Optimization Control and Fault Diagnosis of Intelligent Transportation Systems

Topic Information

Dear Colleagues,

In recent years, significant progress has been made in the optimization control and fault diagnosis of intelligent transportation systems (ITS), driving profound transformations across various transportation sectors. At the core of these advancements lies the integration of cutting-edge sensor technology, big data processing, and artificial intelligence (AI) algorithms. These technologies enable the comprehensive processing and analysis of transportation data, facilitating real-time monitoring, state diagnosis, and optimization control of transportation systems.

As a crucial component of national development strategies, intelligent transportation systems focus on leveraging modern information technologies and smart systems to achieve efficient management of transportation networks, enhance traffic safety, alleviate congestion, and improve travel experiences. ITS harnesses advanced technologies such as automation and industrial Internet to boost productivity, flexibility, and innovation.

In urban rail systems, optimization control has benefited from advanced sensor technologies and intelligent algorithms capable of real-time monitoring of operational parameters such as speed, acceleration, and track conditions. Data from these sensors, processed by sophisticated algorithms, enables precise control of acceleration, deceleration, and stopping, ensuring efficiency and safety.

For diesel engine fault diagnosis, sensor-collected data is analyzed using AI algorithms to assess performance states in real-time and detect potential faults with precision. Early fault prediction and preventive measures effectively reduce failure rates.

In the domain of ship control, high-precision sensors, control algorithms, and real-time environmental perception technologies ensure the accuracy of navigation speed and direction, even under complex and dynamic oceanic conditions.

This multidisciplinary topic focuses on various intelligent transportation tools, including urban rail vehicles, automobiles, and ships. Through the application of optimization control and fault diagnosis technologies, the safety and efficiency of the transportation industry can be significantly improved, further promoting the integration of advanced technologies into real-world scenarios. With ongoing research and technological advancements, ITS is steadily transitioning from experimental phases to practical environments, ushering us into a new era of smart transportation.

This multidisciplinary topic, Optimization Control and Fault Diagnosis of Intelligent Transportation Systems, aims to highlight the latest theoretical developments, cutting-edge research, and innovative applications in the fields of control, optimization, and scheduling within ITS. It explores technological innovations to contribute to advancements in artificial intelligence and control engineering, fostering the development of ITS toward greater intelligence and automation.

We invite submissions addressing theoretical and applied issues, including but not limited to:

• Real-time monitoring and control systems for autonomous train operations;
• Precision detection and fault diagnosis of automotive diesel engines;
• Environmental perception and multi-sensor fusion in autonomous driving;
• Safety evaluation and optimization methods for autonomous driving technologies;
• Autonomous navigation guidance and control of intelligent ships.

This multidisciplinary topic will present the latest advancements in the optimization control and fault diagnosis applications of intelligent transportation systems.

Dr. Longda Wang
Dr. Guibing Zhu
Dr. Chuanfang Xu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Algorithms algorithms	2.1	4.5	2008	17.8 Days	CHF 1800	Submit
Applied Sciences applsci	2.5	5.5	2011	19.8 Days	CHF 2400	Submit
Electronics electronics	2.6	6.1	2012	16.8 Days	CHF 2400	Submit
Machines machines	2.5	4.7	2013	16.9 Days	CHF 2400	Submit
Sensors sensors	3.5	8.2	2001	19.7 Days	CHF 2600	Submit
Vehicles vehicles	2.2	5.3	2019	22.1 Days	CHF 1600	Submit

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Published Papers (4 papers)

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All Algorithms Applied Sciences Electronics Machines Sensors Vehicles

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17 pages, 2625 KB  

Open AccessArticle

Improved Active Disturbance Rejection Speed Tracking Control for High-Speed Trains Based on SBWO Algorithm

by Chuanfang Xu, Chengyu Zhang, Mingxia Xu, Jiaqing Chen, Longda Wang and Zhaoyu Han

Algorithms 2025, 18(9), 566; https://doi.org/10.3390/a18090566 - 8 Sep 2025

Viewed by 294

Abstract

To address the problems of random noise interference, inadequate disturbance estimation and compensation, and the difficulty in controller parameter tuning in speed tracking control of high-speed trains, an improved Active Disturbance Rejection Control (ADRC) strategy combined with a Sobol-based Black Widow Optimization (SBWO) [...] Read more.

To address the problems of random noise interference, inadequate disturbance estimation and compensation, and the difficulty in controller parameter tuning in speed tracking control of high-speed trains, an improved Active Disturbance Rejection Control (ADRC) strategy combined with a Sobol-based Black Widow Optimization (SBWO) algorithm is proposed. An improved Tracking Differentiator (TD) is adopted by integrating a novel optimal control synthesis function with a phase compensator to suppress input noise and ensure a smooth transition process. A novel Extended State Observer (ESO) using a nonlinear saturation function is designed to improve the observation accuracy and decrease chattering. An enhanced Nonlinear State Error Feedback (NLSEF) law that incorporates an error integral and adaptive parameter update laws is developed to reduce steady-state error and achieve self-tuned proportional and derivative gains. A feedforward compensation term is added to provide real-time dynamic compensation for ESO estimation errors. Finally, an enhanced Black Widow Optimization (BWO) algorithm, which initializes its population with Sobol sequences to improve its global search capability, is employed for parameter optimization. The simulation results demonstrate that compared with the control methods based on Proportional–Integral–Derivative (PID) control and conventional ADRC, the proposed strategy achieves higher steady-state tracking accuracy, better adaptability to dynamic operating conditions, stronger anti-disturbance ability, and more precise stopping precision. Full article

(This article belongs to the Topic Optimization Control and Fault Diagnosis of Intelligent Transportation Systems)

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24 pages, 1074 KB  

Open AccessArticle

Research on Dual-Loop ADRC for PMSM Based on Opposition-Based Learning Hybrid Optimization Algorithm

by Longda Wang, Zhang Wu, Yang Liu and Yan Chen

Algorithms 2025, 18(9), 559; https://doi.org/10.3390/a18090559 - 4 Sep 2025

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Abstract

To enhance the speed regulation accuracy and robustness of permanent magnet synchronous motor (PMSM) drives under complex operating conditions, this paper proposes a dual-loop active disturbance rejection control strategy optimized by an opposition-based learning hybrid optimization algorithm (DLADRC-OBLHOA). First, the vector control system [...] Read more.

To enhance the speed regulation accuracy and robustness of permanent magnet synchronous motor (PMSM) drives under complex operating conditions, this paper proposes a dual-loop active disturbance rejection control strategy optimized by an opposition-based learning hybrid optimization algorithm (DLADRC-OBLHOA). First, the vector control system and ADRC model of the PMSM are established. Then, a nonlinear function, ifal, is introduced to improve the performance of the speed-loop ADRC. Meanwhile, an active disturbance rejection controller is also introduced into the current loop to suppress current disturbances. To address the challenge of tuning multiple ADRC parameters, an opposition-based learning hybrid optimization algorithm (OBLHOA) is developed. This algorithm integrates chaotic mapping for population initialization and employs opposition-based learning to enhance global search capability. The proposed OBLHOA is utilized to optimize the speed-loop ADRC parameters, thereby achieving high-precision speed control of the PMSM system. Its optimization performance is validated on 12 benchmark functions from the IEEE CEC2022 test suite, demonstrating superior convergence speed and solution accuracy compared to conventional heuristic algorithms. The proposed strategy achieves superior speed regulation accuracy and reliability under complex operating conditions when deployed on high-performance processors, but its effectiveness may diminish on resource-limited hardware. Moreover, simulation results show that the DLADRC-OBLHOA control strategy outperforms PI control, traditional ADRC, and ADRC-$ifal$ in terms of tracking accuracy and disturbance rejection capability. Full article

(This article belongs to the Topic Optimization Control and Fault Diagnosis of Intelligent Transportation Systems)

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17 pages, 1101 KB  

Open AccessArticle

Ship Scheduling Algorithm Based on Markov-Modulated Fluid Priority Queues

by Jianzhi Deng, Shuilian Lv, Yun Li, Liping Luo, Yishan Su, Xiaolin Wang and Xinzhi Liu

Algorithms 2025, 18(7), 421; https://doi.org/10.3390/a18070421 - 8 Jul 2025

Viewed by 314

Abstract

As a key node in port logistics systems, ship anchorage is often faced with congestion caused by ship flow fluctuations, multi-priority scheduling imbalances and the poor adaptability of scheduling models to complex environments. To solve the above problems, this paper constructs a ship [...] Read more.

As a key node in port logistics systems, ship anchorage is often faced with congestion caused by ship flow fluctuations, multi-priority scheduling imbalances and the poor adaptability of scheduling models to complex environments. To solve the above problems, this paper constructs a ship scheduling algorithm based on a Markov-modulated fluid priority queue, which describes the stochastic evolution of the anchorage operation state via a continuous-time Markov chain and abstracts the arrival and service processes of ships into a continuous fluid input and output mechanism modulated by the state. The algorithm introduces a multi-priority service strategy to achieve the differentiated scheduling of different types of ships and improves the computational efficiency and scalability based on a matrix analysis method. Simulation results show that the proposed model reduces the average waiting time of ships by more than 90% compared with the M/G/1/1 and RL strategies and improves the utilization of anchorage resources by about 20% through dynamic service rate adjustment, showing significant advantages over traditional scheduling methods in multi-priority scenarios. Full article

(This article belongs to the Topic Optimization Control and Fault Diagnosis of Intelligent Transportation Systems)

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22 pages, 12260 KB  

Open AccessArticle

Improved Directional Mutation Moth–Flame Optimization Algorithm via Gene Modification for Automatic Reverse Parking Trajectory Optimization

by Yan Chen, Yi Chen, Yang Guo, Longda Wang and Gang Liu

Algorithms 2025, 18(6), 299; https://doi.org/10.3390/a18060299 - 22 May 2025

Viewed by 462

Abstract

Automatic reverse parking (ARP) faces challenges in finding ideal reference trajectories that avoid collisions, maintain smoothness, and minimize path length. To address this, we propose an improved directional mutation moth–flame optimization algorithm with gene modification (IDMMFO-GM). We develop a practical reference trajectory optimization [...] Read more.

Automatic reverse parking (ARP) faces challenges in finding ideal reference trajectories that avoid collisions, maintain smoothness, and minimize path length. To address this, we propose an improved directional mutation moth–flame optimization algorithm with gene modification (IDMMFO-GM). We develop a practical reference trajectory optimization model by combining cubic spline interpolation with a standardized parking plane coordinate system. To effectively address the infeasible solutions encountered when parking in a garage, we apply gene modification for collision avoidance and berthing tilt generated from the reference trajectory optimization to enhance the preservation of optimization information. Furthermore, we introduce a non-linear decreasing weight coefficient and a directional mutation strategy into the moth–flame optimization algorithm to significantly improve its overall optimization performance. Taking the automatic parking garage space No. 155 in Dalian Shell Museum as the actual vehicle test object, which is situated within Dalian Xinghai Square, test results demonstrate that the proposed algorithm achieves an accelerated optimization speed, enhanced precision in trajectory optimization, and superior tracking control performance. Full article

(This article belongs to the Topic Optimization Control and Fault Diagnosis of Intelligent Transportation Systems)

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