Article
Mathematics, Applied
Hui Fu, Yonggui Kao
Summary: This paper proposes two adaptive sliding mode control (ASMC) strategies for achieving finite-time synchronization of uncertain general fractional unified chaotic systems (UGFUCSs) in the presence of uncertainty and external disturbance. The general fractional unified chaotic system (GFUCS) is first developed, which can be transitioned from the general Lorenz system to the general Chen system using a general kernel function. Two ASMC methods are then employed to achieve finite-time synchronization of UGFUCSs, where the system states reach the sliding surfaces within a finite time. The first ASMC approach uses three sliding mode controllers for synchronization between chaotic systems, while the second ASMC method only requires one sliding mode controller. The effectiveness of the proposed ASMC approaches is verified through numerical simulations.
Article
Multidisciplinary Sciences
Jianxiang Yang, Jianbin Xiong, Jian Cen, Wei He
Summary: This paper focuses on the finite-time generalized synchronization problem of non-identical fractional order chaotic (or hyper-chaotic) systems by designing an adaptive sliding mode controller. The effects of disturbances and model uncertainties are taken into account. The proposed approach is validated through numerical simulations, and a novel speech cryptosystem is proposed based on the generalized finite-time synchronization criterion.
Article
Mathematics, Interdisciplinary Applications
Masoud S. Bahraini, Mohammad Javad Mahmoodabadi, Niels Lohse
Summary: In this paper, a robust adaptive fuzzy fractional control strategy is proposed for stabilizing nonlinear chaotic systems with uncertainties. The strategy combines a fuzzy logic controller with fractional-order calculus to accurately model the system's behavior and adapt to uncertainties in real-time. The proposed controller based on a supervised sliding mode controller and an optimal robust adaptive fractional PID controller proved to outperform a recently introduced controller in the literature, improving the response of the system and demonstrating the effectiveness and robustness of the approach. The presented results provide strong evidence of the potential of the proposed strategy in a range of applications involving nonlinear chaotic systems with uncertainties.
FRACTAL AND FRACTIONAL
(2023)
Article
Mathematics, Interdisciplinary Applications
Youjun Chen, Songyu Wang
Summary: This work investigates a backstepping controller design for fractional-order strict feedback systems using the neural network control method. Robust terms are designed in the controller to handle estimation errors and ensure stability of the controlled system. The proposed controller has a simple form that can be easily implemented.
Article
Engineering, Mechanical
A. A. Kuz'menko
Summary: This article presents a method for constructing robust synchronization laws using a synergy-cybernetic approach, which shows good performance in terms of parametric perturbations and system stability.
NONLINEAR DYNAMICS
(2022)
Article
Mathematics, Applied
Jesus Emmanuel Solis-Perez, Jose Francisco Gomez-Aguilar, Dumitru Baleanu, Fairouz Tchier, Lakhdar Ragoub
Summary: In this paper, a new numerical method based on two-step Lagrange polynomial interpolation was proposed for numerical simulations and adaptive anti-synchronization schemes of two fractional conformable attractors of variable order. The method was applied to derive new results on anti-synchronization of uncertain chaotic systems, demonstrating its effectiveness in adaptive fractional conformable anti-synchronization schemes.
MATHEMATICAL METHODS IN THE APPLIED SCIENCES
(2021)
Article
Acoustics
Samaneh Payandeh Najafabadi, Mahnaz Hashemi
Summary: This article investigates the problem of adaptive sliding synchronization for Duffing-Holmes fractional-order chaotic systems in the presence of dead-zone, disturbance, and uncertainty. The proposed adaptive sliding mode controller guarantees the asymptotic stability of the system despite the presence of the dead-zone and uncertainty. Simulation results show the validity and effectiveness of the proposed controller for synchronization of Duffing-Holmes fractional-order chaotic systems perturbed by the dead-zone, disturbance, and uncertainty.
JOURNAL OF VIBRATION AND CONTROL
(2023)
Article
Engineering, Electrical & Electronic
Leimin Wang, Shan Jiang, Ming-Feng Ge, Cheng Hu, Junhao Hu
Summary: A unified framework is proposed to address the synchronization problem of memristor chaotic systems via the sliding-mode control method in this paper. The finite-time and fixed-time synchronization of MCSs can be realized simultaneously. Theoretical results are proven through mathematical proofs, and an image encryption algorithm along with its implementation process is developed to demonstrate the application of synchronization.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2021)
Article
Mathematics
Weiqiu Pan, Tianzeng Li, Muhammad Sajid, Safdar Ali, Lingping Pu
Summary: This paper investigates the finite-time combination-combination (C-C) synchronization of fractional order chaotic systems under multiple stochastic disturbances using the nonsingular terminal sliding mode control technique. The paper proposes a new fractional order sliding surface and adaptive control laws to achieve finite-time synchronization of drive-response systems. The proposed scheme is validated through examples using MATLAB.
Article
Mathematics, Applied
Hanlin Dong, Jinde Cao, Heng Liu
Summary: In this paper, an observer-based event-triggered adaptive fuzzy backstepping synchronization control method is proposed for a class of uncertain fractional order chaotic systems. Fuzzy logic systems are used to estimate unknown functions and a fractional order command filter is designed to avoid complexity problems. An effective error compensation mechanism is devised to reduce filter error and improve synchronization accuracy. The designed controller ensures convergence of the synchronization error and avoids Zeno behavior, as demonstrated through numerical simulations.
Article
Mathematics, Applied
Honglei Yin, Bo Meng, Zhen Wang
Summary: This article addresses the synchronization control problem of a class of chaotic systems with unknown uncertainties and outside perturbation by using an innovative adaptive sliding mode controller constructed using a disturbance observer. The disturbance observer can approximate the unknown external disturbances well by choosing the appropriate gain matrix. Then, a continuous adaptive sliding mode controller based on the disturbance observer's output is designed using adaptive techniques and the system dimensional expansion method. The efficiency of the suggested strategy is finally tested numerically using the Duffing-Holmes chaotic system.
Article
Computer Science, Information Systems
Madini O. Alassafi, Shumin Ha, Fawaz E. Alsaadi, Adil M. Ahmad, Jinde Cao
Summary: An adaptive command filtered fuzzy synchronization approach is implemented for strict feedback fractional-order chaotic systems, solving computational explosion in backstepping using a fractional-order command filter and error compensation mechanism, ensuring stability of synchronization errors.
INFORMATION SCIENCES
(2021)
Article
Mathematics, Interdisciplinary Applications
Yongbing Huangfu, Kaijuan Xue
Summary: This work focuses on solving the synchronization problem of uncertain chaotic systems with dead zones by utilizing Lyapunov stability theorems, fuzzy inference, and fuzzy adaptive controllers. The synchronization between two chaotic systems with dead zones is achieved, and a fuzzy variable-structure control is implemented. The stability of the system is proven rigorously, and simulation examples are provided to validate the theoretical results.
Article
Mathematics, Interdisciplinary Applications
Yang Wang, Zhen Wang, Lingyun Kong
Summary: A novel time-varying gain observer-based sliding mode control method is proposed to address synchronization of chaotic systems with nonvanishing uncertainties, effectively eliminating spike problem caused by high observer gain.
Article
Mathematics
Chih-Hsueh Lin, Guo-Hsin Hu, Jun-Juh Yan
Summary: This study introduces a robust sliding mode control method to achieve chaos synchronization even under the influence of matched/mismatched disturbances and input uncertainty. By using a proportional-integral switching surface, the controlled error dynamics in the sliding manifold become easier to analyze.
Article
Automation & Control Systems
Mohammad Hosein Sabzalian, Ardashir Mohammadzadeh, Sakthivel Rathinasamy, Weidong Zhang
Summary: This study presents a novel observer-based fuzzy control method for chaotic systems with unmeasurable states, unknown input constraints and unknown dynamics. The proposed control system shows good performance in the face of disturbances, uncertainties, unknown and time-varying input nonlinearities, unmeasurable states, and noisy faults, and is more effective compared to other types of fuzzy systems.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2023)
Article
Engineering, Mechanical
Amin Taghieh, Ardashir Mohammadzadeh, Chunwei Zhang, Sakthivel Rathinasamy, Stelios Bekiros
Summary: A novel observer-based control policy using an interval type-3 fuzzy logic system is developed to overcome the limitations of fuzzy-based controllers in approximating uncertainties and analyzing complex nonlinear systems without detailed dynamics model information. The proposed approach includes online optimized tuning rules, a simple type reduction method, and adaptive mechanisms. It also utilizes an adaptive compensator to improve the robust performance of the closed-loop system and mitigate the effects of approximation errors. Stability analysis is conducted using appropriate Lyapunov functions and Barbalat's lemma. Simulations and experimental implementations demonstrate that the suggested approach achieves more accurate approximation of unknown models and complex nonlinearities, and exhibits good resistance against uncertainties and parameter variations.
NONLINEAR DYNAMICS
(2023)
Article
Computer Science, Artificial Intelligence
Shu-Rong Yan, Wei Guo, Ardashir Mohammadzadeh, Sakthivel Rathinasamy
Summary: This study introduces a new control approach for active/reactive power control in modernized microgrids. The control method utilizes a fuzzy reference tracking linear quadratic regulator and an optimal H-infinity-based deep learned control to handle uncertainties and faults. The study presents several contributions and verifies the applicability of the suggested control method through simulations and real-time examination. A comparison with related controllers shows that the designed controller is more robust and accurate.
APPLIED INTELLIGENCE
(2023)
Article
Energy & Fuels
Man-Wen Tian, Shu-Rong Yan, Wei Guo, Ardashir Mohammadzadeh, Ebrahim Ghaderpour
Summary: This article proposes a decisive task scheduling method for energy conservation in IoT and MEC architectures. The method utilizes conditional decision-making through classification disseminations and energy slots to prevent overload and dissemination. The proposed method achieved a high data dissemination rate (8.16%), lower energy utilization (10.65%), and reduced latency (11.44%) at different time slots.
Article
Automation & Control Systems
Mohammad Javad Mirzaei, Sehraneh Ghaemi, Mohammad Ali Badamchizadeh, Mahdi Baradarannia
Summary: In this paper, a robust distributed consensus control method based on adaptive time-varying gains is proposed for nonlinear multi-agent systems (MAS) with uncertain parameters and external disturbances. The discontinuous and continuous adaptive integral sliding mode control strategies are designed to achieve precise consensus for non-identical MASs influenced by perturbations. An adaptive scheme is used to overcome the unknown upper bound of perturbations. The designed distributed super-twisting sliding mode strategy adjusts the gain of the control inputs and guarantees the proper performance of the protocol without chattering phenomenon. Simulation results demonstrate the robustness, accuracy, and effectiveness of the proposed methods.
Article
Computer Science, Artificial Intelligence
Amiraslan Haghrah, Amirarslan Haghrah, Javad M. Niya, Sehraneh Ghaemi
Summary: Increasing spectrum efficiency in new-generation communication networks can be achieved by increasing operating frequencies or serving cells. This leads to a decrease in cell size and raises the importance of mobility management to ensure seamless connectivity. This paper proposes a novel fuzzy logic-based method to trigger handover procedures based on estimated radio link quality values of serving and neighboring cells, resulting in an improved handover performance.
Article
Mathematics
Jinfeng Wang, Hui Dong, Fenghua Chen, Mai The Vu, Ali Dokht Shakibjoo, Ardashir Mohammadzadeh
Summary: Formation control of a group of robots in trajectory tracking problems is a key research topic in robotics. Using organized robots has advantages like efficient resource utilization, increased reliability due to cooperation, and better resistance against defects. A controller is proposed to steer the leader and follower robots to a reference trajectory asymptotically. The controller uses feedback linearization and a compensator based on type-3 fuzzy logic systems (T3-FLSs) and a data-driven control strategy to ensure stability against perturbations.
Article
Computer Science, Artificial Intelligence
Abdulaziz S. Alkabaa, Osman Taylan, Muhammed Balubaid, Chunwei Zhang, Ardashir Mohammadzadeh
Summary: This study introduces a novel path-following scheme for mobile robots using a new intelligent type-3 fuzzy system. The system is capable of handling natural disturbances and dynamics uncertainties by employing a non-singleton FS and error measurement signals. To improve accuracy, a Boltzmann machine is utilized to model tracking errors and predict compensators. A parallel supervisor is incorporated in the central controller for robustness. Simulation results with chaotic reference signals demonstrate the accuracy and robustness of the proposed scheme even in the presence of unknown dynamics and disturbances. Additionally, a practical implementation on a real-world robot confirms the feasibility of the designed controller. To watch a short video of the scheme in action, visit shorturl.at/imoCH.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Mathematics, Applied
N. Aravinth, T. Satheesh, R. Sakthivel, G. Ran, A. Mohammadzadeh
Summary: In this work, the problems of input-output finite-time stability and disturbance rejection for continuous-time periodic piecewise systems with linear fractional uncertainty are investigated. A periodic piecewise disturbance observer (PPDO) is proposed to estimate the matched disturbances, while the mismatched disturbances are handled by implementing H infinity control protocol and quantizing the state feedback. The anti-disturbance control protocol is developed by combining the quantized state-feedback control law with the output of the PPDO. With the help of linear matrix inequalities (LMIs), a collection of criteria affirming the system's input-output finite-time stability are obtained. The simulation results verify the potential of the developed control strategy.
APPLIED MATHEMATICS AND COMPUTATION
(2023)
Article
Automation & Control Systems
Ardashir Mohammadzadeh, Hamid Taghavifar, Chunwei Zhang, Khalid A. Alattas, Jinping Liu, Mai The Vu
Summary: This study introduces a robust type-3 fuzzy controller implementation for the path-tracking task of driverless cars during critical driving conditions and subject to exogenous disturbances. The proposed scheme is independent of the parameter information and assumes unknown and non-linear system dynamics. Control inputs are constructed to improve robustness and ensure stability by leveraging the Lyapunov stability theorem and Barbalat's lemma. Also, a predicate scheme based on non-linear predictive control technique is introduced to enhance the lateral displacement.
IET CONTROL THEORY AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Ommegolsoum Jafarzadeh, Seyyed Arash Mousavi Ghasemi, Seyed Mehdi Zahrai, Ardashir Mohammadzadeh, Ramin Vafaei Poursorkhabi
Summary: This paper introduces a novel adaptive neurochaotic fuzzy control system based on type-2 fuzzy systems to reduce seismic responses in multistory structures with active tuned mass dampers. The proposed control system utilizes online estimation and adaptive parameter training methods to achieve efficient reduction of seismic responses such as maximum displacement and acceleration.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Man-Wen Tian, Khalid A. Alattas, Wei Guo, Hamid Taghavifar, Ardashir Mohammadzadeh, Wenjun Zhang, Chunwei Zhang
Summary: This paper studies the synchronization and control of chaotic systems while proposing a novel chaotic-based path-tracking application for mobile robots to ensure their safety and security. The main challenges are that the dynamics of the robots are entirely unknown.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Engineering, Mechanical
Hamid Taghavifar, Ardashir Mohammadzadeh
Summary: This paper proposes a novel control framework for the path-tracking task of autonomous ground vehicles (AGVs). The control system utilizes a nonlinear adaptive approach, combining integral backstepping with terminal sliding mode control. The controller achieves finite time convergence, robustness, and a chatter-free response by integrating integral action and terminal sliding mode. Additionally, adaptive control compensators are developed to ensure robustness against unknown disturbances. High-fidelity cosimulations are conducted to validate the effectiveness of the proposed control scheme.
Article
Computer Science, Information Systems
Bicheng Yan, Xiaoqiang Jiang, Khalid A. Alattas, Chunwei Zhang, Ardashir Mohammadzadeh
Summary: This paper presents a novel fuzzy control strategy for generating limit cycles with specific behaviors in nonlinear complex dynamics. The proposed controller utilizes interval type-3 fuzzy logic, enhancing the quality of the closed-loop response and robust performance. An adaptively learned backstepping controller based on fuzzy control is employed to analyze convergence and robustness. Various simulations are conducted to validate the effectiveness of the fuzzy-based control law and adaptation rules.
Article
Computer Science, Information Systems
Lili Wu, Haiyan Huang, Meng Wang, Khalid A. Alattas, Ardashir Mohammadzadeh, Ebrahim Ghaderpour
Summary: The paper investigates the control of wheeled land mobile robots using nonlinear equations and non-holonomic dynamic constraints. It proposes a novel approach based on type-3 fuzzy logic systems for system identification and parameter estimation. The simulations demonstrate that the proposed controller yields excellent results even in the presence of non-holonomic constraints and fully unknown dynamics.
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.
