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Echo State Network-Enhanced Super-Twisting control of Passive Gait Training Exoskeleton Driven by Pneumatic Muscles

IEEE-ASME TRANSACTIONS ON MECHATRONICS 2022年第6期27卷 5107-5118页

作者： Zhang, Mengshi Huang, Jian Cao, Yu Xiong, Cai-Hua Mohammed, Samer Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Educ Minist Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Res Inst Shenzhen 518060 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Univ Paris Est Creteil F-94400 Vitry Sur Seine France

In this article, a robust trajectory tracking control method is proposed for the passive gait training exoskeleton system driven by pneumatic muscles (PMs). Conventional model-based controllers suffer from limitations with respect to model uncertainties and external disturbances caused by PMs and complex robotic systems. An echo state network (ESN) is used in this study to approximate the model uncertainties and external disturbances of the exoskeleton system. Based on the approximation of ESN, a super-twisting control (STC) algorithm is designed to guarantee accurate tracking control at both hip and knee joint levels. Because there are both weight error and global approximation error in neural network approximation, a standard quadratic form cannot be obtained which plays an important role in the stability analysis of the traditional super twisting algorithm. To solve this issue, a dedicated positive definite matrix is constructed in this article, which bridges the ESN and STC by providing a parameter selection criteria. The stability with respect to the tracking problem of the exoskeleton system is then guaranteed according to the Lyapunov theorem. Both numerical simulations and experimental results present better tracking accuracy and robustness compared with the traditional sliding mode control and STC.

关键词： Echo state network (ESN) lower limb exoskeleton pneumatic muscle (PM) actuator rehabilitation robotics super-twisting control (STC)

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Multistability of delayed fractional-order competitive neural networks

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NEURAL NETWORKS 2021年 140卷 325-335页

作者： Zhang, Fanghai Huang, Tingwen Wu, Qiujie Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Texas A&M Univ Qatar Sci Program Doha Qatar Anhui Univ Sch Internet Hefei Peoples R China

This paper is concerned with the multistability of fractional-order competitive neural networks (FCNNs) with time-varying delays. Based on the division of state space, the equilibrium points (EPs) of FCNNs are given. Several sufficient conditions and criteria are proposed to ascertain the multiple O(t(-alpha))-stability of delayed FCNNs. The O(t(-alpha))-stability is the extension of Mittag-Leffler stability of fractional-order neural networks, which contains monostability and multistability. Moreover, the attraction basins of the stable EPs of FCNNs are estimated, which shows the attraction basins of the stable EPs can be larger than the divided subsets. These conditions and criteria supplement and improve the previous results. Finally, the results are illustrated by the simulation examples. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Fractional-order competitive neural networks Multistability Attraction basins Delays

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Testbed implementation of reinforcement learning-based demand response

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APPLIED ENERGY 2021年 297卷

作者： Zhang, Xiongfeng Lu, Renzhi Jiang, Junhui Hong, Seung Ho Song, Won Seok Hanyang Univ Dept Elect Engn Ansan 15588 South Korea Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Hanyang Univ Nestfield Co Ltd 211 BI CtrERICA Campus Ansan 15588 South Korea

Demand response (DR) has been acknowledged as an effective method to improve the stability, and financial efficiency of power grids. During operation of a DR program, there are usually multiple interactions among different grid entities, which complicates decision-making processes with respect to grid operations. Recently, reinforcement learning (RL) has attracted increasing attention for managing complex decision-making problems, owing to its self-learning capacity. Several theoretical RL-based approaches have been proposed for addressing various DR issues, but the practical feasibility of these theoretical approaches remains to be proven. In this paper, a conceptual architecture is firstly proposed to support DR management of a diversified facility in the context of a price-based DR environment. Secondly, exhaustive guidelines are provided to illustrate how to implement a multi-agent RL-based algorithm in the constructed DR management system. Afterwards, a laboratory-level testbed was set up to evaluate the effectiveness of the deployed DR algorithm. The experimental evaluation results show that the RL-based DR algorithm takes about 20s and 50 episodes to achieve optimal load control policy. By executing the optimal operation policy, the overall energy consumption during the highest price period (i.e., 15:00-18:00) is significantly reduced by 133.6% compared with the lowest price period (i.e., 02:00-05:00).

关键词： Reinforcement learning (RL) Demand response (DR) Energy management Practical implementation

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Memristive Circuit Implementation of Operant Cascaded With Classical Conditioning

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IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2022年第5期16卷 926-938页

作者： Yang, Chao Wang, Xiaoping Chen, Zhanfei Zhang, Sen Zeng, Zhigang Huazhong Univ Sci & Technol Inst Artificial Intelligence Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Guangxi Univ Sci & Technol Sch Comp Sci & Technol Liuzhou 545006 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

Classical conditioning (CC) and operant conditioning (OC), also known as associative memory, are two of the most fundamental and critical learning mechanisms in the biological brain. However, the existing designs of associative memory memristive circuits mainly focus on CC, and few studies have used memristors to imitate OC at the behavioral level, as well as the OC-CC cascaded associative memories that are widespread in biological learning processes. This work proposes an OC-CC cascaded circuit composed of OC and CC circuits. With the OC memristive circuit, bio-like functions such as random exploration, feedback learning, experience memory, and experience-based decision-making are achieved, which enables the circuit to continuously reshape its own memories and actions to adapt to changing environments. By cascading it with the CC memristive circuit that has the functions of associative learning, forgetting, generalization, and differentiation, the OC-CC cascaded circuit implements richer associative memories and has stronger environmental adaptability. Finally, the proposed circuits can perform on-line in-situ learning and in-memory computing. This is a more brain-like processing method, which is different from the von Neumann architecture. The simulation results of the proposed circuits in PSPICE show that they can simulate the above functions and have advantages in power consumption and hardware overhead. This work provides a possible realization idea for large-scale bionic learning.

关键词： Memristors Associative memory Mice Electric shock Behavioral sciences Integrated circuit modeling In-memory computing Associative memory bionic learning classical conditioning in-memory computing memristor operant conditioning

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Sparse Prior Is Not All You Need: When Differential Directionality Meets Saliency Coherence for Infrared Small Target Detection

arXiv

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arXiv 2024年

作者： Zhou, Fei Fu, Maixia Qian, Yulei Yang, Jian Dai, Yimian Ministry of Education Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou China Nanjing Marine Radar Institute Nanjing China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Infrared small target detection is crucial for the efficacy of infrared search and tracking systems. Current tensor decomposition methods emphasize representing small targets with sparsity but struggle to separate targets from complex backgrounds due to insufficient use of intrinsic directional information and reduced target visibility during decomposition. To address these challenges, this study introduces a Sparse Differential Directionality prior (SDD) framework. SDD leverages the distinct directional characteristics of targets to differentiate them from the background, applying mixed sparse constraints on the differential directional images and continuity difference matrix of the temporal component, both derived from Tucker decomposition. We further enhance target detectability with a saliency coherence strategy that intensifies target contrast against the background during hierarchical decomposition. A Proximal Alternating Minimization-based (PAM) algorithm efficiently solves our proposed model. Experimental results on several real-world datasets validate our method’s effectiveness, outperforming ten state-of-the-art methods in target detection and clutter suppression. Our code is available at https://***/GrokCV/SDD. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

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Multivariate/minor fault diagnosis with severity level based on Bayesian decision theory and multidimensional RBC

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JOURNAL OF PROCESS control 2021年 101卷 68-77页

作者： Zheng, Ying Zhou, Wei Yang, Weidong Liu, Lang Liu, Yuanle Zhang, Yong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China Wuhan Univ Sci & Technol Engn Res Ctr Met Automat & Measurement Technol Sch Informat Sci & Engn Minist Educ Wuhan 430081 Peoples R China

Fault diagnosis(FD) is vital for monitoring of industrial processes. Actually, both the multiple fault variables and minor faults are inclined to bring wrong diagnosis results. In this paper, a fault variable identification method is proposed for multivariate/minor fault diagnosis. The deviation factor is adopted as the characteristic of the sample, and Bayesian decision theory is adopted to calculate the possibility of the variable being faulty, then multi-dimensional reconstruction-based contribution (MRBC) is used to determine the fault source variables. This method not only improves the diagnosis rate for multiple/minor faults with less computation time, but also can indicate severity level of the different fault variables according to the fault occurrence probability. A Numerical example and Tennessee Eastman process are given to show the efficiency of the proposed method. (C) 2021 Published by Elsevier Ltd.

关键词： Multivariate fault diagnosis Bayesian decision theory Multi-dimensional RBC

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Multiple ψ-Type Stability of Cohen-Grossberg Neural Networks With Unbounded Time-Varying Delays

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第1期51卷 521-531页

作者： Zhang, Fanghai Zeng, Zhigang Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

This paper investigates Cohen-Grossberg neural networks (CGNNs) with unbounded time-varying delays. The existence condition of multiple equilibrium points is established by the algebraic inequality method. And then, the phi-type stability is studied by analysis method for CGNNs with unbounded time-varying delays, and the relative convergence rate can be adjusted by the selection of phi-type functions. Moreover, by introducing the topological degree, the algebraic sum of the number of equilibria remains unchanged for any smooth function in the specified metric space. Finally, one numerical example is implemented to show the validity of the presented results.

关键词： phi-type stability Cohen-Grossberg neural networks (CGNNs) topological degree unbounded time-varying delays

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Enzymatic Numerical Spiking Neural Membrane Systems and their Application in Designing Membrane controllers

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INTERNATIONAL JOURNAL OF NEURAL SYSTEMS 2022年第11期32卷 2250055页

作者： Zhang, Luping Xu, Fei Xiao, Dongyang Dong, Jianping Zhang, Gexiang Neri, Ferrante Educ Minist China Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Chengdu Univ Technol Res Ctr Artificial Intelligence Chengdu 610059 Peoples R China Chengdu Univ Informat Technol Sch Automat Chengdu 610225 Peoples R China Univ Surrey Dept Comp Sci NICE Grp Guildford Surrey England

Spiking neural P systems (SN P systems), inspired by biological neurons, are introduced as symbolical neural-like computing models that encode information with multisets of symbolized spikes in neurons and process information by using spike-based rewriting rules. Inspired by neuronal activities affected by enzymes, a numerical variant of SN P systems called enzymatic numerical spiking neural P systems (ENSNP systems) is proposed wherein each neuron has a set of variables with real values and a set of enzymatic activation-production spiking rules, and each synapse has an assigned weight. By using spiking rules, ENSNP systems can directly implement mathematical methods based on real numbers and continuous functions. Furthermore, ENSNP systems are used to model ENSNP membrane controllers (ENSNP-MCs) for robots implementing wall following. The trajectories, distances from the wall, and wheel speeds of robots with ENSNP-MCs for wall following are compared with those of a robot with a membrane controller for wall following. The average error values of the designed ENSNP-MCs are compared with three recently fuzzy logical controllers with optimization algorithms for wall following. The experimental results showed that the designed ENSNP-MCs can be candidates as efficient controllers to control robots implementing the task of wall following.

关键词： Bio-inspired computing membrane computing spiking neural P system robotic control wall following

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Modeling collective behavior of posting microblogs by stochastic differential equation with jump

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2021年 578卷

作者： Pan, Jun-Shan Li, Yuan-Qi Hu, Han-Ping Hu, Yong Hubei Univ Fac Math & Stat Hubei Key Lab Appl Math Wuhan 430062 Peoples R China Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Phys Wuhan 430074 Peoples R China

The characterization and understanding of online social network behavior is of importance from both the points of view of fundamental research and realistic application. In this manuscript, we propose a stochastic differential equation to describe the online microblogging behavior. Our analysis is based on the microblog data collected from Sina Weibo, which is one of the most popular microblogging platforms in China. Especially, we focus on the collective nature of the microblogging behavior, which embodies itself as the periodic patterns, the stochastic fluctuations around the baseline, and the extraordinary jumps in the analyzed data. Compared with existing works, we use time dependent parameters to facilitate the periodic feature of the microblogging behavior and incorporate a compound Poisson process to describe the extraordinary spikes in the Sina Weibo volume. These distinct merits lead to significant improvement in the prediction performance, thus justifying the validity of our model. This work may offer an alternative route towards the future detection of the anomalous behavior in online social network platforms. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Social network Online microblog behavior Collective behavior modeling

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Containment control in fractional-order multi-agent systems with intermittent sampled data over directed networks

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NEUROCOMPUTING 2021年 442卷 209-220页

作者： Wu, Di An, Qing Sun, Yaping Liu, Yifan Su, Housheng Huazhong Univ Sci & Technol Educ Minist China Key Lab Image Proc & Intelligent Control Sch Artificial Intelligence & Automat Luoyu Rd 1037 Wuhan 430074 Peoples R China Wuchang Univ Technol Artificial Intelligence Sch Wuhan 430223 Peoples R China Guangdong HUST Ind Technol Res Inst Guangdong Prov Key Lab Digital Mfg Equipment Dongguan 523808 Peoples R China

The containment control problems of fractional order multi-agent systems (FOMASs) over directed networks are discussed in this paper. Firstly, two distributed intermittent sampled data control (ISDC) protocols which can reduce controllers' update rates and work hours, are introduced. Subsequently, some necessary and sufficient containment conditions are derived by virtue of Laplace transform and stability theory. The containment conditions reveal the relations among the fractional orders, sampling periods, communication constraints, coupling strengths and network topologies. Finally, two numerical experiments are presented to validate the theoretical results. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Containment FOMASs Intermittent control Fractional-order Sampled data Communication constraints

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