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Intelligent Modeling and Control

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (23 April 2024) | Viewed by 4447

Special Issue Editor

College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Interests: modeling, control and optimization of intelligent system; networked control systems

Special Issue Information

Dear Colleagues,

System identification, modeling and control algorithms have always been the core issues of control theory. Creating and studying the mathematical model through the representation data of systems is the basis for recognizing, regulating and controlling systems. However, with the improvement of the requirements for system cognition, interpretability and modeling accuracy, the traditional system modeling technologies have certain limitations and defects, which created greater difficulties and challenges for system control. In recent years, due to the progress of science and technology and the development of artificial intelligence, system modeling and control has ushered in a new opportunity. More and more intelligent technologies including machine learning, fuzzy logic system, neural networks, evolutionary algorithms, reinforcement learning, deep learning and so on are being applied to system modeling and system control. Although these intelligent technologies have developed rapidly, there are still some new technology and application problems that needed to be solved, such as the compromise in the cost and accuracy of system modeling, the effectiveness and efficiency of control algorithms, etc. Therefore, innovative intelligent algorithms, advanced system modeling strategies and practical control methods are urgently needed.

Entropy and information-theoretic concepts also have strong relevance to intelligent modeling and control. To be specific, in intelligent modeling and control, entropy can be used to describe the complexity and randomness of the system. For example, in machine learning, entropy can be used to calculate the information entropy of data sets, thus helping select the optimal segmentation point or decision tree. In addition, in cybernetics, entropy can be used to describe the stability and controllability of the system, which helps design effective controllers. Furthermore, entropy in information theory can also be used to build machine learning models. For example, in natural language processing, entropy can be used to evaluate the complexity and prediction ability of language models. In deep learning, the cross-entropy loss function is often used to measure the prediction accuracy of the model. Therefore, it is a popular research direction to apply entropy or information-theoretic concepts with intelligent technology to system modeling and control.

The main objective of this Special Issue is, through scientific researchers and technical engineers, to introduce the latest research in the field on intelligent modeling and control, including multi-agent modeling and control, intelligent modeling and control of complex systems, new insights into intelligent modeling and control, human–computer cooperation, advanced control strategies, the application of entropy or information-theoretic concepts in intelligent modeling and control, etc. Furthermore, intelligent solutions for complex engineering and future research prospects will also be included.

Authors are encouraged to submit their original contributions regarding intelligent modeling and control or information-theoretic concepts in modeling and control. Potential topics include but are not limited to the following:

  • Intelligent modeling technology (e.g., entropy-based methods including Shannon entropy, K–L Divergence, minimizing of relative entropy and so on, fuzzy logic systems, neural network systems and evolutionary learning systems, information-related methods, etc.)
  • Intelligent control algorithms applied to practical systems (e.g., fuzzy control, neural network control, reinforcement learning control, adaptive control, sliding mode control, optimal control, fractional order control, etc.)
  • Muti-agent intelligent modeling and control technology (e.g., machine learning, deep learning, natural language processing, biological recognition, computer vision, modeling strategies and optimization decisions, etc.)
  • Complex system modeling and control (e.g., self-organization, chaos and nonlinear dynamics, simplicity and complexity, networks, symmetry breaking, similarity, etc.)
  • Intelligent technology in related fields (such as prediction, robots, physics, computing, information, biology, materials, energy, environment, food, pharmaceuticals and manufacturing, etc.)
  • Experimental examples of the intelligent modeling and control technology are also encouraged to submit to this Special Issue.

Dr. Tao Zhao
Guest Editor

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. Entropy 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 2600 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.

Published Papers (4 papers)

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Research

19 pages, 5625 KiB  
Article
Optimal Robust Control of Nonlinear Systems with Unknown Dynamics via NN Learning with Relaxed Excitation
by Rui Luo, Zhinan Peng and Jiangping Hu
Entropy 2024, 26(1), 72; https://doi.org/10.3390/e26010072 - 14 Jan 2024
Viewed by 754
Abstract
This paper presents an adaptive learning structure based on neural networks (NNs) to solve the optimal robust control problem for nonlinear continuous-time systems with unknown dynamics and disturbances. First, a system identifier is introduced to approximate the unknown system matrices and disturbances with [...] Read more.
This paper presents an adaptive learning structure based on neural networks (NNs) to solve the optimal robust control problem for nonlinear continuous-time systems with unknown dynamics and disturbances. First, a system identifier is introduced to approximate the unknown system matrices and disturbances with the help of NNs and parameter estimation techniques. To obtain the optimal solution of the optimal robust control problem, a critic learning control structure is proposed to compute the approximate controller. Unlike existing identifier-critic NNs learning control methods, novel adaptive tuning laws based on Kreisselmeier’s regressor extension and mixing technique are designed to estimate the unknown parameters of the two NNs under relaxed persistence of excitation conditions. Furthermore, theoretical analysis is also given to prove the significant relaxation of the proposed convergence conditions. Finally, effectiveness of the proposed learning approach is demonstrated via a simulation study. Full article
(This article belongs to the Special Issue Intelligent Modeling and Control)
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23 pages, 1251 KiB  
Article
Neural Adaptive H Sliding-Mode Control for Uncertain Nonlinear Systems with Disturbances Using Adaptive Dynamic Programming
by Yuzhu Huang and Zhaoyan Zhang
Entropy 2023, 25(12), 1570; https://doi.org/10.3390/e25121570 - 22 Nov 2023
Viewed by 716
Abstract
This paper focuses on a neural adaptive H sliding-mode control scheme for a class of uncertain nonlinear systems subject to external disturbances by the aid of adaptive dynamic programming (ADP). First, by combining the neural network (NN) approximation method with a nonlinear [...] Read more.
This paper focuses on a neural adaptive H sliding-mode control scheme for a class of uncertain nonlinear systems subject to external disturbances by the aid of adaptive dynamic programming (ADP). First, by combining the neural network (NN) approximation method with a nonlinear disturbance observer, an enhanced observer framework is developed for estimating the system uncertainties and observing the external disturbances simultaneously. Then, based on the reliable estimations provided by the enhanced observer, an adaptive sliding-mode controller is meticulously designed, which can effectively counteract the effects of the system uncertainties and the separated matched disturbances, even in the absence of prior knowledge regarding their upper bounds. While the remaining unmatched disturbances are attenuated by means of H control performance on the sliding surface. Moreover, a single critic network-based ADP algorithm is employed to learn the cost function related to the Hamilton–Jacobi–Isaacs equation, and thus, the H optimal control is obtained. An updated law for the critic NN is proposed not only to make the Nash equilibrium achieved, but also to stabilize the sliding-mode dynamics without the need for an initial stabilizing control. In addition, we analyze the uniform ultimate boundedness stability of the resultant closed-loop system via Lyapunov’s method. Finally, the effectiveness of the proposed scheme is verified through simulations of a single-link robot arm and a power system. Full article
(This article belongs to the Special Issue Intelligent Modeling and Control)
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20 pages, 3009 KiB  
Article
Periodic Intermittent Adaptive Control with Saturation for Pinning Quasi-Consensus of Heterogeneous Multi-Agent Systems with External Disturbances
by Bin Du, Quan Xu, Junfu Zhang, Yi Tang, Lei Wang, Ruihao Yuan, Yu Yuan and Jiaju An
Entropy 2023, 25(9), 1266; https://doi.org/10.3390/e25091266 - 27 Aug 2023
Cited by 1 | Viewed by 987
Abstract
A periodic intermittent adaptive control method with saturation is proposed to pin the quasi-consensus of nonlinear heterogeneous multi-agent systems with external disturbances in this paper. A new periodic intermittent adaptive control protocol with saturation is designed to control the internal coupling between the [...] Read more.
A periodic intermittent adaptive control method with saturation is proposed to pin the quasi-consensus of nonlinear heterogeneous multi-agent systems with external disturbances in this paper. A new periodic intermittent adaptive control protocol with saturation is designed to control the internal coupling between the follower agents and the feedback gain between the leader and the follower. In particular, we use the saturation adaptive law: when the quasi-consensus error converges to a certain range, the adaptive coupling edge weight and the adaptive feedback gain will not be updated. Furthermore, we propose three saturated adaptive pinning control protocols. The quasi-consensus is achieved through its own pinning as long as the agents remain connected to each other. Using the Lyapunov function method and inequality technique, the convergence range of the quasi-consensus error of a heterogeneous multi-agent system is obtained. Finally, the rationality of the proposed control protocol is verified through numerical simulation. Theoretical derivation and simulation results show that the novel proposed periodic intermittent adaptive control method with saturation can successfully be used to achieve the pinning of quasi-consensus of nonlinear heterogeneous multi-agent systems. Full article
(This article belongs to the Special Issue Intelligent Modeling and Control)
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23 pages, 4172 KiB  
Article
Self-Organizing Interval Type-2 Fuzzy Neural Network Compensation Control Based on Real-Time Data Information Entropy and Its Application in n-DOF Manipulator
by Youbo Sun, Tao Zhao and Nian Liu
Entropy 2023, 25(5), 789; https://doi.org/10.3390/e25050789 - 12 May 2023
Cited by 1 | Viewed by 1173
Abstract
In order to solve the high-precision motion control problem of the n-degree-of-freedom (n-DOF) manipulator driven by large amount of real-time data, a motion control algorithm based on self-organizing interval type-2 fuzzy neural network error compensation (SOT2-FNNEC) is proposed. The proposed control framework can [...] Read more.
In order to solve the high-precision motion control problem of the n-degree-of-freedom (n-DOF) manipulator driven by large amount of real-time data, a motion control algorithm based on self-organizing interval type-2 fuzzy neural network error compensation (SOT2-FNNEC) is proposed. The proposed control framework can effectively suppress various types of interference such as base jitter, signal interference, time delay, etc., during the movement of the manipulator. The fuzzy neural network structure and self-organization method are used to realize the online self-organization of fuzzy rules based on control data. The stability of the closed-loop control systems are proved by Lyapunov stability theory. Simulations show that the algorithm is superior to a self-organizing fuzzy error compensation network and conventional sliding mode variable structure control methods in control performance. Full article
(This article belongs to the Special Issue Intelligent Modeling and Control)
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