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: 28 February 2025 | Viewed by 772
Special Issue Editors
Interests: intelligent control; adaptive visual control; high-performance actuators; robotics
Special Issues, Collections and Topics in MDPI journals
Interests: multi-agent system; adaptive control; stochastic systems; robotics
Interests: intelligent control; network system control; optimal control
Special Issues, Collections and Topics in MDPI journals
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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Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Adaptive Predefined Time Control Of Strict Feedback System With Actuator Quantization
Authors: Wang Fang, Wentong Zhang
Affiliation: Shandong University, Weihai
Abstract: An adaptive tracking control scheme is
presented for a nonlinear system with actuator quantization. In this article,
the neural networks are applied to model the unknown nonlinearity of system. To
analyze the predefined-time stability under approximation error, a stability
lemma is firstly introduced. Following this lemma, a refreshing predefined-time
quantized control strategy is proposed. 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. Different from the available
predefined-time control studies, a novel parameter adaptive law and virtual
controllers are designed. This design not only ensures the predefined-time
stability, but overcome the singularities of system in conventional
backstepping control design because of
repeating differentiations of virtual controllers. An example verifies the
availability of the control method in the end.
