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adaptive finite-time fault-tolerant control for nano-satellite attitude tracking under actuator constraints

AEROSPACE SCIENCE AND TECHNOLOGY 2023年第1期138卷

作者： Sadigh, Sevil M. Kashaninia, Abdorreza Dehghan, Seyyed Mohammad Mehdi Malek Ashtar Univ Technol Fac Elect & Comp Engn Tehran Iran

In this paper, a finite-time fault-tolerant control method is developed for attitude tracking of a nano-satellite with three magnetorquers and one reaction wheel in the presence of actuator faults, inertia uncertainties, external distribution, and actuator constraints. For this purpose, the modified non-singular fast terminal sliding mode control is used due to the ability of robustness against uncertainties and finite-time convergence. In this approach, the modified sliding surface variable is constructed based on angular velocity and attitude error to converge the attitude error to zero in a finite time. In addition, three adaptive parameters are employed to reduce the adverse effect of uncertain expressions and to track the attitude commands with high control accuracy. The adaptive parameters and sliding surface variable are used in the reaching phase of the controller to decrease the chattering phenomenon. The dynamics of adaptive parameters, with regard to the angular velocities of the nano-satellite and reaction wheel and the sliding surface variable, are obtained during the stability proof of the closed-loop system. Also, the finite-time convergence of sliding surface and attitude variables are proved by the extended Lyapunov condition, and the convergence regions of attitude tracking errors are obtained. The simulations are accomplished and compared with an existing adaptive sliding mode control approach to evaluate the performance of the proposed controller. The results verify that the proposed controller performs better than the mentioned controller in terms of finite-time convergence, the accuracy of attitude tracking, and the reduction of the chattering phenomenon.(c) 2023 Elsevier Masson SAS. All rights reserved.

关键词： Satellite attitude tracking Fault-tolerant control Sliding mode control adaptive algorithm Input saturation Actuator faults

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An Advanced adaptive Least Recently Used Buffer Management algorithm for SSD

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IEEE ACCESS 2019年 7卷 33494-33505页

作者： Yao, Yingbiao Kong, Xiaochong Zhou, Jie Xu, Xiaorong Feng, Wei Liu, Zhaoting Hangzhou Dianzi Univ Sch Commun Engn Hangzhou 310018 Zhejiang Peoples R China

The traditional solid-state drive buffer management algorithm generally adopts fixed structures and parameters, leading to their poor adaptability. For example, after the underlying flash translation layer (FTL) or the upper workload is changed, the traditional algorithm's performance fluctuates significantly. Focusing on this problem, based on the cross-layer aware method, we propose an Advanced adaptive Least Recently Used buffer management algorithm (AALRU). The core idea of the AALRU is that by sensing the characteristics of the upper workload and the status of the underlying FTL, the AALRU adaptively adjusts its structure, parameters, and write-back strategy to optimize the buffer's performance. First, the AALRU divides the buffer into two parts: read buffer and write buffer, and their proportion is adjusted by sensing the read-write characteristics of the workload and the underlying read-write latency. Second, the AALRU employs different granularities to manage the buffer. On one hand, for data loading and migrating, the AALRU adopts page-level granularity, which can avoid the problem of hot and cold data page entanglement in block management, and thus improve the buffer hit ratio. On the other hand, for data writing back to the FTL, the AALRU adopts block-level granularity, which can enhance the continuity of write requests and thus reduce the underlying FTL's garbage collection overhead. Finally, when the clustered data are written back, by sensing the underlying FTL's garbage collection status, the AALRU adaptively adjusts the page-padding trigger threshold to reconstruct the continuity of the write-back data, which can mitigate the underlying FTL's garbage collection overhead. The experimental results show that the AALRU has the best adaptability to different FTLs and test workloads, and it can achieve optimal or near-optimal results.

关键词： Solid-state drive buffer management adaptive algorithm cross-layer aware

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A Linearized adaptive Dynamic Diffusion Finite Element Method for Convection-Diffusion-Reaction Equations

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Annals of Applied Mathematics 2023年第3期39卷 323-351页

作者： Shaohong Du Qianqian Hou Xiaoping Xie School of Mathematics and Statistics Chongqing Jiaotong UniversityChongqing 400074China School of Mathematics Sichuan UniversityChengduSichuan 610064China

In this paper,we consider a modified nonlinear dynamic diffusion(DD)method for convection-diffusion-reaction *** method is free of stabilization parameters and capable of precluding spurious *** develop a reliable and efficient residual-type a posteriori error estimator,which is robust with respect to the diffusivity ***,we propose a linearized adaptive DD algorithm based on the a posteriori ***,we perform numerical experiments to verify the theoretical analysis and the performance of the adaptive algorithm.

关键词： Convection-diffusion-reaction equation dynamical diffusion method residualtype a posteriori error estimator adaptive algorithm

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New symmetric decorrelating set-membership NLMS adaptive algorithms for blind speech intelligibility enhancement

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SN APPLIED SCIENCES 2019年第12期1卷 1587页

作者： Djendi, Mohamed Cheffi, Abdelhak Univ Blida 1 Lab Signal Proc & Imaging LATSI Route SoumaaBP 270 Blida 09000 Algeria

This paper addresses the problem of speech enhancement and acoustic noise reduction by partial and set-membership adaptive algorithms combined with the symmetric decorrelating adaptive (SAD) algorithm structure. In this paper, we propose two new adaptive algorithms based on set-membership principle that improve the original set-membership algorithm behavior in speech enhancement applications. The first proposed algorithm (called Proposed 1), is based on the combination of the SAD algorithm structure with a smart control that uses decorrelating properties between the output and the mixing signal to control and update the SAD adaptive filters. The second proposed algorithm (called Proposed 2) is a modification of Proposed 1 and based on a new regularization relations of the SAD adaptive filters that use a combination between the variance of the mixing and the output signals of the SAD structures. These two proposed algorithms (Proposed 1 and Proposed 2) aim to improve the convergence speed performance and the output signal-to-noise-ratio of the original SAD algorithm when no smart control of the adaptive filters is used. The proposed algorithms have very interesting properties with non-stationary signal like speech when the SAD algorithm is, used alone, fails. The simulation results that are obtained by the comparison between the proposed algorithms (Proposed 1 and Proposed 2) and the original two-channel set-membership NLMS algorithm have shown the best performances of Proposed 1 and Proposed 2 in terms of the following criteria: systems mismatch, segmental SNR and segmental men square error.

关键词： Speech enhancement SAD NLMS Set-membership adaptive algorithm Convergence speed

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A novel multi-objective evolutionary algorithm with fuzzy logic based adaptive selection of operators: FAME

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INFORMATION SCIENCES 2019年 471卷 233-251页

作者： Santiago, Alejandro Dorronsoro, Bernabe Nebro, Antonio J. Durillo, Juan J. Castillo, Oscar Fraire, Hector J. Univ Cadiz Sch Engn Cadiz Spain Polytech Univ Altamira Altamira Mexico Madero City Inst Technol Madero City Mexico Univ Malaga Malaga Spain Leibniz Supercomp Ctr Munich Germany Tijuana Inst Technol Tijuana Mexico

We propose a new method for multi-objective optimization, called Fuzzy adaptive Multi-objective Evolutionary algorithm (FAME). It makes use of a smart operator controller that dynamically chooses the most promising variation operator to apply in the different stages of the search. This choice is guided by a fuzzy logic engine, according to the contributions of the different operators in the past. FAME also includes a novel effective density estimator with polynomial complexity, called Spatial Spread Deviation (SSD). Our proposal follows a steady-state selection scheme and includes an external archive implementing SSD to identify the candidate solutions to be removed when it becomes full. To assess the performance of our proposal, we compare FAME with a number of state of the art algorithms (MOEA/D-DE, SMEA, SMPSOhv, SMS-EMOA, and BORG) on a set of difficult problems. The results show that FAME achieves the best overall performance. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Multi-objective optimization density estimation evolutionary algorithm adaptive algorithm fuzzy logic

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adaptive Dynamic Planning Consensus Control of Multi Agent Systems Based on Stochastic Fuzzy Neural Networks

Adaptive Dynamic Planning Consensus Control of Multi Agent S...

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第43届中国控制会议

作者： Shengjie Tang Xianxia Zhang Songbo Xie Shanghai University School of Communication and Information Engineering

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper proposes an SFNN-ADHDP algorithm to approximate the solution of the highly coupled HJB equation for the optimal consensus control problem of first-order nonlinear multi-agent systems. Optimal consensus control of multi-agent systems is achieved by innovatively combining stochastic fuzzy neural networks with adaptive dynamic programming. The proposed SFNN-ADHDP algorithm only requires the topology information of multi-agent systems for optimal consensus *** theory related knowledge and consensus problem modeling for the optimal consensus control problem of multi-agent systems are firstly presented in this paper. Secondly, the specific algorithm structure and implementation details of the SFNN-ADHDP are proposed. Finally, a set of simulation experiments are conducted to demonstrate the excellent anti-interference ability and superiority of the proposed algorithm, comparing the SFNN network with the ANFIS network by attempting to incorporate Gaussian white noise and other methods into the network.

关键词： rst order nonlinear multi-agent systems optimal consensus control problem SFNN-ADHDP algorithm adaptive algorithm

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adaptive algorithm for estimation of two-dimensional autoregressive fields from noisy observations

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International Journal of Stochastic Analysis 2014年第1期2014卷 247274-1-247274-5页

作者： Mahmoudi, Alimorad Electrical Engineering Department Shahid Chamran University Ahvaz Iran

This paper deals with the problem of two-dimensional autoregressive (AR) estimation from noisy observations. The Yule-Walker equations are solved using adaptive steepest descent (SD) algorithm. Performance comparisons... 详细信息

关键词： adaptive algorithm Autoregressive Field Noisy Observations adaptive algorithms Self tuning steeping

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adaptive fusion of multi-exposure images based on perceptron model

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年第1期9卷

作者： Mei, Jianqiang Chen, Wanyan Li, Biyuan Li, Shixin Zhang, Jun Yan, Jun Tianjin Univ Technol & Educ Sch Elect Engn Tianjin 300222 Peoples R China Tianjin Engn Res Ctr Fieldbus Control Technol Tianjin 300222 Peoples R China Tianjin Univ Sch Math Tianjin 300072 Peoples R China

Multi-exposure image fusion as a technical means to bridge the dynamic range gap between real scenes and image acquisition devices, which makes the fused images better quality and more realistic and vivid simulation of real scenes, has been widely concerned by scholars from various countries. In order to improve the adaptive fusion effect of multi-exposure images, this paper proposes a fusion algorithm based on multilayer perceptron (MLP) based on the perceptron model and verifies the feasibility of the algorithm by the peak signal-to-noise ratio (PSNR), correlation coefficient (PCC), structural similarity (SSMI) and HDR-VDR-2, an evaluation index of HDR image quality. Comparison with other algorithms revealed that the average PSNR of the MLP algorithm improved by 4.43% over the Ma algorithm, 7.88% over the Vanmail algorithm, 10.30% over the FMMR algorithm, 11.19% over the PMF algorithm, and 11.19% over the PMF algorithm. For PCC, the MLP algorithm improves by 20.14%, 17.46%, 2.31%, 11.24%, and 15.36% over the other algorithms in that order. For SSMI, the MLP algorithm improved by 16.99%, 8.96%, 17.17%, 14.41%, and 4.85% over the other algorithms, in that order. For HDR-VDR-2, the MLP algorithm improved by 3.02%, 2.79%, 6.84%, 4.90%, and 6.55% over the other algorithms, in that order. The results show that the MLP algorithm can avoid image artifacts while retaining more details. The MLP-based adaptive fusion method is a step further in the theoretical study of multi-exposure image fusion, which is of great significance for subsequent research and practical application by related technology vendors.

关键词： Perceptron model Multi-exposure images Image fusion Multilayer perceptron adaptive algorithm

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Energy Detection in Cognitive Radio applications using Logarithmic Square adaptive Learning

Energy Detection in Cognitive Radio applications using Logar...

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International Conference on Mobile Networks and Wireless Communications (ICMNWC)

作者： Surekha, Sala Mantravadi, Nagesh Mirza, Shafi Shahsavar Rahman, Md Zia Ur KL Univ Koneru Lakshmaiah Educ Fdn Dept Elect & Commun Engn Guntur AP India Chalapathi Inst Engn & Technol Dept Elect & Commun Engn Guntur AP India

ISBN: (纸本)9780738146379

To avoid spectrum scarcity problems in wireless communications, cognitive radio concept used as reliable and effective solution. To use proper exploitation of white sources in cognitive radios required accurate, fast and robust methods. In this paper, we proposed new method for detecting white spaces in spectrum. Based on this strategy, cognitive radio performs spectrum sensing via energy detection technique. Main novelty of this paper is adaptive algorithm i.e., error normalized least mean logarithmic square (ENLMLS), it contains the information of primary user presence or absence. Identification of white spaces depends on entity which is able to improve deflection coefficient significantly related with detector when compared to other adaptive algorithms. Simulation results shows that proposed ENLMLS algorithm performs well compared to LMS algorithm by means of convergence. Further by using clipping function, it reduces noise levels and yields missed detection probability is smaller by SNR values and predefined threshold value.

关键词： adaptive algorithm energy detection cognitive radio spectrum sensing

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Tracking online low-rank approximations of higher-order incomplete streaming tensors

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PATTERNS 2023年第6期4卷 100759页

作者： Thanh, Le Trung Abed-Meraim, Karim Trung, Nguyen Linh Hafiane, Adel Univ Orleans PRISME Lab INSA CVL 12 Rue Blois F-45100 Orleans France VNU Univ Engn & Technol AVITECH Inst 144 Xuan Thuy Hanoi 10000 Vietnam Acad Inst France 1 Rue Descartes F-75005 Paris France

In this paper, we propose two new provable algorithms for tracking online low-rank approximations of high order streaming tensors with missing data. The first algorithm, dubbed adaptive Tucker decomposition (ATD), minimizes a weighted recursive least-squares cost function to obtain the tensor factors and the core tensor in an efficient way, thanks to an alternating minimization framework and a randomized sketching technique. Under the canonical polyadic (CP) model, the second algorithm, called ACP, is developed as a variant of ATD when the core tensor is imposed to be identity. Both algorithms are low-complexity tensor trackers that have fast convergence and low memory storage requirements. A unified convergence analysis is presented for ATD and ACP to justify their performance. Experiments indicate that the two proposed algorithms are capable of streaming tensor decomposition with competitive performance with respect to estimation accuracy and runtime on both synthetic and real data.

关键词： tensor decomposition low-rank approximation adaptive algorithm streaming tensor missing data randomized method

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