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Generalized Discrete-Time Variable Gain ADRC for Nonlinear systems and Its Application to Parallel Teleoperated Manipulators

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IEEE TRANSACTIONS ON CIRCUITS AND systems I-REGULAR PAPERS 2025年第6期72卷 2868-2877页

作者： Gu, Shaomeng Zhang, Jinhui Cheng, Long Wu, Yuanqing Yanshan Univ Inst Elect Engn Qinhuangdao 066004 Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen 518107 Peoples R China

In this paper, we propose a novel generalized discrete-time variable gain active disturbance rejection control (DTVGADRC) method for the n-th order discrete-time nonlinear systems. The error-driven generalized DTVGADRC can dynamically improve the control performances, including generalized discrete-time variable gain tracking differentiator (DTVGTD), generalized discrete-time variable gain extended state observer (DTVGESO), and generalized discrete-time variable gain controller (DTVGC). Furthermore, the stability analysis of generalized DTVGADRC is performed, and the parameters in the variable gain functions are determined by the theoretical analysis. Finally, the generalized DTVGADRC method is applied to parallel teleoperated manipulators, and the experiment results are presented to illustrate effectiveness of the proposed method.

关键词： Digital TV Gain Nonlinear dynamical systems Transient analysis Sufficient conditions Stability analysis Convergence state estimation Manipulator dynamics Automation Nonlinear systems active disturbance rejection control tracking differentiator extended state observer variable gain parallel teleoperated manipulators

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Event-Triggered Optimal Parallel Tracking control for Discrete-Time Nonlinear systems

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2022年第6期52卷 3772-3784页

作者： Lu, Jingwei Wei, Qinglai Liu, Yujia Zhou, Tianmin Wang, Fei-Yue Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China

A novel event-triggered optimal tracking control (ETOTC) method is developed for discrete-time nonlinear systems in this study. For the time-invariant desired trajectory, we prove that the tracking error is asymptotically stable, and an upper bound of the real performance index can be predetermined by a design parameter. For the time-varying desired trajectory, the developed triggering condition reduces communication costs by relaxing the restriction of the asymptotic stability of the closed-loop system, and we prove that the tracking error is uniformly ultimately bounded (UUB). The developed ETOTC method entails obtaining the next state of the real system. Therefore, a parallel control approach is proposed to predict the next state by constructing a parallel system for the real system. Neural networks (NNs) and adaptive dynamic programming (ADP) techniques are utilized in the parallel control approach. Moreover, the stability analysis of the closed-loop system is shown, and the tracking error and NN weight estimation errors are proved to be UUB using the Lyapunov approach. Finally, we validate the developed ETOTC method through two simulations.

关键词： Nonlinear systems Trajectory Optimal control control systems Mathematical model Steady-state Dynamic programming Adaptive dynamic programming (ADP) event-triggered neural network (NN) nonlinear optimal control parallel control parallel system tracking control

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Development and control of an Underwater Vehicle-Manipulator System Propelled by Flexible Flippers for Grasping Marine Organisms

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2022年第4期69卷 3898-3908页

作者： Wang, Yu Cai, Mingxue Wang, Shuo Bai, Xuejian Wang, Rui Tan, Min Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Univ Hong Kong Dept Mech Engn Hong Kong Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Shanghai 200031 Peoples R China

Robots have the potential to assist and complement humans in the exploration of hostile environments. For example, underwater vehicle-manipulator systems (UVMSs) have been researched and applied in underwater operations. Motivated by the need for collecting marine organisms cultivated in shallow sea aquafarm, a lightweight and portable UVMS propelled by flexible flippers (named, F-UVMS), rather than typical propellers or jet-based propulsion systems, is investigated. To this end, integrative mechatronic design of F-UVMS is first presented, involving six flipper propulsors for generating thrust in a flapping way, a four-degree-of-freedom arm for grasping, and a certain amount of onboard sensors for environment perception. A thrust-power measurement platform is established to evaluate thrust-power relationship for different sizes and stiffness of flippers. Meanwhile, the locomotion modalities of F-UVMS are analyzed. A framework based on the nonlinear model-predictive control method is implemented to govern autonomous tracking marine organisms. The capabilities of F-UVMS are validated through a series of experiments in laboratory pool and field missions in real sea conditions.

关键词： Organisms Wires Sensors Grasping Switches Grippers Propulsion Flipper propulsor locomotion control underwater robotic grasping underwater vehicle-manipulator system (UVMS)

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A New Neuro-Optimal Nonlinear Tracking control Method via Integral Reinforcement Learning with Applications to Nuclear systems

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NEUROCOMPUTING 2022年 483卷 361-369页

作者： Zhong, Weifeng Wang, Mengxuan Wei, Qinglai Lu, Jingwei Harbin Univ Sci & Technol Harbin Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China

In this paper, a new infinite horizon optimal tracking control method for continuous-time nonlinear sys-tems is given using an actor-critic structure. This present integral reinforcement learning (IRL) method is a novelty method in adaptive dynamic programming (ADP) algorithms and an online policy iteration algorithm. For the optimal tracking problem, the cost function is defined by tracking errors. Consequently, the goal is to minimize tracking errors toward desired trajectories. Since it is hard to solve the Hamilton-Jacobi-Bellman (HJB) equation for continuous-time nonlinear systems control problems, leveraging the actor-critic architecture with neural networks (NNs) to approximate the tracking error performance index and error control law is necessary. Instead of using conventional neural networks, we employ higher-order polynomials in the whole actor-critic architecture. Finally, we apply this new neuro-optimal tracking method to the 2500MW pressurized water reactor (PWR) nuclear power plant, and simulation results are given to demonstrate the effectiveness of the developed method.(c) 2022 Published by Elsevier B.V.

关键词： Integral reinforcement learning Nuclear power reactor Nonlinear system Optimal tracking control Neural networks

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ESTNet: Embedded Spatial-Temporal Network for Modeling Traffic Flow Dynamics

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2022年第10期23卷 19201-19212页

作者： Luo, Guiyang Zhang, Hui Yuan, Quan Li, Jinglin Wang, Fei-Yue Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100088 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Beijing Univ Posts & Telecommun Sci & Technol Commun Networks Lab Beijing 100088 Peoples R China State Key Lab Networking & Switching Technol Shijiahznang 050081 Hebei Peoples R China Qingdao Acad Intelligent Ind Innovat Ctr Parallel Vis Qingdao 266000 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa Macao Peoples R China

Accurate spatial-temporal prediction is a fundamental building block of many real-world applications such as traffic scheduling and management, environment policy making, and public safety. This problem is still challenging due to nonlinear, complicated, and dynamic spatial-temporal dependencies. To address these challenges, we propose a novel embedded spatial-temporal network (ESTNet), which extracts efficient features to model the dynamic correlations and then exploits three-dimension convolution to synchronously model the spatial-temporal dependencies. Specifically, we propose multi-range graph convolution networks for extracting multi-scale static features from the fine-grained road network. Meanwhile, dynamic features are extracted from real-time traffic using a gated recurrent unit network. These features can be applied to identify the dynamic and flexible correlations among sensors and make it possible to exploit a three-dimension convolution unit (3DCon) to simultaneously model the spatial-temporal dependencies. Furthermore, we propose a residual network by stacking multiple 3DCon to capture the nonlinear and complicated dependencies. The effectiveness and superiority of ESTNet are verified on two real-world datasets, and experiments show ESTNet outperforms the state-of-the-art with a significant margin. The code and models will be publicly available.

关键词： Roads Correlation Feature extraction Sensors Deep learning Convolution Sensor phenomena and characterization Traffic forecasting graph convolutional network spatial-temporal networks

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Monte Carlo-based reinforcement learning control for unmanned aerial vehicle systems

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NEUROCOMPUTING 2022年 507卷 282-291页

作者： Wei, Qinglai Yang, Zesheng Su, Huaizhong Wang, Lijian Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China Beijing Aeronaut Technol Res Inst COMAC Beijing 102211 Peoples R China

In this paper, a new data-driven reinforcement learning method based on Monte Carlo simulation is developed to solve the optimal control problem of unmanned aerial vehicle (UAV) systems. Based on the data which are generated by Monte Carlo simulation, neural network (NN) is used to construct the dynamics of the UAV system with unknown disturbances, where the mathematical model of the UAV sys-tem is unnecessary. An effective iterative framework of action and critic is constructed to obtain the opti-mal control law. The convergence property is developed to guarantee that the iterative performance cost function converges to a finite neighborhood of the optimal performance cost function. Finally, numerical results are given to illustrate the effectiveness of the developed method.(c) 2022 Published by Elsevier B.V.

关键词： Reinforcement learning Adaptive dynamic programming (ADP) UAV control Monte Carlo simulation Neural networks

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A Novel Iterative Adaptive Critic Design for Smart Home Energy systems With Solar Energy

A Novel Iterative Adaptive Critic Design for Smart Home Ener...

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Chinese Automation Congress (CAC)

作者： Zehua Liao Qinglai Wei Hongyang Li School of Artificial Intelligence University of Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Beijing China

As a distributed energy storage system, smart home energy (SHE) system can be used to reduce the consumption cost of household users. Aiming at the optimal control SHE systems, a novel multi-iteration adaptive dynamic programming (MIADP) method is constructed. The present algorithm introduces three iterations, namely, exterior iteration, local iteration and interior iteration, to ensure that the iterative Q-function converges to the optimal, and thus the optimal energy storage control scheme of the SHE system with solar energy is obtained. Finally, the present MIADP algorithm is illustrated by the experimental results.

关键词： Renewable energy sources Costs Adaptive systems Heuristic algorithms Optimal control Solar energy Smart homes

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Artificial Identification: A Novel Privacy Framework for Federated Learning Based on Blockchain

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2023年第6期10卷 3576-3585页

作者： Ouyang, Liwei Wang, Fei-Yue Tian, Yonglin Jia, Xiaofeng Qi, Hongwei Wang, Ge Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China Univ Sci & Technol China Dept Automat Hefei 230027 Anhui Peoples R China Beijing Big Data Ctr Dept Data Management Beijing 101100 Peoples R China Datang Beijing Technol Co Ltd Beijing 100192 Peoples R China

To provide off-chain federations with complete privacy services to realize on-chain federated learning (FL), this article proposes a novel privacy framework for FL based on blockchain and smart contracts, named Artificial Identification. It consists of two modules: private peer-to-peer identification and private FL, using two scalable smart contracts to manage the identification and learning process, respectively. Based on Ethereum and interplenary file systems (IPFS), we implement our framework and comprehensively analyze its performance. Experiments show that the proposed framework has acceptable collaboration costs and offers advantages in terms of privacy, security, and decentralization. Furthermore, combined with radio frequency identification (RFID) technology, the framework has the potential to realize automatic on-chain identification and autonomous FL of machine clusters composed of Internet of Things (IoT) devices or distributed participants.

关键词： Blockchains Smart contracts Radiofrequency identification Privacy Collaboration Iron Security Blockchain federated learning (FL) private identification radio frequency identification (RFID) smart contracts

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Hydrodynamic Analysis and Verification of an Innovative Whale Shark-like Underwater Glider

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Journal of Bionic Engineering 2020年第1期17卷 123-133页

作者： Huijie Dong Zhengxing Wu Min Tan Junzhi Yu State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceBIC-ESATCollege of EngineeringPeking UniversityBeijing 100871China

This paper presents an innovative design for a biomimetic whale shark-like underwater glider aiming at the combination of high maneuverability and long *** a hybrid of the underwater glider and the robotic fish,its pectoral fins and tail can serve as not only the external control surfaces for attitude regulation during gliding but also the propellers for agile fish-like swimming *** verify the gliding capability of the whale shark-like glider and prepare for future dynamic analysis,the hydrodynamic coefficients,including drag,lift,sliding force,and corresponding moments are estimated through computational fluid dynamics *** addition,the hydrodynamic analyses of the proposed glider and an equivalent conventional glider during steady gliding motion are executed for *** experiments are performed to verify the downward gliding *** results reveal that the whale shark-like glider has less drag as well as higher lift-to-drag ratio and a markable gliding capability in *** may offer important inspiration for improving the gliding efficiency and performance of an underwater glider in biomimetic shape design.

关键词： biomimetic robot CFD hydrodynamic analysis underwater glider

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Double Domain Guided Real-Time Low-Light Image Enhancement for Ultra-High-Definition Transportation Surveillance

arXiv

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arXiv 2023年

作者： Qu, Jingxiang Liu, Ryan Wen Gao, Yuan Guo, Yu Zhu, Fenghua Wang, Fei-Yue The School of Navigation The State Key Laboratory of Maritime Technology and Safety Wuhan University of Technology Wuhan430063 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

Real-time transportation surveillance is an essential part of the intelligent transportation system (ITS). However, images captured under low-light conditions often suffer the poor visibility with types of degradation, such as noise interference and vague edge features, etc. With the development of imaging devices, the quality of the visual surveillance data is continually increasing, like 2K and 4K, which has more strict requirements on the efficiency of image processing. To satisfy the requirements on both enhancement quality and computational speed, this paper proposes a double domain guided real-time low-light image enhancement network (DDNet) for ultra-high-definition (UHD) transportation surveillance. Specifically, we design an encoder-decoder structure as the main architecture of the learning network. In particular, the enhancement processing is divided into two subtasks (i.e., color enhancement and gradient enhancement) via the proposed coarse enhancement module (CEM) and LoG-based gradient enhancement module (GEM), which are embedded in the encoder-decoder structure. It enables the network to enhance the color and edge features simultaneously. Through the decomposition and reconstruction on both color and gradient domains, our DDNet can restore the detailed feature information concealed by the darkness with better visual quality and efficiency. The evaluation experiments on standard and transportation-related datasets demonstrate that our DDNet provides superior enhancement quality and efficiency compared with the state-of-the-art methods. Besides, the object detection and scene segmentation experiments indicate the practical benefits for higher-level image analysis under low-light environments in ITS. The source code is available at https://***/QuJX/DDNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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