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Modelling, Control and Condition Monitoring of Actuator-Based Land Transport Systems

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

Deadline for manuscript submissions: closed (30 July 2021) | Viewed by 35986

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Special Issue Editors

Dr. Hai Wang

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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. Ming Yu

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Guest Editor

School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China
Interests: fault diagnosis and prognosis of vehicle systems; hybrid system modeling; evolutionary algorithms

Prof. Dr. Zhaowu Ping

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Guest Editor

School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China
Interests: nonlinear output regulation theory and applications; electromechanical system control; neural network control; control and estimation of switched systems

Prof. Dr. Yongfu Li

E-Mail Website
Guest Editor

College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 40065, China
Interests: connected and automated vehicles; UAV swarm control; air–ground cooperative control; vehicle dynamics and control

Prof. Dr. Bin Xu

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Guest Editor

School of Automation, Northwestern Polytechnical University, Xian 710072, Shaanxi, China
Interests: fault tolerant control; intelligent control and adaptive control with application to flight dynamics

Special Issue Information

Dear colleagues,

With the rapid development of modern land transport systems (autonomous vehicles, intelligent vehicles, electric vehicles, connected vehicles, etc.), more requirements on ride comfort and handling stability are demanded. However, no matter how good the quality of vehicle is, the operating performance will be degraded over time, which eventually leads to faults/failures on components resulting in undesirable consequences. In order to improve system safety and reliability, the techniques pertaining to fault tolerant control and condition monitoring (fault diagnosis and prognosis) of key components with actuators (steering, braking, throttle, suspension, etc.) for land transport systems, which are effective means to enhance the performance of vehicle systems, have been extensively explored and studied by researchers and practitioners from both academia and industry. With this success, more application requirements, i.e., robustness, reliability, and computational efficiency for real-time implementation, are being highly demanded. These lead to several challenging issues, especially in the presence of complex traffic conditions, unknown external disturbances, and component/system degradations and faults. In the past few decades, with the development of control and condition monitoring theories, many emerging methods have the potential to further improve the performance of various onboard actuator-based vehicle systems (braking, steering, suspension, engine, clutch, differential, powertrain, etc.) by enhancing robustness and reliability. With this in mind, we are pleased to announce a Special Issue on “Modeling, Control and Condition Monitoring of Actuator-based Land Transport Systems”. The main objective of this Special Issue is to highlight the latest advancements and challenges in control and condition monitoring methods of actuators and actuator-based land transport systems as well as new applications.

This Special Issue will bring together original and high-quality articles through an international standard peer review process on the following (but nonexclusive) main topics:

Modeling, estimation, and control of actuator-based land transport systems.
Model-based/data-driven fault diagnosis and prognosis of actuator-based land transport systems.
Active/passive fault tolerant control of actuator-based land transport systems.
Model-based/data-driven fault tolerant control of actuator-based land transport systems.
Sensor placement of land transport systems for condition monitoring.
Distributed fault diagnosis and prognosis methods.
Case studies on new applications of control and condition monitoring methods.

We look forward to your valuable contributions.

Dr. Hai Wang
Prof. Dr. Ming Yu
Prof. Dr. Zhaowu Ping
Prof. Dr. Yongfu Li
Prof. Dr. Bin Xu
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
Actuators for land transport systems
Sensor placement

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Published Papers (10 papers)

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Research

14 pages, 2305 KiB

Open AccessArticle

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Deep Learning and Soft Sensing

by Zhuqing Wang, Qiqi Ma and Yangming Guo

Actuators 2021, 10(9), 234; https://doi.org/10.3390/act10090234 - 13 Sep 2021

Cited by 11 | Viewed by 3271

Abstract

The Remaining useful life (RUL) prediction is of great concern for the reliability and safety of lithium-ion batteries in electric vehicles (EVs), but the prediction precision is still unsatisfactory due to the unreliable measurement and fluctuation of data. Aiming to solve these issues, an adaptive sliding window-based gated recurrent unit neural network (GRU NN) is constructed in this paper to achieve the precise RUL prediction of LIBs with the soft sensing method. To evaluate the battery degradation performance, an indirect health indicator (HI), i.e., the constant current duration (CCD), is firstly extracted from charge voltage data, providing a reliable soft measurement of battery capacity. Then, a GRU NN with an adaptive sliding window is designed to learn the long-term dependencies and simultaneously fit the local regenerations and fluctuations. Employing the inherent memory units and gate mechanism of a GRU, the designed model can learn the long-term dependencies of HIs to the utmost with low computation cost. Furthermore, since the length of the sliding window updates timely according to the variation of HIs, the model can also capture the local tendency of HIs and address the influence of local regeneration. The effectiveness and advantages of the integrated prediction methodology are validated via experiments and comparison, and a more precise RUL prediction result is provided as well. Full article

(This article belongs to the Special Issue Modelling, Control and Condition Monitoring of Actuator-Based Land Transport Systems)

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17 pages, 17407 KiB

Open AccessArticle

Human–Machine Cooperative Control of Intelligent Vehicles for Lane Keeping—Considering Safety of the Intended Functionality

by Mingyue Yan, Wuwei Chen, Qidong Wang, Linfeng Zhao, Xiutian Liang and Bixin Cai

Actuators 2021, 10(9), 210; https://doi.org/10.3390/act10090210 - 28 Aug 2021

Cited by 10 | Viewed by 3508

Abstract

Reasonably foreseeable misuse by persons, as a primary aspect of safety of the intended functionality (SOTIF), has a significant effect on cooperation performance for lane keeping. This paper presents a novel human–machine cooperative control scheme with consideration of SOTIF issues caused by driver error. It is challenging to balance lane keeping performance and driving freedom when driver error occurs. A safety evaluation strategy is proposed for safety supervision, containing assessments of driver error and lane departure risk caused by driver error. A dynamic evaluation model of driver error is designed based on a typical driver model in the loop to deal with the uncertainty and variability of driver behavior. Additionally, an extension model is established for determining the cooperation domain. Then, an authority allocation strategy is proposed to generate a dynamic shared authority and achieve an adequate balance between lane keeping performance and driving freedom. Finally, a model predictive control (MPC)-based controller is designed for calculating optimal steering angle, and a steer-by-wheel (SBW) system is employed as an actuator. Numerical simulation tests are conducted on driver error scenarios based on the CarSim and MATLAB/Simulink software platforms. The simulation results demonstrate the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Modelling, Control and Condition Monitoring of Actuator-Based Land Transport Systems)

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18 pages, 723 KiB

Open AccessArticle

Optimized Control of Virtual Coupling at Junctions: A Cooperative Game-Based Approach

by Qi Wang, Ming Chai, Hongjie Liu and Tao Tang

Actuators 2021, 10(9), 207; https://doi.org/10.3390/act10090207 - 27 Aug 2021

Cited by 22 | Viewed by 3023

Abstract

Recently, virtual coupling has aroused increasing interest in regard to achieving flexible and on-demand train operations. However, one of the main challenges in increasing the throughput of a train network is to couple trains quickly at junctions. Pre-programmed train operation strategies cause trains to decelerate or stop at junctions. Such strategies can reduce the coupling efficiency or even cause trains to fail to reach coupled status. To fill this critical gap, this paper proposes a cooperative game model to represent train coupling at junctions and adopts the Shapley theorem to solve the formulated game. Due to the discrete and high-dimensional characteristics of the model, the optimal solution method is non-convex and is difficult to solve in a reasonable amount of time. To find optimal operation strategies for large-scale models in a reasonable amount of time, we propose an improved particle swarm optimization algorithm by introducing self-adaptive parameters and a mutation method. This paper compares the strategy for train coupling at junctions generated by the proposed method with two naive strategies and unimproved particle swarm optimization. The results show that the operation time was reduced by using the proposed cooperative game-based optimization approach. Full article

(This article belongs to the Special Issue Modelling, Control and Condition Monitoring of Actuator-Based Land Transport Systems)

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21 pages, 1330 KiB

Open AccessArticle

An AdaBoost-Based Intelligent Driving Algorithm for Heavy-Haul Trains

by Siyu Wei, Li Zhu, Lijie Chen and Qingqing Lin

Actuators 2021, 10(8), 188; https://doi.org/10.3390/act10080188 - 6 Aug 2021

Cited by 6 | Viewed by 2609

Abstract

Heavy-haul trains have the characteristics of large volume, long formation, and complex line conditions, which increase the driving difficulty of drivers and can easily cause safety problems. In order to improve the safety and efficiency of heavy-haul railways, the train control mode urgently needs to be developed towards the direction of automatic driving. In this paper, we take the Shuohuang Railway as the research background and analyze the train operation data of SS4G locomotives. We find that the proportion of operation data under different working conditions is seriously out of balance. Aiming at this unbalanced characteristic, we introduce the classification method in the field of machine learning and design an intelligent driving algorithm for heavy-haul trains. Specifically, we extract the data by random forest algorithm and compare the classification performance of C4.5 and CART algorithms. We then select the CART algorithm as the base classifier of the AdaBoost algorithm to build the model of the automatic air brake. For the purpose of heightening the precision of the model, we optimize the AdaBoost algorithm by improving the generation of training subsets and the weight of voting. The numerical results certify the effectiveness of our proposed approach. Full article

(This article belongs to the Special Issue Modelling, Control and Condition Monitoring of Actuator-Based Land Transport Systems)

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25 pages, 4813 KiB

Open AccessArticle

Motion Characteristics of a Clutch Actuator for Heavy-Duty Vehicles with Automated Mechanical Transmission

by Yunxia Li and Zengcai Wang

Actuators 2021, 10(8), 179; https://doi.org/10.3390/act10080179 - 3 Aug 2021

Cited by 4 | Viewed by 3911

Abstract

Clutch control has a great effect on the starting quality and shifting quality of heavy-duty vehicles with automated mechanical transmission (AMT). The motion characteristics of a clutch actuator for heavy-duty vehicles with AMT are studied in this paper to investigate the clutch control strategy further. The modeling principle of the automatic clutch system is analyzed, and a simulation analysis is given to prove its validity and rationality. Normalized velocity and velocity modulation percentage are proposed as evaluation parameters for the clutch actuator driven by pulse width modulation (PWM) signals. Based on an AMT test bench, the actuator motion characteristics are analyzed. Experimental results show that the range of normalized velocity and velocity modulation percentage are obtained for the clutch engagement and disengagement processes. By analyzing the experimental data, the engaging velocity and disengaging velocity of the actuator are estimated using the solenoid valves in combination. The research results provide a fundamental basis for precise controlling of the clutch and improving the smoothness of heave-duty vehicles. Full article

(This article belongs to the Special Issue Modelling, Control and Condition Monitoring of Actuator-Based Land Transport Systems)

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19 pages, 584 KiB

Open AccessArticle

An Adaptive Model Predictive Control System for Virtual Coupling in Metros

by Xiaolin Luo, Tao Tang, Hongjie Liu, Lei Zhang and Kaicheng Li

Actuators 2021, 10(8), 178; https://doi.org/10.3390/act10080178 - 1 Aug 2021

Cited by 29 | Viewed by 3431

Abstract

Virtual coupling (VC) is an emerging concept and hot research topic in railways, especially for metro systems. Several unit trains in VC drive with a desired minimum distance, and they, as a whole, are regarded as a single train. In this work, a distributed adaptive model predictive control (AMPC) system is proposed to coordinate the driving of each unit train in VC. To obtain the accurate parameters of train dynamics model in a time varying environment, an estimator of the train dynamics model is designed for each AMPC controller. A variable step descent algorithm along the negative gradient direction is adopted for each estimator, which steers the estimated values of the parameters to real ones. Simulations are conducted and the results are compared with both nominal model predictive control system and AMPC system with fixed steps in the literature. Our proposed AMPC system with variable step (AMPCVS) has better performances than other two systems. Results indicate that there is an improvement of the proposed AMPC system with variable steps system when compared with other two existed systems. A running process of VC in a whole inter-station is also simulated here. Experimental results show that the trains track the desired objective well. Full article

(This article belongs to the Special Issue Modelling, Control and Condition Monitoring of Actuator-Based Land Transport Systems)

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12 pages, 2533 KiB

Open AccessCommunication

Dynamic Analysis of Intermittent-Motion Conveyor Actuator

by Alexander Prikhodko

Actuators 2021, 10(8), 174; https://doi.org/10.3390/act10080174 - 24 Jul 2021

Cited by 5 | Viewed by 3389

Abstract

Conveyors are one of the important components of transport systems and are used in almost all branches of mechanical engineering. This paper investigates the dynamics of the intermittent motion conveyor mechanical system. The mechanical transmission is a planetary mechanism with elliptical gears, in which the intermittent motion of the output shaft is provided by a variable gear ratio of non-circular gears. A single-mass dynamic model is built by reducing the masses, forces and moments to the initial link, which is the input shaft of the mechanism. The solutions of the equations of initial link motion were obtained using two methods, the energy-mass method and the third-order Hermite method. Dynamic studies by the energy-mass method made it possible to determine flywheel moment of inertia to reduce the coefficient of initial link rotation irregularity. The convergence of the functions of the initial link angular velocity obtained by both methods was confirmed. The results can be used for further force analysis, strength calculations, design and manufacture of the conveyor. Full article

(This article belongs to the Special Issue Modelling, Control and Condition Monitoring of Actuator-Based Land Transport Systems)

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16 pages, 2219 KiB

Open AccessArticle

Lateral Stability Control of Four-Wheel-Drive Electric Vehicle Based on Coordinated Control of Torque Distribution and ESP Differential Braking

by Liqing Chen, Zhiqiang Li, Juanjuan Yang and Yu Song

Actuators 2021, 10(6), 135; https://doi.org/10.3390/act10060135 - 18 Jun 2021

Cited by 28 | Viewed by 4591

Abstract

This research focuses on four-wheel-drive electric vehicles. On the basis of the hierarchical coordinated control strategy, the coordinated control system of driving force distribution regulation and differential braking regulation was designed to increase the electric vehicles steering stability under special road working conditions. A seven-degree-of-freedom model of an electric vehicle was established in MATLAB/Simulink, and then a hierarchical coordination control model of the Electronic stability program and dynamic torque distribution control system was established. Adaptive fuzzy control was applied to ESP and, based on the neural network PID control, a torque distribution control system was designed. On the basis of the proposed coordinated control model, a performance simulation and a hardware-in-the-loop test of the control system under the typical working condition of single line shift were carried out. From the final results, it can be seen that the proposed control strategy can greatly improve the safety of the vehicle after serious side slip, increase the stability of the whole vehicle, and effectively increase the vehicle lateral stability. Full article

(This article belongs to the Special Issue Modelling, Control and Condition Monitoring of Actuator-Based Land Transport Systems)

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21 pages, 6949 KiB

Open AccessArticle

An Intelligent Actuator of an Indoor Logistics System Based on Multi-Sensor Fusion

by Pangwei Wang, Yunfeng Wang, Xu Wang, Ying Liu and Juan Zhang

Actuators 2021, 10(6), 120; https://doi.org/10.3390/act10060120 - 4 Jun 2021

Cited by 6 | Viewed by 3345

Abstract

Integration technologies of artificial intelligence (AI) and autonomous vehicles play important roles in intelligent transportation systems (ITS). In order to achieve better logistics distribution efficiency, this paper proposes an intelligent actuator of an indoor logistics system by fusing multiple involved sensors. Firstly, an actuator based on a four-wheel differential chassis is equipped with sensors, including an RGB camera, a lidar and an indoor inertial navigation system, by which autonomous driving can be realized. Secondly, cross-floor positioning can be realized by multi-node simultaneous localization and mappings (SLAM) based on the Cartographer algorithm Thirdly the actuator can communicate with elevators and take the elevator to the designated delivery floor. Finally, a novel indoor route planning strategy is designed based on an A* algorithm and genetic algorithm (GA) and an actual building is tested as a scenario. The experimental results have shown that the actuator can model the indoor mapping and develop the optimal route effectively. At the same time, the actuator displays its superiority in detecting the dynamic obstacles and actively avoiding the collision in the indoor scenario. Through communicating with indoor elevators, the final delivery task can be completed accurately by autonomous driving. Full article

(This article belongs to the Special Issue Modelling, Control and Condition Monitoring of Actuator-Based Land Transport Systems)

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19 pages, 842 KiB

Open AccessArticle

A Representation Generation Approach of Transmission Gear Based on Conditional Generative Adversarial Network

by Jie Li, Boyu Zhao, Kai Wu, Zhicheng Dong, Xuerui Zhang and Zhihao Zheng

Actuators 2021, 10(5), 86; https://doi.org/10.3390/act10050086 - 23 Apr 2021

Cited by 2 | Viewed by 2768

Abstract

Gear reliability assessment of vehicle transmission has been a challenging issue of determining vehicle safety in the transmission industry due to a significant amount of classification errors with high-coupling gear parameters and insufficient high-density data. In terms of the preprocessing of gear reliability assessment, this paper presents a representation generation approach based on generative adversarial networks (GAN) to advance the performance of reliability evaluation as a classification problem. First, with no need for complex modeling and massive calculations, a conditional generative adversarial net (CGAN) based model is established to generate gear representations through discovering inherent mapping between features with gear parameters and gear reliability. Instead of producing intact samples like other GAN techniques, the CGAN based model is designed to learn features of gear data. In this model, to raise the diversity of produced features, a mini-batch strategy of randomly sampling from the combination of raw and generated representations is used in the discriminator, instead of using all of the data features. Second, in order to overcome the unlabeled ability of CGAN, a Wasserstein labeling (WL) scheme is proposed to tag the created representations from our model for classification. Lastly, original and produced representations are fused to train classifiers. Experiments on real-world gear data from the industry indicate that the proposed approach outperforms other techniques on operational metrics. Full article

(This article belongs to the Special Issue Modelling, Control and Condition Monitoring of Actuator-Based Land Transport Systems)

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