Topic Editors

Prof. Dr. Hongyu Zheng
State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, China
Automotive Engineering Research Institute, Jiangsu University, Zhenjiang, China

Intelligent Mechatronics System Modeling, Control and Evaluation

Abstract submission deadline
31 December 2026
Manuscript submission deadline
31 May 2027
Viewed by
1728

Topic Information

Dear Colleagues,

With the continuous development of artificial intelligence technology, mechatronics equipment represented by intelligent vehicles, engineering machinery, robots, and unmanned systems have played an increasingly important role. This mechatronic equipment has significantly improved operational accuracy, efficiency, and safety in hazardous environments by integrating artificial intelligence, and can achieve autonomous and collaborative capabilities beyond human capabilities. However, they also commonly face challenges such as technological reliability (especially in complex scenarios), high costs, and network security. Looking ahead to the future, we should focus on developing research directions such as embodied intelligence and autonomous decision-making (enabling equipment to truly understand the physical world and adapt to it), multi-machine collaboration and cluster intelligence (achieving efficient collaboration of large-scale equipment), human–machine integration and safe interaction (ensuring natural and safe collaboration between humans and systems), and synchronously construct corresponding testing standards, safety regulations, and ethical frameworks to promote their advancement towards higher levels of intelligence and scale applications, thereby better serving social and economic development.

Potential topics include, but are not limited to, the following:

  • Design of intelligent mechatronics systems;
  • New technologies of intelligent mechatronics systems;
  • Thermal and energy management of intelligent mechatronics systems;
  • Analysis, control, and testing of intelligent mechatronics systems;
  • Case studies of successful intelligent mechatronics systems.

Prof. Dr. HongYu Zheng
Dr. Yong Li
Topic Editors

Keywords

  • mechatronics
  • intelligent vehicle
  • construction machinery
  • robot
  • unmanned system

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Actuators
actuators
2.4 4.3 2012 16.6 Days CHF 2400 Submit
Applied Sciences
applsci
2.9 6.1 2011 15 Days CHF 2400 Submit
Machines
machines
3.0 6.1 2013 15.9 Days CHF 2400 Submit

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Published Papers (2 papers)

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30 pages, 54090 KB  
Article
Research on Hierarchical Sliding Mode–Fuzzy Combined Regenerative Braking Control Strategy Optimized by Adaptive Network-Based Fuzzy Inference System (ANFIS)
by Bing Fu, Yuzi Tan, Weihao Ai, Jingang Liu and Liang Yu
Actuators 2026, 15(7), 373; https://doi.org/10.3390/act15070373 - 4 Jul 2026
Viewed by 356
Abstract
The capability of recovering a portion of braking energy during vehicle deceleration is one of the distinctive advantages of new energy vehicles (EVs) over Conventional Internal Combustion Engine Vehicles (ICEVs). In existing production vehicles, regenerative braking control is commonly implemented using rule-based lookup [...] Read more.
The capability of recovering a portion of braking energy during vehicle deceleration is one of the distinctive advantages of new energy vehicles (EVs) over Conventional Internal Combustion Engine Vehicles (ICEVs). In existing production vehicles, regenerative braking control is commonly implemented using rule-based lookup table methods. Although such approaches are simple, reliable, and easy to implement, they lack the ability to adaptively adjust the braking force allocation according to varying driving conditions, thereby limiting the potential for high efficiency energy recovery. To improve regenerative energy recovery while simultaneously maintaining braking stability, this study introduces an ANFIS-optimized Sliding Mode–Fuzzy Joint Hierarchical Control Strategy (S-FJHCS) for regenerative braking systems. In the upper control layer, an improved tire road friction coefficient estimation algorithm is integrated with a sliding mode controller to ensure consistent slip ratio regulation between the front and rear wheels. In the lower control layer, a fuzzy control algorithm is employed to coordinate the distribution of braking torque between the hydraulic braking system and the hub motors. Furthermore, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized to perform offline optimization of the fuzzy controller, enabling the adaptive adjustment of fuzzy rules and membership functions based on historical operating conditions. Simulation and experimental results demonstrate that the proposed regenerative braking control strategy can improve regenerative energy recovery efficiency by approximately 5–10% compared with a conventional rule based regenerative braking strategy, while maintaining satisfactory braking performance and vehicle stability under various driving conditions. Full article
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26 pages, 5612 KB  
Article
Dynamics Parameter Calibration for Performance Enhancement of Heavy-Duty Servo Press
by Jian Li, Shuaiyi Ma, Bingqing Liu, Tao Liu and Zhen Wang
Appl. Sci. 2026, 16(2), 847; https://doi.org/10.3390/app16020847 - 14 Jan 2026
Viewed by 603
Abstract
The accuracy of dynamics parameters in the transmission system is essential for high-performance motion trajectory planning and stable operation of heavy-duty servo presses. To mitigate the performance degradation and potential overload risks caused by deviations between theoretical and actual parameters, this paper proposes [...] Read more.
The accuracy of dynamics parameters in the transmission system is essential for high-performance motion trajectory planning and stable operation of heavy-duty servo presses. To mitigate the performance degradation and potential overload risks caused by deviations between theoretical and actual parameters, this paper proposes a dynamics model accuracy enhancement method that integrates multi-objective global sensitivity analysis and ant colony optimization-based calibration. First, a nonlinear dynamics model of the eight-bar mechanism was constructed based on Lagrange’s equations, which systematically incorporates generalized external force models consistent with actual production, including gravity, friction, balance force, and stamping process load. Subsequently, six key sensitive parameters were identified from 28 system parameters using Sobol global sensitivity analysis, with response functions defined for torque prediction accuracy, transient overload risk, thermal load, and work done. Based on the sensitivity results, a parameter calibration model was formulated to minimize torque prediction error and transient overload risk, and solved by the ant colony algorithm. Experimental validation showed that, after calibration, the root mean square error between predicted and measured torque decreased significantly from 1366.9 N·m to 277.7 N·m (a reduction of 79.7%), the peak error dropped by 72.7%, and the servo motor’s effective torque prediction error was reduced from 7.6% to 1.4%. In an automotive door panel stamping application on a 25,000 kN heavy-duty servo press, the production rate increased from 11.4 to 11.6 strokes per minute, demonstrating enhanced performance without operational safety. This study provides a theoretical foundation and an effective engineering solution for high-precision modeling and performance optimization of heavy-duty servo presses. Full article
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