333; https://doi.org/10.3390/machines10050333 - 2 May 2022

Cited by 2 | Viewed by 1507

Abstract

This study derives a learning algorithm for a quaternion neural network using the steepest descent method extended to quaternion numbers. This applies the generalised Hamiltonian–Real calculus to obtain derivatives of a real–valued cost function concerning quaternion variables and designs a feedback–feedforward controller as a control system application using such a network. The quaternion neural network is trained in real-time by introducing a feedback error learning framework to the controller. Thus, the quaternion neural network-based controller functions as an adaptive-type controller. The designed controller is applied to the control problem of a three-link robot manipulator, with the control task of making the robot manipulator’s end effector follow a desired trajectory in the Cartesian space. Computational experiments are conducted to investigate the learning capability and the characteristics of the quaternion neural network used in the controller. The experimental results confirm the feasibility of using the derived learning algorithm based on the generalised Hamiltonian–Real calculus to train the quaternion neural network and the availability of such a network for a control systems application. Full article

(This article belongs to the Special Issue Selected Papers from The 11th International Conference on Advanced Mechatronic Systems (ICAMechS 2021))

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14 pages, 361 KiB

Open AccessArticle

Add-On Type Data-Driven Ripple-Free Dual-Rate Control Design Based on the Null Space of Steady-State Step Responses

by Takao Sato, Ryota Yasui and Natsuki Kawaguchi

Machines 2022, 10(5), 296; https://doi.org/10.3390/machines10050296 - 22 Apr 2022

Viewed by 1311

Abstract

In the present study, a data-driven ripple-free design is proposed for a dual-rate sampled-data control system in which the sampling interval of the plant output is longer than the holding interval of the control input. The objective of the present study is to improve the steady-state intersample response without changing the sampled response and without using the plant model. To achieve the objective directly from controlled data, an add-on input based on the null space of steady-state step responses to an existing control system is used. The open-loop or closed-loop system to obtain the step response is assumed to be stable. In the present study, a two-degree-of-freedom design is given that redesigns the intersample output response independently of the steady-state sampled output response. In a numerical example, the proposed method is applied to a linear time-invariant single-input single-output stable system, where intersample ripples are eliminated using the add-on input that is independent of the existing sample output response in steady state. Full article

(This article belongs to the Special Issue Selected Papers from The 11th International Conference on Advanced Mechatronic Systems (ICAMechS 2021))

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20 pages, 3534 KiB

Open AccessEditor’s ChoiceArticle

YOLO-GD: A Deep Learning-Based Object Detection Algorithm for Empty-Dish Recycling Robots

by Xuebin Yue, Hengyi Li, Masao Shimizu, Sadao Kawamura and Lin Meng

Machines 2022, 10(5), 294; https://doi.org/10.3390/machines10050294 - 22 Apr 2022

Cited by 32 | Viewed by 5301

Abstract

Due to the workforce shortage caused by the declining birth rate and aging population, robotics is one of the solutions to replace humans and overcome this urgent problem. This paper introduces a deep learning-based object detection algorithm for empty-dish recycling robots to automatically recycle dishes in restaurants and canteens, etc. In detail, a lightweight object detection model YOLO-GD (Ghost Net and Depthwise convolution) is proposed for detecting dishes in images such as cups, chopsticks, bowls, towels, etc., and an image processing-based catch point calculation is designed for extracting the catch point coordinates of the different-type dishes. The coordinates are used to recycle the target dishes by controlling the robot arm. Jetson Nano is equipped on the robot as a computer module, and the YOLO-GD model is also quantized by TensorRT for improving the performance. The experimental results demonstrate that the YOLO-GD model is only 1/5 size of the state-of-the-art model YOLOv4, and the

m A P

of YOLO-GD achieves 97.38%, 3.41% higher than YOLOv4. After quantization, the YOLO-GD model decreases the inference time per image from 207.92 ms to 32.75 ms, and the

m A P

is 97.42%, which is slightly higher than the model without quantization. Through the proposed image processing method, the catch points of various types of dishes are effectively extracted. The functions of empty-dish recycling are realized and will lead to further development toward practical use. Full article

(This article belongs to the Special Issue Selected Papers from The 11th International Conference on Advanced Mechatronic Systems (ICAMechS 2021))

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