Application of Artificial Intelligence in Mechatronics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 11906

Special Issue Editors

School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: fibrous composites; metallic alloys; hybrid composite stacks; high-performance materials; functional surfaces; multilayer coatings; coating evaluation; coated tools; mechanical machining; materials processing; numerical modeling surface texturing
Special Issues, Collections and Topics in MDPI journals
School of Mechanical Engineering, and Key Laboratory of High Efficiency and Clean Mechanical Manufacture of Ministry of Education, National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan 250061, China
Interests: robotics; motion control; non-linear control theory
Special Issues, Collections and Topics in MDPI journals
School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China
Interests: artificial intelligence; computational intelligence; evolutionary computation; swarm intelligence; particle swarm optimization

Special Issue Information

Dear Colleagues,

In recent years, artificial intelligence has promoted the rapid development of intelligence in various fields, and mechatronics is the hot index of artificial intelligence research. The research purpose is to organically combine mechanical skills, microelectronics skills, and information skills to realize the optimization of the whole system. It is a general trend to combine the development of mechatronics systems with artificial intelligence. This intelligence is mainly designed and realized through control technology, that is, it is realized by the control system in the mechatronics system. The intelligent system can imitate human behavior, learn and imitate all kinds of known information and content in its environment, and use what it has learned for analysis, decision making and control, optimizing the entire intelligent system and achieving the best effect. Therefore, the application of artificial intelligence in mechatronics should be given more attention in research.

Dr. Jinyang Xu
Prof. Dr. Kai Guo
Prof. Dr. Zhi-Hui Zhan
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. Applied Sciences is an international peer-reviewed open access semimonthly 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.

Keywords

  • artificial intelligence
  • mechatronics
  • neural network control

Published Papers (7 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Editorial

Jump to: Research

2 pages, 160 KiB  
Editorial
Special Issue on Application of Artificial Intelligence in Mechatronics
by
Appl. Sci. 2023, 13(1), 158; https://doi.org/10.3390/app13010158 - 23 Dec 2022
Cited by 2 | Viewed by 1374
Abstract
In recent years, artificial intelligence has promoted the rapid development of intelligence in various fields, with mechatronics being one of its hot research topics [...] Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Mechatronics)

Research

Jump to: Editorial

14 pages, 3571 KiB  
Article
An Object Detection and Localization Method Based on Improved YOLOv5 for the Teleoperated Robot
Appl. Sci. 2022, 12(22), 11441; https://doi.org/10.3390/app122211441 - 11 Nov 2022
Cited by 6 | Viewed by 2375
Abstract
In the traditional teleoperation system, the operator locates the object using the real-time scene information sent back from the robot terminal; however, the localization accuracy is poor and the execution efficiency is low. To address the issues, we propose an object detection and [...] Read more.
In the traditional teleoperation system, the operator locates the object using the real-time scene information sent back from the robot terminal; however, the localization accuracy is poor and the execution efficiency is low. To address the issues, we propose an object detection and localization method for the teleoperated robot. First, we improved the classic YOLOv5 network model to produce superior object detection performance and named the improved model YOLOv5_Tel. On the basis of the classic YOLOv5 network model, the feature pyramid network was changed to a bidirectional feature pyramid network (BiFPN) network module to achieve the weighted feature fusion mechanism. The coordinate attention (CA) module was added to make the model pay more attention to the features of interest. Furthermore, we pruned the model from the depth and width to make it more lightweight and changed the bounding box regression loss function GIOU to SIOU to speed up model convergence. Then, the YOLOv5_Tel model and ZED2 depth camera were used to achieve object localization based on the binocular stereo vision ranging principle. Finally, we established an object detection platform for the teleoperated robot and created a small dataset to validate the proposed method. The experiment shows that compared with the classic YOLOv5 series network model, the YOLOv5_Tel is higher in accuracy, lighter in weight, and faster in detection speed. The mean average precision (mAP) value of the YOLOv5_Tel increased by 0.8%, 0.9%, and 1.0%, respectively. The model size decreased by 11.1%, 70.0%, and 86.4%, respectively. The inference time decreased by 9.1%, 42.9%, and 58.3%, respectively. The proposed object localization method has a high localization accuracy with an average relative error of only 1.12%. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Mechatronics)
Show Figures

Figure 1

29 pages, 6690 KiB  
Article
Target State Optimization: Drivability Improvement for Vehicles with Dual Clutch Transmissions
Appl. Sci. 2022, 12(20), 10283; https://doi.org/10.3390/app122010283 - 12 Oct 2022
Cited by 3 | Viewed by 1565
Abstract
Vehicles with dual clutch transmissions (DCT) are well known for their comfortable drivability since gear shifts can be performed jerklessly. The ability of blending the torque during gear shifts from one clutch to the other, making the type of automated transmission a perfect [...] Read more.
Vehicles with dual clutch transmissions (DCT) are well known for their comfortable drivability since gear shifts can be performed jerklessly. The ability of blending the torque during gear shifts from one clutch to the other, making the type of automated transmission a perfect alternative to torque converters, which also comes with a higher efficiency. Nevertheless, DCT also have some drawbacks. The actuation of two clutches requires an immense control effort, which is handled in the implementation of a wide range of software functions on the transmission control unit (TCU). These usually contain control parameters, which makes the behavior adaptable to different vehicle and engine platforms. The adaption of these parameters is called calibration, which is usually an iterative time-consuming process. The calibration of the embedded software solutions in control units is a widely known problem in the automotive industry. The calibration of any vehicle subsystem (e.g., engine, transmission, suspension, driver assistance systems for autonomous driving, etc.) requires costly test trips in different ambient conditions. To reduce the calibration effort and the accompanying use of professionals, several approaches to automize the calibration process are proposed. Due to the fact that a solution is desired which can optimize different calibration problems, a generic metaheuristic approach is aimed. Regardless, the scope of the current research is the optimization of the launch behavior for vehicles equipped with DCT since, particularly at low speeds, the transmission behavior must meet the intention of the driver (drivers tend to be more perceptive at low speeds). To clarify the characteristics of the launch, several test subject studies are performed. The influence factors, such as engine sound, maximal acceleration, acceleration build-up (mean jerk), and the reaction time, are taken into account. Their influence on the evaluation of launch with relation to the criteria of sportiness, comfort, and jerkiness, are examined based on the evaluation of the test subject studies. According to the results of the study, reference values for the optimization of the launch behavior are derived. The research contains a study of existing approaches for optimizing driving behavior with metaheuristics (e.g., genetic algorithms, reinforcement learning, etc.). Since the existing approaches have different drawbacks (in scope of the optimization problem) a new approach is proposed, which outperforms existing ones. The approach itself is a hybrid solution of reinforcement learning (RL) and supervised learning (SL) and is applied in a software in the loop environment, and in a test vehicle. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Mechatronics)
Show Figures

Figure 1

18 pages, 3008 KiB  
Article
Adaptive Robust Fuzzy Impedance Control of an Electro-Hydraulic Actuator
Appl. Sci. 2022, 12(19), 9575; https://doi.org/10.3390/app12199575 - 23 Sep 2022
Cited by 2 | Viewed by 1118
Abstract
This paper concentrates on both velocity and force control of a single-rod electro-hydraulic actuator in the presence of parameter uncertainties and uncertain nonlinearities. Both velocity control and force control are required in some cases. Impedance control and adaptive robust control are synthesized to [...] Read more.
This paper concentrates on both velocity and force control of a single-rod electro-hydraulic actuator in the presence of parameter uncertainties and uncertain nonlinearities. Both velocity control and force control are required in some cases. Impedance control and adaptive robust control are synthesized to deal with this problem. In this paper, the primary goal is velocity control while the contact force is kept in an acceptable range. To keep proper contact force with environment or workpieces, impedance control is adopted to regulate the dynamic relationship between velocity and force. Fuzzy logic is used to adjust the parameters of impedance rules to improve control performance. The velocity command of adaptive robust velocity control is determined by impedance control based on fuzzy logic. Parameter uncertainties and uncertain nonlinearities can be compensated through adaptive robust velocity control, which leads to accurate velocity tracking. The stability of the overall system was analyzed. Comparative experiments verified that the proposed control strategy has both high-accuracy velocity tracking and force regulation performance. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Mechatronics)
Show Figures

Figure 1

13 pages, 1647 KiB  
Article
A Novel Reference Governor for Disturbance Observer-Based Load Pressure Control in a Dual-Actuator-Driven Electrohydraulic Actuator
Appl. Sci. 2022, 12(16), 8367; https://doi.org/10.3390/app12168367 - 21 Aug 2022
Cited by 2 | Viewed by 1167
Abstract
In real-world applications, hydraulic pressure control performance is influenced by model uncertainties, the control bandwidths of valves and pumps, and deviations from the linear working region. To overcome the aforementioned obstacles, a novel reference governor for disturbance observer (DOB)-based load pressure control is [...] Read more.
In real-world applications, hydraulic pressure control performance is influenced by model uncertainties, the control bandwidths of valves and pumps, and deviations from the linear working region. To overcome the aforementioned obstacles, a novel reference governor for disturbance observer (DOB)-based load pressure control is proposed in this paper for a dual-actuator-driven electrohydraulic cylinder. First, a control-oriented model for load pressure control was developed. On the basis of this, a nonlinear DOB-based feedback controller, as well as a mid-range control architecture for the variable displacement pump and proportional valve, was fabricated so that the performance degradation caused by the pump’s slow responses and imprecise system parameters is suppressed. Specifically, this controller is augmented by a novel smooth reference governor, which modifies the load pressure command in the pressure transition periods to guarantee that the actuator’s constraints are not violated. Another merit of the novel reference governor is that it ensures a smooth trajectory transition, and therefore, unmodeled high-frequency plant dynamics will not be invoked. Case studies were carried out to verify the effectiveness of the proposed control approach. The study results show that the approach can significantly enhance the hydraulic system’s pressure tracking performance. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Mechatronics)
Show Figures

Figure 1

14 pages, 8127 KiB  
Article
Chatter Detection in Robotic Milling Using Entropy Features
Appl. Sci. 2022, 12(16), 8276; https://doi.org/10.3390/app12168276 - 18 Aug 2022
Cited by 7 | Viewed by 1449
Abstract
Chatter detection in robotic milling is a difficult issue due to the complex dynamic behavior of robots. In this paper, a novel approach to detecting chatter in the robotic milling process is proposed. The method of improved complete ensemble empirical mode decomposition with [...] Read more.
Chatter detection in robotic milling is a difficult issue due to the complex dynamic behavior of robots. In this paper, a novel approach to detecting chatter in the robotic milling process is proposed. The method of improved complete ensemble empirical mode decomposition with adaptive noise is introduced for decomposing the milling vibration signals into a series of intrinsic mode functions (IMFs). The effective IMFs are chosen according to the correlation between the original signals and each IMF. Signal reconstruction is conducted using the selected IMFs. The weighted refined composite multiscale dispersion entropy is extracted from the reconstructed signals in order to characterize the chatter states. Then, a classification model is established for chatter detection. Experimental results prove that the proposed method is feasible for chatter detection in the robotic milling process under different robot configurations and machining parameters. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Mechatronics)
Show Figures

Figure 1

16 pages, 2974 KiB  
Article
Sliding Mode Control of Electro-Hydraulic Position Servo System Based on Adaptive Reaching Law
Appl. Sci. 2022, 12(14), 6897; https://doi.org/10.3390/app12146897 - 07 Jul 2022
Cited by 6 | Viewed by 1615
Abstract
For the problem of the system state variable taking a long time to reach the sliding mode surface and the chattering frequency being high in the sliding mode surface, a sliding mode control method based on the adaptive reaching law is proposed, the [...] Read more.
For the problem of the system state variable taking a long time to reach the sliding mode surface and the chattering frequency being high in the sliding mode surface, a sliding mode control method based on the adaptive reaching law is proposed, the system state variable is introduced based on the subreaching law, and an improved variable-speed reaching law is added with reference to the characteristics of the hyperbolic tangent function. The sliding mode control method is divided into two stages, namely, the initial state to the critical value s = ±1 and the system state variable reaching the equilibrium point of the sliding mode surface, and the total time obtained is less than the sum of these two stages. Secondly, this method is adopted in the electro-hydraulic position servo system, and a sliding mode controller is established. Through an AMESim/Simulink co-simulation, it is compared with the sliding mode controller based on the traditional exponential reaching law. The results show that this method can effectively reduce the jitter of the system, reduce the time for the system to reach the sliding surface, and improve the robustness of the system. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Mechatronics)
Show Figures

Figure 1

Back to TopTop