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Applications of Intelligent Control Methods in Mechatronic Systems Ⅱ

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

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 8724

Special Issue Editor


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Guest Editor
Department of Electrical Engineering, National Taiwan Normal University, Taipei, Taiwan
Interests: intelligent control; artificial intelligence; mechatronics; vehicle power management and control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the great success of the “Applications of Intelligent Control Methods in Mechatronic Systems” Special Issue, which was published in December 2020, we have decided to launch a second edition, which will hopefully be as successful and provide as much insight as the first.

Mechatronics is an engineering discipline integrating the fields of mechanics, electronics, control, and computer science. Many modern systems and products, such as robots, manipulators, autonomous vehicles, electronic instruments, manufacturing equipment, and energy systems, are designed and constructed by using mechatronic systems. The main characteristic of these systems is the progressively tighter coupling of mechanisms and an increasing number of electrical or electronic components with software. The arrangements of these components and software ensure their functions, specifically in terms of their reliability, stability, and performance. From the point of view of dynamics, mechatronic systems can be characterized by model uncertainties, high nonlinearities, complicated couplings, and stringent performance requirements, among other things. To deal with the abovementioned facts, strong research activity is still ongoing that aims to design efficient controllers that can guarantee high performance control despite the presence of system uncertainties and external disturbances. Among the various control strategies, intelligent control (IC) methods with artificial intelligence have been widely studied and developed for mechatronic systems in terms of direct control, parameter optimization, system identification, uncertainty estimation, and compensation. With the help of IC methods, it is possible to achieve better accuracy, robustness, reliability, and implementation simplicity. In addition, the learning algorithms can adapt the control parameters to ensure stability and efficiency for mechatronic systems in diverse applications under adverse conditions.

Hence, the objective of this Special Issue of the Applied Sciences journal is to provide a forum for the presentation of new and recent developments in IC methods as applied to mechatronics systems. This Special Issue will consider high-quality research and review papers that deal with theoretical and application aspects of IC methods in mechatronic systems. Specific topics of interest for this Special Issue include, but are not limited to, the following or related topics:

  • Mechanical and mechatronic systems;
  • Robotics and automation systems;
  • Industry and manufacturing applications;
  • Transportation and energy systems;
  • Intelligent systems design and control;
  • Neural- and fuzzy-based control systems;
  • Machine learning in control applications;
  • Knowledge-based control systems;
  • Information-based models for control;
  • Data-driven control and applications;
  • Artificial intelligence and soft computing;
  • Evolutionary, echanism-based control;
  • Biologically inspired algorithms in control;
  • Hierarchical intelligent control systems;
  • Hybrid learning and control techniques;
  • Reinforcement learning for control;
  • Adaptive signal processing and control;
  • Observation and approximation techniques;
  • Uncertainty estimation and compensation;
  • Systems modelling and simulation;
  • Intelligent surveillance, fault detection, and diagnosis;
  • Real-time and hardware-in-the-loop simulation.

Prof. Dr. Syuan-Yi Chen
Guest Editor

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.

Published Papers (3 papers)

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Research

18 pages, 3362 KiB  
Article
An Optimal Sizing Design Approach of Hybrid Energy Sources for Various Electric Vehicles
by Syuan-Yi Chen, Chu-Yang Chiu, Yi-Hsuan Hung, Kuo-Kuang Jen, Gwo-Huei You and Po-Lin Shih
Appl. Sci. 2022, 12(6), 2961; https://doi.org/10.3390/app12062961 - 14 Mar 2022
Cited by 3 | Viewed by 1919
Abstract
In this paper, we present a discussion about green energy sources that have been widely utilized in electric vehicles (EVs). To achieve different requirements of various EVs, the correct sizing of energy sources is crucial so that the cost and output performance will [...] Read more.
In this paper, we present a discussion about green energy sources that have been widely utilized in electric vehicles (EVs). To achieve different requirements of various EVs, the correct sizing of energy sources is crucial so that the cost and output performance will be optimized. In this research, three energy sources, supercapacitors (SCs), lithium titanate oxide (LTO) batteries, and Nickel Manganese Cobalt (NCM) (or Li3) batteries, were considered for hybridization. An effective global search algorithm (GSA) was designed for optimal sizing of hybrid electric energy systems (HEESs). The GSA procedures were: (1) vehicle specification and performance requirements of energy sources, (2) determination of cost function and constraints, (3) GSA optimization with for-loops, (4) optimal results. Five examples of EVs, the electric sedan, long-distance electric bus, short-distance electric bus, electric forklift, and electric sports car, were analyzed for optimal hybrid energy combination under different criteria and specifications. The GSA effectively optimized the designs of energy sizing. The performance indices and vehicle requirements studied were the specific price, specific energy at a constant volume, specific energy at a constant mass, and specific power at a constant mass for three energy sources, SCs, LTO batteries, and Li batteries. The vehicle requirements including the maximum output power, vehicle acceleration, climbability, and maximum speed have been formulated as the design constraints. A numerical analysis of five types of EVs was analyzed for optimal sizing of the HEES and the optimal position of the DC/DC converter with the lowest cost function. The integrated system and control designs of the HESS using the GSA, more applications for green energy sources, and different types of EVs will be studied in the future. Full article
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26 pages, 12978 KiB  
Article
Intelligent Robotic Palletizer System
by Jeng-Dao Lee, Chen-Huan Chang, En-Shuo Cheng, Chia-Chen Kuo and Chia-Ying Hsieh
Appl. Sci. 2021, 11(24), 12159; https://doi.org/10.3390/app112412159 - 20 Dec 2021
Cited by 3 | Viewed by 4638
Abstract
In the global wave of automation, logistics and manufacturing are indispensable and important industries. Among them, the related automatic warehousing system is even more urgently needed. There are quite a few cases of using robotic arms in the current industry cargo stacking operations. [...] Read more.
In the global wave of automation, logistics and manufacturing are indispensable and important industries. Among them, the related automatic warehousing system is even more urgently needed. There are quite a few cases of using robotic arms in the current industry cargo stacking operations. Traditional operations require engineers to plan the stacking path for the robotic arm. If the size of the object changes, it will take extra time to re-plan the work path. Therefore, in recent years, quite a lot of automatic palletizing software has been developed; however, none of it has a detection mechanism for stacking correctness and personnel safety. As a result, in this research, an intelligent robotic palletizer system is developed based on a self-developed symmetrical algorithm to stack the goods in a staggered arrangement to ensure the overall structure. Innovatively, it is proposed to check the arrangement status and warnings during the visual stack inspection to ensure the correctness of the stacking process. Besides, an AI algorithm is imported to ensure that personnel cannot enter the set dangerous area during the work of the robotic arm to improve safety during stacking. In addition to uploading the relevant data to the cloud database in real time, the stacking process combined database and vision system also provide users with real-time monitoring of system information. Full article
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18 pages, 4831 KiB  
Article
A Real-Time Simulator for an Innovative Hybrid Thermal Management System Based on Experimental Verification
by Yu-hsuan Lin, Li-fan Liu, Yi-hsuan Hung and Chun-hsin Chang
Appl. Sci. 2021, 11(24), 11729; https://doi.org/10.3390/app112411729 - 10 Dec 2021
Cited by 1 | Viewed by 1342
Abstract
The performance and efficiency of green energy sources in electric vehicles (EVs) are significantly affected by operation temperatures. To maintain the optimal temperatures of a hybrid energy system (HES), an innovative hybrid thermal management system (IHTMS) was designed. The IHTMS contains a coolant [...] Read more.
The performance and efficiency of green energy sources in electric vehicles (EVs) are significantly affected by operation temperatures. To maintain the optimal temperatures of a hybrid energy system (HES), an innovative hybrid thermal management system (IHTMS) was designed. The IHTMS contains a coolant pump, a heat exchanger, a proportional valve for hybrid flow rates, five coolant pipes, and three electromagnetic valves to form two mode-switch coolant loops. A Matlab/Simulink-based simulator of the IHTMS was constructed by formulating a set of first-ordered dynamics of temperatures of coolant pipes and energy bodies using the theories of Newton’s law of cooling and the lumped-parameter technique. Parameters were majorly derived by measured performance maps and data from the experimental platform of the IHTMS. To properly manage the optimal temperatures, four control modes were designed for inner-loop form and outer-loop form. For the experimental platform to verify the simulator, two power supplies generated the waste heat of dual energy sources calculated by the driving cycle and vehicle dynamics. Simulation results show that the temperatures were controlled at their optimal ranges by proper mode/loop switch. With the inner-loop mechanism, the rise time of optimal temperature decreased 27.4%. The average simulation-experiment temperature error of the battery was 0.898 °C; the average simulation-experiment temperature error of the PEMFC was 4.839 °C. The IHTMS will be integrated to a real HES in the future. Full article
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