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Actuators
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Advancing Actuators-Based Land Transport Systems: State of the Art and...
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Advancing Actuators-Based Land Transport Systems: State of the Art and New Technologies

A special issue of Actuators (ISSN 2076-0825). This special issue belongs to the section "Actuators for Land Transport".

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 10557

Special Issue Editors

Dr. Hai Wang

E-Mail Website
Guest Editor
College of Science, Health, Engineering & Education, Murdoch University, Murdoch, WA 6150, Australia
Interests: sliding mode control and observation; robotics and mechatronics; autonomous vehicles and systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vladimir V. Vantsevich

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Vehicle and Robotics Engineering Laboratory, The University of Alabama at Birmingham, 1720 University Blvd, Birmingham, AL 35294, USA
Interests: manned and unmanned land vehicles; mobility, maneuver and energy efficiency; mechanical and electrical driveline systems and actuation; modeling, control and design
Prof. Dr. Giuseppe Carbone

E-Mail Website
Guest Editor
Department of Mechanical, Energy and Management Engineering, Università della Calabria, 87036 Rende, Italy
Interests: robotics; robot design; mechatronics; walking hexapod; design procedure; mechanics of machinery; leg–wheel
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As the demands of modern land transport systems (autonomous vehicles, intelligent vehicles, electric vehicles, connected vehicles, etc.) have increased in the past few years, intensive studies and developments have been achieved among academic researchers and engineers. Involving the emerging technologies like advanced control, sensing techniques, signal processing, artificial intelligence (AI), etc., the modern transport systems are developed to meet the demands for more efficiency and safe mobility, high intelligence, enhanced driving safety and road safety, reduced emissions, high transportation efficiency, etc.  

 Aiming at widely spreading the latest research in the field, we are pleased to announce a Special Issue “Advancing Actuators-based Land Transport Systems: State of the Art and New Technologies”. This Special Issue will bring together original and high-quality articles through an international standard peer-review process with the following main topics (not an exhaustive list):

  • Modeling, estimation, and control of actuator-based land transport systems.
  • Fault diagnosis and prognosis of actuator-based land transport systems.
  • Fault tolerant control of actuator-based land transport systems.
  • Classical chassis and modern by-wire systems in intelligent vehicles of actuator-based land transport systems.
  • Sensing, interpreting, and decision makings of connected and autonomous vehicles in land transport systems.
  • Navigation, guidance, and control of autonomous vehicles in land transport systems.
  • AI based modelling, optimization, estimation and control technologies for actuator-based land transport systems.
  • Tests and evaluation on actuator-based land transport systems.

Dr. Hai Wang
Prof. Dr. Vladimir Vantsevich
Prof. Dr. Giuseppe Carbone
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. Actuators 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.

Keywords

  • modeling, control, and estimation
  • fault diagnosis and prognosis
  • fault tolerant control
  • classical chassis and modern by-wire systems
  • sensing, interpreting, and decision makings
  • navigation, guidance, and control
  • artificial intelligence

Published Papers (4 papers)

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Research

14 pages, 2157 KiB  
Article
Toward the Trajectory Predictor for Automatic Train Operation System Using CNN–LSTM Network
by Yijuan He, Jidong Lv, Hongjie Liu and Tao Tang
Actuators 2022, 11(9), 247; https://doi.org/10.3390/act11090247 - 31 Aug 2022
Cited by 6 | Viewed by 1837
Abstract
The accurate trajectory of the train ahead with more dynamic behaviour, such as train position, speed, acceleration, etc., is the critical issue of virtual coupling for future railways, which can drastically reduce their headways and increase line capacity. This paper presents an integrated [...] Read more.
The accurate trajectory of the train ahead with more dynamic behaviour, such as train position, speed, acceleration, etc., is the critical issue of virtual coupling for future railways, which can drastically reduce their headways and increase line capacity. This paper presents an integrated convolutional neural network (CNN) and long short-term memory (LSTM) hybrid model for the task of trajectory prediction. A CNN–LSTM hybrid algorithm has been proposed. The model employs CNN and LSTM to extract the spatial dimension feature of the trajectory and the long-term dependencies of train trajectory data, respectively. The proposed CNN–LSTM model has superiority in achieving collaborative data mining on spatiotemporal measurement data to simultaneously learn spatial and temporal features from phasor measurement unit data. Therefore, the high-precision prediction of the train trajectory prediction is achieved based on the sufficient fusion of the above features. We use real automatic train operation (ATO) collected data for experiments and compare the proposed method with recurrent neural networks (RNN), recurrent neural networks (GRU), LSTM, and stateful-LSTM models on the same data sets. Experimental results show that the prediction performance of long-term trajectories is satisfyingly accurate. The root mean square error (RMSE) error can be reduced to less than 0.21 m, and the hit rate achieves 93% when the time horizon increases to 4S, respectively. Full article
(This article belongs to the Special Issue Advancing Actuators-Based Land Transport Systems: State of the Art and New Technologies)
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14 pages, 2777 KiB  
Article
Communication-Based Train Control with Dynamic Headway Based on Trajectory Prediction
by Yijuan He, Jidong Lv and Tao Tang
Actuators 2022, 11(8), 237; https://doi.org/10.3390/act11080237 - 18 Aug 2022
Cited by 3 | Viewed by 1635
Abstract
Rail transit plays a significant role in the operation of an efficient and effective urban public transportation system. Safety and capacity are some of the most crucial objectives in railway operations. The communication-based train control (CBTC) system is a continuous and automatic train [...] Read more.
Rail transit plays a significant role in the operation of an efficient and effective urban public transportation system. Safety and capacity are some of the most crucial objectives in railway operations. The communication-based train control (CBTC) system is a continuous and automatic train control system that realizes constant and high-capacity train ground two-way communication. In this study, a dynamic headway model of the ‘softwall’ moving-block approach is proposed for CBTC to increase the track capacity and improve dispatching efficiency based on the train trajectory prediction. For this precise trajectory prediction task, we introduce a hybrid trajectory prediction model to combine Long Short-term memory (LSTM) and Kalman Filter (KF) to extract the train’s local data features and learn the long-term dependencies, respectively. Then we present a dynamic headway model to maximize the train headway and reduce the track distance. The leading trains’ information is used to construct the iterative learning control strategy, and the predicted trajectory is input into the algorithm of the headway model. We use a simulation model of the rail network in Chengdu to demonstrate the effectiveness of our proposed approach. The results show the Mean Absolute Error (MAE) of the predicted trajectory retreated to 93.97 cm and reductions in operation headway of at least 64.33% under the dynamic headway model versus the traditional moving-block model. Full article
(This article belongs to the Special Issue Advancing Actuators-Based Land Transport Systems: State of the Art and New Technologies)
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16 pages, 1476 KiB  
Article
Analytical Derivation and Analysis of Vertical and Lateral Installation Ratios for Swing Axle, McPherson and Double Wishbone Suspension Architectures
by Francesco Bucchi and Basilio Lenzo
Actuators 2022, 11(8), 229; https://doi.org/10.3390/act11080229 - 09 Aug 2022
Cited by 1 | Viewed by 3585
Abstract
In the context of suspension design, the installation ratio (or motion ratio) is a parameter that relates wheel movement with spring deflection, quite an important kinematic property of a suspension. Yet, no study in the literature provides a clear relationship between the installation [...] Read more.
In the context of suspension design, the installation ratio (or motion ratio) is a parameter that relates wheel movement with spring deflection, quite an important kinematic property of a suspension. Yet, no study in the literature provides a clear relationship between the installation ratio and the geometrical features of a suspension. This paper employs rigid body kinematics and appropriate geometrical schematics to fill such a gap. Analytical expressions of the installation ratio are derived for three suspension layouts: swing axle, McPherson, double wishbone. Key concepts such as instant center, roll center and camber gain are harnessed to provide insightful analyses for relevant case studies of suspension passenger cars. Among the key results, the typical assumption of a McPherson installation ratio close to 1 is supported by a formal demonstration, and the new concept of “lateral” installation ratio is introduced which, alongside the classical “vertical” installation ratio, further characterizes suspension motion. Numerical results obtained through a multibody software support the findings of this paper. In conclusion, this study provides valuable insights for suspension design engineers. Full article
(This article belongs to the Special Issue Advancing Actuators-Based Land Transport Systems: State of the Art and New Technologies)
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22 pages, 3206 KiB  
Article
Local Path Planning of the Autonomous Vehicle Based on Adaptive Improved RRT Algorithm in Certain Lane Environments
by Xiao Zhang, Tong Zhu, Yu Xu, Haoxue Liu and Fei Liu
Actuators 2022, 11(4), 109; https://doi.org/10.3390/act11040109 - 15 Apr 2022
Cited by 8 | Viewed by 2678
Abstract
Given that the rapidly exploring random tree algorithm (RRT) and its variants cannot efficiently solve problems of path planning of autonomous vehicles, this paper proposes a new, adaptive improved RRT algorithm. Firstly, an adaptive directional sampling strategy is introduced to avoid excessive search [...] Read more.
Given that the rapidly exploring random tree algorithm (RRT) and its variants cannot efficiently solve problems of path planning of autonomous vehicles, this paper proposes a new, adaptive improved RRT algorithm. Firstly, an adaptive directional sampling strategy is introduced to avoid excessive search by reducing the randomness of sampling points. Secondly, a reasonable node selection strategy is used to improve the smoothness of the path by utilizing a comprehensive criterion that combines angle and distance. Thirdly, an adaptive node expansion strategy is utilized to avoid invalid expansion and make the generated path more reasonable. Finally, the expanded ellipse is used to realize vehicle obstacle avoidance in advance, and the post-processing strategy removes redundant line segments of the initial path to improve its quality. The simulation results show that the quality of the planned path is significantly improved. This path followed successfully has good trajectory stability, which shows the proposed algorithm’s effectiveness and practicability in autonomous vehicles’ local path planning. Full article
(This article belongs to the Special Issue Advancing Actuators-Based Land Transport Systems: State of the Art and New Technologies)
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