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PRECOM: A Parallel Recommendation Engine for control, Operations, and management on Congested Urban Traffic Networks

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2022年第7期23卷 7332-7342页

作者： Jin, Junchen Rong, Dingding Pang, Yuqi Zhu, Fenghua Guo, Haifeng Ma, Xiaoliang Wang, Fei-Yue Enjoyor Co Ltd Hangzhou 310030 Peoples R China KTH Royal Inst Technol Dept Civil & Architecture Engn S-10044 Stockholm Sweden Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Zhejiang Univ Technol Coll Informat Engn Hangzhou 310013 Peoples R China

This paper proposes a parallel recommendation engine, PRECOM, for traffic control operations to mitigate congestion of road traffic in the metropolitan area. The recommendation engine can provide, in real-time, effective and optimal control plans to traffic engineers, who are responsible for manually calibrating traffic signal plans especially when a road network suffers from heavy congestion due to disruptive events. With the idea of incorporating expert knowledge in the operation loop, the PRECOM system is designed to include three conceptual components: an artificial system model, a computational experiment module, and a parallel execution module. Meanwhile, three essential algorithmic steps are implemented in the recommendation engine: a candidate generator based on a graph model, a spatiotemporal ranker, and a context-aware re-ranker. The PRECOM system has been deployed in the city of Hangzhou, China, through both offline and online evaluation. The experimental results are promising, and prove that the recommendation system can provide effective support to the current human-in-the-loop control scheme in the practice of traffic control, operations, and management.

关键词： Spatial-temporal recommender system urban traffic control parallel traffic management human-in-the-loop system

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Dynamic deadline constrained multi-objective workflow scheduling in multi-cloud environments

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EXPERT systems WITH APPLICATIONS 2024年 258卷

作者： Cai, Xingjuan Zhang, Yan Li, Mengxia Wu, Linjie Zhang, Wensheng Chen, Jinjun Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan 030024 Shanxi Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Swinburne Univ Technol Dept Comp Sci & Software Engn Melbourne Australia

Workflow scheduling becomes difficult and demanding in multi-cloud systems because of the variety of billing models and resource kinds, as well as the susceptibility of processes to time limitations. In which the execution duration of the workflow needs to be flexibly adjusted according to the urgency or otherwise of the real situation, in this work, we model the workflow scheduling problem with deadline constraints as a dynamically constrained multi-objective optimization problem (DCMOP), where the disruption rate of the special cloud resources and the failure probability in different cloud environments are expressed as the disruption probability of the workflow in a comprehensive manner while considering the execution time and cost. Dynamics arise from price changes for specialized cloud resources and changes in the execution duration of workflows. In addition, an algorithm for dynamically constrained multi-objective optimization (DC-MOEADPDS) is proposed in this paper. The algorithm based on two-population synergy as well as diversity selection. The auxiliary population ignores constraint limitations to help the main population speed up convergence, and diversity selection enables the auxiliary population to have better diversity to assist the general public in looking for more viable areas. Through dynamic constrained workflow simulation experiments in a multi-cloud environment, our algorithm reduces the execution time by an average of 13.29%, the cost by an average of 32.26%, and the interruption probability by 56.75%. In addition, our algorithm outperforms other algorithms in experiments on a dynamically constrained benchmark set.

关键词： Multi-cloud Dynamically constrained multi-objective optimization Workflow scheduling Outage rates Deadline constraints

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Active Disturbance Rejection control for a Fluid-Driven Hand Rehabilitation Device

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2021年第2期26卷 841-853页

作者： Li, Houcheng Cheng, Long Li, Zhengwei Xue, Wenchao 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 Chinese Acad Sci Acad Math & Syst Sci Inst Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China

This article proposes a new hand rehabilitation device to help therapists repetitively perform hand group stretching training for poststroke patients. The actuator of the proposed device is made of elastomeric materials, driven by fluid pressure, and put onto the surface of a glove. Compared to the traditional rigid exoskeletons for hand rehabilitation, the proposed device is soft, lightweight, and low cost. Because the exact and explicit model is hard to be built for the proposed device and the unconscious tremors occurred during hand rehabilitation, the active disturbance rejection control (ADRC) algorithm, based on the extended state observer (ESO), is adopted to achieve the control purpose. This article analyzes the transient and steady-state performances of the controller based on some reasonable assumptions, and shows its better disturbance rejection ability compared to the widely used proportional-integral-differential control method. In addition, the ADRC algorithm with the reduced-order extended state observer (RESO) has been studied as well. The parameters of the RESO can be obtained by the parameters of the previously determined ESO instead of the trial and error approach, which can ensure the control performance of the ADRC with the RESO does not degrade. Finally, clinical tests have been conducted to verify the functional correctness of the proposed device, and these theoretical results have also been verified by experiments and comparisons.

关键词： Actuators Exoskeletons Performance evaluation Training IEEE transactions Mechatronics Active disturbance rejection control extended state observer extension motion hand rehabilitation parameter selection transient steady-state performance

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

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NEUROCOMPUTING 2022年第0期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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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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