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Actuators
Special Issues
Neural Networks-Based Modeling and Control for Uncertain Dynamical Systems
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Neural Networks-Based Modeling and Control for Uncertain Dynamical Systems

A special issue of Actuators (ISSN 2076-0825). This special issue belongs to the section "Control Systems".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2291

Special Issue Editors

Dr. Guanyu Lai

E-Mail Website
Guest Editor
School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Interests: intelligent control; adaptive visual control; high-performance actuators; robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Fang Wang

E-Mail Website
Guest Editor
College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, China
Interests: multi-agent system; adaptive control; stochastic systems; robotics
Dr. Weijun Yang

E-Mail
Guest Editor
School of Mechanical and Electrical Engineering, Guangzhou City Polytechnic, Guangzhou 510405, China
Interests: intelligent control; network system control; optimal control
Special Issues, Collections and Topics in MDPI journals
Dr. Hanzhen Xiao

E-Mail Website
Guest Editor
School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Interests: mobile robot; formation control; model predictive control; consensus control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As a powerful tool for modeling uncertain dynamical systems, the technique of neural networks has been used broadly in many important areas, e.g., aerospace, high-speed trains, navigation, numerical control machine, industrial robots, power systems, etc. Unlike traditional canonical-form nonlinear systems, however, neural networks-based nonlinear systems can have noncanonical forms, for which the existing Lyapunov-based design and analysis approaches may not be applicable any longer, and many new control problems and technical challenges need to be investigated and addressed. Furthermore, when actuators are subject to some nonlinear constraints, e.g., saturation, deadzone, backlash, and hysteresis, a complete design and analysis framework for neural networks-based systems has not yet been established. Due to the considerations above, this Special Issue aims to bring together researchers, scholars, and engineers to discuss and share their latest advancements, findings, and experiences in the field.

This Special Issue will include, but is not limited to, the following topics relevant to AFTC:

  • Neural networks;
  • Machine learning;
  • System modeling;
  • Nonlinear systems;
  • Intelligent control;
  • Model predictive control;
  • Robust adaptive control;
  • Adaptive fault-tolerant control;
  • Stability analysis;
  • Multi-agent systems;
  • Power systems;
  • Industrial robots or mobile robots.

Dr. Guanyu Lai
Dr. Fang Wang
Dr. Weijun Yang
Dr. Hanzhen Xiao
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

  • neural networks
  • actuator nonlinearities
  • robust adaptive control
  • noncanonical nonlinear systems
  • stability analysis

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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16 pages, 8919 KiB  
Article
Data-Driven Feedforward Force Control of a Single-Acting Pneumatic Cylinder with a Nonlinear Hysteresis Characteristic
by Xiaofeng Wu, Hongliang Hua, Songquan Feng, Yanli Zhao, Yuhong Yang and Zhenqiang Liao
Actuators 2025, 14(4), 162; https://doi.org/10.3390/act14040162 - 24 Mar 2025
Viewed by 118
Abstract
Pneumatic force control has a broad application background in the automation field, such as in industrial polishing, robotic grasping, and humanoid robots. Nonlinear hysteresis characteristics are one of the major factors that affect the feedforward force control performance of a pneumatic system. The [...] Read more.
Pneumatic force control has a broad application background in the automation field, such as in industrial polishing, robotic grasping, and humanoid robots. Nonlinear hysteresis characteristics are one of the major factors that affect the feedforward force control performance of a pneumatic system. The primary motivation of this paper is to develop an accurate feedforward actuating force control method for a single-acting pneumatic cylinder with a nonlinear hysteresis characteristic. A data-driven neural network modeling method is presented to achieve accurate actuating force modeling. The modeling accuracy of the neural network model under different configurations of the input layer is quantitatively analyzed to determine the essential modeling variables. The real-time execution speed of neural network models with different numbers of hidden neurons is evaluated to achieve a balance between the modeling accuracy and the real-time computing speed of the neural network model. Then, a single-acting pneumatic system is fabricated to experimentally verify the effectiveness of the proposed modeling and control method. The experimental results reveal that the actuating force can achieve ideal tracking of the target. In both the loading and the unloading process, the amplitude of the control error is less than 0.5 N. The overall RMS value of the control error is about 1 N. An instruction smoothing operation could reduce the percentage overshoot and steady-state error of the feedforward step actuating force control. Full article
(This article belongs to the Special Issue Neural Networks-Based Modeling and Control for Uncertain Dynamical Systems)
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20 pages, 20108 KiB  
Article
Physics-Informed Neural Network-Based Input Shaping for Vibration Suppression of Flexible Single-Link Robots
by Tingfeng Li and Tengfei Xiao
Actuators 2025, 14(1), 14; https://doi.org/10.3390/act14010014 - 5 Jan 2025
Viewed by 684
Abstract
The vibration suppression of flexible robotic arms is challenging due to their nonlinear spatiotemporal dynamics. This paper presents a novel physics-informed neural network (PINN)-based input-shaping method for the vibration suppression problem. Through a two-phase training process of a neural network based on a [...] Read more.
The vibration suppression of flexible robotic arms is challenging due to their nonlinear spatiotemporal dynamics. This paper presents a novel physics-informed neural network (PINN)-based input-shaping method for the vibration suppression problem. Through a two-phase training process of a neural network based on a loss function that follows both the physical model constraints and the vibration modal conditions, we identify optimal input-shaping parameters to minimize residual vibration. With the use of powerful computational resources to handle multimode information about the vibration, the PINN-based approach outperforms traditional input-shaping methods in terms of computational efficiency and performance. Extensive simulations are carried out to validate the effectiveness of the method and highlight its potential for complex control tasks in flexible robotic systems. Full article
(This article belongs to the Special Issue Neural Networks-Based Modeling and Control for Uncertain Dynamical Systems)
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17 pages, 655 KiB  
Article
Adaptive Predefined Time Control for Strict-Feedback Systems with Actuator Quantization
by Wentong Zhang and Bo Yu
Actuators 2024, 13(9), 366; https://doi.org/10.3390/act13090366 - 19 Sep 2024
Cited by 1 | Viewed by 839
Abstract
An adaptive predefined-time quantized control issue is considered for strict-feedback systems with actuator quantization. To handle the unknown nonlinearities of a system, the neural networks are first applied to model them. To analyze the predefined-time stability under approximation error, a stability lemma is [...] Read more.
An adaptive predefined-time quantized control issue is considered for strict-feedback systems with actuator quantization. To handle the unknown nonlinearities of a system, the neural networks are first applied to model them. To analyze the predefined-time stability under approximation error, a stability lemma is first introduced. Then, a refreshing predefined-time quantized control strategy is presented. Compared with the existing control studies for actuator quantization, the stability time is not influenced by the initial state and can be set in advance. Furthermore, unlike the available predefined-time control studies, a new parameter adaptive law and virtual controllers are designed. This design not only ensures the predefined-time stability, but overcomes the singularities of system in coventional backstepping control design because of repeating differentiation for virtual controllers. Full article
(This article belongs to the Special Issue Neural Networks-Based Modeling and Control for Uncertain Dynamical Systems)
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