Sliding mode control(SMC)has been studied since the 1950s and widely used in practical applications due to its insensitivity to matched *** aim of this paper is to present a review of SMC describing the key developmen...
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Sliding mode control(SMC)has been studied since the 1950s and widely used in practical applications due to its insensitivity to matched *** aim of this paper is to present a review of SMC describing the key developments and examining the new trends and challenges for its application to power electronic *** fundamental theory of SMC is briefly reviewed and the key technical problems associated with the implementation of SMC to power converters and drives,such chattering phenomenon and variable switching frequency,are discussed and *** recent developments in SMC systems,future challenges and perspectives of SMC for power converters are discussed.
This paper focuses on the optimal output synchronization control problem of heterogeneous multiagent systems(HMASs) subject to nonidentical communication delays by a reinforcement learning *** with existing studies as...
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This paper focuses on the optimal output synchronization control problem of heterogeneous multiagent systems(HMASs) subject to nonidentical communication delays by a reinforcement learning *** with existing studies assuming that the precise model of the leader is globally or distributively accessible to all or some of the followers, the leader's precise dynamical model is entirely inaccessible to all the followers in this paper. A data-based learning algorithm is first proposed to reconstruct the leader's unknown system matrix online. A distributed predictor subject to communication delays is further devised to estimate the leader's state, where interaction delays are allowed to be nonidentical. Then, a learning-based local controller, together with a discounted performance function, is projected to reach the optimal output synchronization. Bellman equations and game algebraic Riccati equations are constructed to learn the optimal solution by developing a model-based reinforcement learning(RL) algorithm online without solving regulator equations, which is followed by a model-free off-policy RL algorithm to relax the requirement of all agents' dynamics faced by the model-based RL algorithm. The optimal tracking control of HMASs subject to unknown leader dynamics and communication delays is shown to be solvable under the proposed RL algorithms. Finally, the effectiveness of theoretical analysis is verified by numerical simulations.
Reinforcement learning(RL)has shown significant potential for dealing with complex decision-making ***,its performance relies heavily on the availability of a large amount of high-quality *** many real-world situation...
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Reinforcement learning(RL)has shown significant potential for dealing with complex decision-making ***,its performance relies heavily on the availability of a large amount of high-quality *** many real-world situations,data distribution in the target domain may differ significantly from that in the source domain,leading to a significant drop in the performance of RL *** adaptation(DA)strategies have been proposed to address this issue by transferring knowledge from a source domain to a target ***,there have been no comprehensive and in-depth studies to evaluate these *** this paper we present a comprehensive and systematic study of DA in *** first introduce the basic concepts and formulations of DA in RL and then review the existing DA methods used in *** main objective is to fill the existing literature gap regarding DA in *** achieve this,we conduct a rigorous evaluation of state-of-the-art DA *** aim to provide comprehensive insights into DA in RL and contribute to advancing knowledge in this *** existing DA approaches are divided into seven categories based on application *** approaches in each category are discussed based on the important data adaptation metrics,and then their key characteristics are ***,challenging issues and future research trends are highlighted to assist researchers in developing innovative improvements.
Multivariate Time Series (MTS) forecasting has gained significant importance in diverse domains. Although Recurrent Neural Network (RNN)-based approaches have made notable advancements in MTS forecasting, they do not ...
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Medical image segmentation is a crucial process for computer-aided diagnosis and *** image segmentation refers to portioning the images into small,disjointed parts for simplifying the processes of analysis and *** and...
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Medical image segmentation is a crucial process for computer-aided diagnosis and *** image segmentation refers to portioning the images into small,disjointed parts for simplifying the processes of analysis and *** and speckle noise are different types of noise in magnetic resonance imaging(MRI)that affect the accuracy of the segmentation process ***,image enhancement has a significant role in MRI *** paper proposes a novel framework that uses 3D MRI images from Kaggle and applies different diverse models to remove Rician and speckle noise using the best possible noise-free *** proposed techniques consider the values of Peak Signal to Noise Ratio(PSNR)and the level of noise as inputs to the attention-U-Net model for segmentation of the *** framework has been divided into three stages:removing speckle and Rician noise,the segmentation stage,and the feature extraction *** framework presents solutions for each problem at a different stage of the *** the first stage,the framework uses Vibrational Mode Decomposition(VMD)along with Block-matching and 3D filtering(Bm3D)algorithms to remove the ***,the most significant Rician noise-free images are passed to the three different methods:Deep Residual Network(DeRNet),Dilated Convolution Auto-encoder Denoising Network(Di-Conv-AE-Net),andDenoising Generative Adversarial Network(DGAN-Net)for removing the speckle *** Bm3D have achieved PSNR values for levels of noise(0,0.25,0.5,0.75)for reducing the Rician noise by(35.243,32.135,28.214,24.124)and(36.11,31.212,26.215,24.123)*** framework also achieved PSNR values for removing the speckle noise process for each level as follows:(34.146,30.313,28.125,24.001),(33.112,29.103,27.110,24.194),and(32.113,28.017,26.193,23.121)forDeRNet,Di-Conv-AE-Net,and DGAN-Net,*** experiments that have been conducted have proved the efficiency of the proposed framework a
In this study,a localisation system without cumulative errors is ***,depth odometry is achieved only by using the depth information from the depth *** the point cloud cross-source map registration is realised by 3D pa...
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In this study,a localisation system without cumulative errors is ***,depth odometry is achieved only by using the depth information from the depth *** the point cloud cross-source map registration is realised by 3D particle filtering to obtain the pose of the point cloud relative to the ***,we fuse the odometry results with the point cloud to map registration results,so the system can operate effectively even if the map is *** effectiveness of the system for long-term localisation,localisation in the incomplete map,and localisation in low light through multiple experiments on the self-recorded dataset is *** with other methods,the results are better than theirs and achieve high indoor localisation accuracy.
In this paper,the formation control problem of secondorder nonholonomic mobile robot systems is investigated in a dynamic event-triggered ***-triggered control protocols combined with persistent excitation(PE)conditio...
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In this paper,the formation control problem of secondorder nonholonomic mobile robot systems is investigated in a dynamic event-triggered ***-triggered control protocols combined with persistent excitation(PE)conditions are *** event-detecting processes,an inactive time is introduced after each sampling instant,which can ensure a positive minimum sampling *** increase the flexibility of the event-triggered scheme,internal dynamic variables are included in event-triggering ***,the dynamic event-triggered scheme plays an important role in increasing the lengths of time intervals between any two consecutive *** addition,event-triggered control protocols without forward and angular velocities are also presented based on approximate-differentiation(low-pass)*** asymptotic convergence results are given based on a nested Matrosov theorem and artificial sampling methods.
Today, system identification plays a pivotal role in control science and offering a myriad of applications. This paper places its focus on the identification of actuator models within real-world delta robots for infor...
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controlling an active distribution network(ADN)from a single PCC has been advantageous for improving the performance of coordinated Intermittent RESs(IRESs).Recent studies have proposed a constant PQ regulation approa...
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controlling an active distribution network(ADN)from a single PCC has been advantageous for improving the performance of coordinated Intermittent RESs(IRESs).Recent studies have proposed a constant PQ regulation approach at the PCC of ADNs using coordination of non-MPPT based ***,due to the intermittent nature of DGs coupled with PCC through uni-directional broadcast communication,the PCC becomes vulnerable to transient *** address this challenge,this study first presents a detailed mathematical model of an ADN from the perspective of PCC regulation to realize rigidness of PCC against ***,an H_(∞)controller is formulated and employed to achieve optimal performance against disturbances,consequently,ensuring the least oscillations during transients at ***,an eigenvalue analysis is presented to analyze convergence speed limitations of the newly derived system ***,simulation results show the proposed method offers superior performance as compared to the state-of-the-art methods.
The sample’s hemoglobin and glucose levels can be determined by obtaining a blood sample from the human body using a needle and analyzing ***(HGB)is a critical component of the human body because it transports oxygen...
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The sample’s hemoglobin and glucose levels can be determined by obtaining a blood sample from the human body using a needle and analyzing ***(HGB)is a critical component of the human body because it transports oxygen from the lungs to the body’s tissues and returns carbon dioxide from the tissues to the *** the HGB level is a critical step in any blood analysis *** often indicate whether a person is anemic or polycythemia *** ensemble models by combining two or more base machine learning(ML)models can help create a more improved *** purpose of this work is to present a weighted average ensemble model for predicting hemoglobin *** optimization method is utilized to get the ensemble’s optimum *** optimum weight for this work is determined using a sine cosine algorithm based on stochastic fractal search(SCSFS).The proposed SCSFS ensemble is compared toDecision Tree,Multilayer perceptron(MLP),Support Vector Regression(SVR)and Random Forest Regressors as model-based approaches and the average ensemble *** SCSFS results indicate that the proposed model outperforms existing models and provides an almost accurate hemoglobin estimate.
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