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5th International Conference on Cognitive systems and Signal Processing, ICCSIP 2020

作者： Zhang, Xiaodong Ding, Pengchao Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Application Beijing Institute of Spacecraft System Engineering CAST Beijing100086 China Institute of Technology Harbin Heilongjiang150001 China

ISBN: (纸本)9789811623356

Hole-peg assembly using robot is widely used to validate the abilities of autonomous assembly task. Currently, the autonomous assembly is mainly depended on the high precision of position, force measurement and the compliant control method. The assembly process is complicated and the ability in unknown situations is relatively low. In this paper, a kind of assembly strategy based on deep reinforcement learning is proposed using the TD3 reinforcement learning algorithm based on DDPG and an adaptive annealing guide is added into the exploration process which greatly accelerates the convergence rate of deep reinforcement learning. The assembly task can be finished by the intelligent agent based on the measurement information of force-moment and the pose. In this paper, the training and verification of assembly verification is realized on the V-rep simulation platform and the UR5 manipulator. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Forecasting-Aided Voltage Estimation for Distribution Network Considering the Integrated Management of Virtual Power Plants Platform

Forecasting-Aided Voltage Estimation for Distribution Networ...

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IEEE Student Conference on Electric Machines and systems

作者： Yujian Jia Zhao Liu Changtao Kan Yue Yu Changgang Li State Grid Jinan Power Supply Company Jinan China Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

ISBN: (数字)9798350366945

ISBN: (纸本)9798350366952

As more distributed energy sources integrate into distribution networks, issues such as voltage fluctuations and reduced observability become increasingly prominent. Therefore, this paper proposes an algorithm for forecasting-aided state estimation (FASE) in virtual power plants' integrated operation management platform. Firstly, a Bidirectional Long Short-Term Memory (BiLSTM)-Extended Kalman Filter (EKF) algorithm for distribution network FASE is introduced. Secondly, the integrated management of virtual power plant platforms (IM-VPPP) enhances the observability of distribution networks. To evaluate the performance of the proposed method, simulations are conducted using the IEEE 33-bus standard distribution system. Results demonstrate significant advantages of the algorithm in terms of accuracy and robustness.

关键词： Voltage measurement Voltage fluctuations Noise Distribution networks Virtual power plants Robustness Kalman filters state estimation Observability Standards

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Relative Roughness Measurement Based Real-Time Speed Planning for Autonomous Vehicles on Rugged Road

Relative Roughness Measurement Based Real-Time Speed Plannin...

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IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Liang Wang Tianwei Niu Shoukun Wang Shuai Wang Junzheng Wang Key Laboratory of Drive and Control of Servo Motion System School of Automation Beijing Institute of Technology Beijing China The State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology School of Automation Beijing Institute of Technology Beijing China

In order to guarantee autonomous vehicles' autonomy, mobility, and ride quality in rugged environments, a real-time speed planning method based on the time-frequency transformation of terrain characteristics is designed to achieve adaptive speed planning of autonomous vehicles in rough ground. On the one hand, the vertical profile of the lidar's point cloud data is converted from the time domain to the frequency domain in real time, and the integrated area of the sub-frequency range in the frequency domain is chosen as the relative roughness quantification value to realize the roughness quantification under various terrains. On the other hand, to model the relationship between vehicle speed and relative roughness, iterative search is utilized to create a speed and roughness model, and sliding windows are employed to update the roughness to achieve continuous mapping between speed and roughness. Ultimately, a number of tests were conducted on various rough roads using the oil exploration vehicle EV-56 as the study object. The experimental results show that the proposed method can identify the terrain roughness changes under complex terrain and change their speed within 0.2 m accuracy.

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Low-Carbon Dispatch Strategy for Virtual Power Plant Based on Bagging Ensemble Learning Forecasting Method

Low-Carbon Dispatch Strategy for Virtual Power Plant Based o...

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IEEE Student Conference on Electric Machines and systems

作者： Guang Li Xin Dong Li Li Yue Yu Changgang Li State Grid Jinan Power Supply Company Jinan China Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

ISBN: (数字)9798350366945

ISBN: (纸本)9798350366952

Virtual power plants (VPPs) struggle with peak-shaving due to unreliable distributed generation (DG) data and the intermittent nature of wind and solar power, impacting carbon emissions. This paper proposes a low-carbon dispatch strategy for VPPs based on a Bagging ensemble forecasting model. Firstly, a VPP dispatch framework based on DG output and load forecasting is established, and a Bagging ensemble forecasting model with feature selection is proposed. Secondly, the control characteristics of flexible industrial, commercial, and residential loads are analyzed to establish a control cost model, which is then integrated with the balanced relationship between wind, photovoltaic, and energy storage, as well as the carbon emission mechanism, to formulate a low-carbon dispatch model. Finally, the simulation results show that the proposed strategy can improve the accuracy of DG output and load forecasting in complex feature environments while realizing the interaction between source-grid-load and reducing the generation and operation costs of the grid.

关键词： Analytical models Costs Carbon dioxide Predictive models Wind power generation Virtual power plants Forecasting Wind forecasting Bagging Load modeling

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Automated Adherent Cell Injection Method Based on Double-micropipettes Operation

Automated Adherent Cell Injection Method Based on Double-mic...

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第42届中国控制会议

作者： Zhaotong Chu Linxuan Cai MaoSheng Cui Zuqi Wang Yidi Zhang Minghui Li Qili Zhao Mingzhu Sun Xin Zhao Yaowei Liu Institute of Robotics and Automatic Information System The Tianjin Key Laboratory of Intelligent RoboticsNankai University Tianjin Academy of Agriculture Science Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University

Microinjection of adherent cells is a technique of inserting glass pipette into adherent cells and injecting few exogenous substances into adherent cells,which is widely used in the field of cell *** in this field usually inject adherent cells manually,however,the microinjection operation is complicated,which requires a lot of energy and takes a lot of *** existing automated microinjection method only uses one injection micropipette to inject,compared with the manual injection method,the microinjection process is consistent not optimized,and the injection efficiency cannot be greatly *** this paper,an automated adherent cell injection method based on double-micropipettes operation is ***,the adherent cell injection process based on double-micropipettes operation is ***,this paper solves the key technologies of automated injection of adherent cells based on double-micropipettes operation were introduced,including adherent cell detection,injection path planning and double-micropipettes ***,the experiment of adherent cell microinjection based on double-micropipettes operation,the operation of two micropipettes was carried *** experimental results show that the success rate of injection can reach 60% and the injection speed of this method can reach 35 cells/minute,which is 75% higher than that of manual injection with single pipette(20 cells/minute) and 40% higher than that of automated adherent cell injection with single pipette(25 cells/minute).

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MUP-LIO: Mapping Uncertainty-aware Point-wise Lidar Inertial Odometry

MUP-LIO: Mapping Uncertainty-aware Point-wise Lidar Inertial...

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IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Hekai Yao Xuetao Zhang Gang Sun Yisha Liu Xuebo Zhang Yan Zhuang Intelligent Robotic Laboratory School of Artificial Intelligence School of Control Science and Engineering Dalian University of Technology Dalian China School of Information Science and Technology Dalian Maritime University Dalian China College of Artificial Intelligence The Institute of Robotics and Automatic Information System (IRAIS) Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper proposes a mapping uncertainty-aware point-wise Lidar Inertial Odometry (LIO), which synthesizes the point-wise point-to-plane match and map refreshment into a probabilistic model. As a result, it can address the issue of mismatching during point registration and remove in-frame motion distortion of Lidar sensors. Specifically, the uncertainty-aware map is designed to embody the uncertainty of map geometric features (points and planes), which comes from the Lidar point measurement and pose estimation. Then the map can be modeled in a probabilistic form. In addition, the proposed framework refreshes map at each Lidar point measurement to timely revise geometric features and provide non-delayed map. On the basis, the probabilistic point-to-plane match method is designed to seek a corresponding plane for each Lidar point in point registration, which can enhance the effectiveness of match and provide adaptive observation noises for more accurate state estimation. Comparative experiments on various public datasets are conducted to demonstrate the superior performance of the proposed framework in terms of higher accuracy and better robustness.

关键词： Laser radar Accuracy Uncertainty Probabilistic logic Distortion Robustness Sensors Odometry Distortion measurement state estimation

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A NOVEL PID SELF-TUNING METHOD BASED ON CLOSED-LOOP IDENTIFICATION USED IN NUCLEAR POWER PLANTS 29

A NOVEL PID SELF-TUNING METHOD BASED ON CLOSED-LOOP IDENTIFI...

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2022 29th International Conference on Nuclear Engineering, ICONE 2022

作者： Zhang, Pengbin Xing, Kunpeng Zou, Tao Yang, Zhijia Wang, Jingyang Wang, Hongrui State Key Laboratory of Robotics Key Laboratory of Networked Control System Institutes for Robotics and Intelligent Manufacturing Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China China Nuclear Power Engineering Co. Ltd Shenzhen China College of Mechanical and Electrical Engineering Guangzhou University Guangzhou China Key Laboratory of Networked Control System Institutes for Robotics and Intelligent Manufacturing Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9780791886472

Nuclear power is one of several significant clean energy and its production capacity is increasing rapidly. This paper proposed a novel PID self-tuning method based on closed-loop identification used in nuclear power plants to deal with the turbine's load tracking problem. A setpoint-shifting method is adopted to achieve the informativity of the closed-loop system, and the PID parameters are designed through an optimization method. For validating the proposed method, this paper modeled a pressurized-water reactor (PWR) nuclear power plant, a complex system consisting of three circuits, through the first principle. The primary circuit contains three main devices: a nuclear reactor, a pressurizer, and a steam generator. The models of these three devices are provided by previous literature separately. The whole model is generated through mass and energy balance. This mechanism model is used as the plant model, while the identified model is used as the control model. The initial PID parameters are set through open-loop identification using an external excitation. Then the proposed method works online to track the turbine's load in time. The effectiveness of the proposed method is illustrated by simulations. Copyright © 2022 by ASME.

关键词： Nuclear power plants

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Dynamic Decentralized Event-Triggered Tracking Control of Continuous Stirred Tank Reactor systems

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 16632-16643页

作者： Xiong Yang Jianling Meng Qinglai Wei Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin University Tianjin China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

We develop a dynamic decentralized event-triggered tracking control strategy for cascade interconnected continuous stirred tank reactor (CSTR) systems subject to asymmetric input limits. Initially, we construct auxiliary augmented subsystems related to the cascade interconnected CSTR systems. Then, by introducing modified nonquadratic cost functions for the auxiliary augmented subsystems, we convert the decentralized constrained tracking control problem into an array of unconstrained optimal regulation problems. After that, with the construction of dynamic event-triggering mechanisms, we propose the event-triggered Hamilton-Jacobi-Bellman equations (ET-HJBEs) associated with the transformed optimal regulation problems. To approximately solve the ET-HJBEs, we design critic neural networks (CNNs) in the adaptive dynamic programming framework with the CNNs’ weights being updated through the gradient descent method. Furthermore, we use Lyapunov method to prove that the CNNs’ weight estimation errors and the tracking error are stable in the sense of uniform ultimate boundedness. Finally, simulation results of the three-reactor cascade interconnected CSTR systems are provided to validate the present dynamic decentralized event-triggered tracking control scheme. Note to Practitioners—Cascade interconnected continuous stirred tank reactor (CSTR) systems widely emerge in chemical industries. They are sensitive to variations in the reactant concentration and the reaction temperature. Therefore, it is necessary to regulate the concentration of reactants and the reaction temperature via designing an online tracking controller for cascade interconnected CSTR systems. However, the heat transfer limitations and the computational cost must be considered for designing such a tracking controller, which leads to big challenges. This article designs an online-learning decentralized tracking controller in a dynamic event-triggering mechanism for the cascade interconnected CSTR systems w

关键词： Chemical reactions Inductors Event detection Coolants Optimal control Interconnected systems Dynamic programming Temperature sensors Regulation Nonlinear systems

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RESEARCH ON THE PID PARAMETER SELF-TUNING OF THE PRESSURIZER CONTROL system OF NUCLEAR POWER PLANT 29

RESEARCH ON THE PID PARAMETER SELF-TUNING OF THE PRESSURIZER...

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2022 29th International Conference on Nuclear Engineering, ICONE 2022

作者： Wang, Hongrui Xing, Kunpeng Zou, Tao Yang, Zhijia Wang, Jingyang Zhang, Pengbin State Key Laboratory of Robotics Key Laboratory of Networked Control System Institutes for Robotics and Intelligent Manufacturing Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China China Nuclear Power Engineering Co. Ltd Shenzhen China College of Mechanical and Electrical Engineering Guangzhou University Guangzhou China Key Laboratory of Networked Control System Institutes for Robotics and Intelligent Manufacturing Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9780791886373

The pressurizer is an essential part of the safe operation of the nuclear power plant. It contains both the steam phase and the liquid phase, which requires simultaneous control of the pressure and water level, and the control system is strongly coupled. When using the PID control method, it is difficult to tune the parameters, and the difficulty is exacerbated when the set value fluctuates. This paper aims at the multi-input and multioutput pressurizer control system, based on the particle swarm optimization to realize the PID controller parameters self-tuning, adopting the time and absolute error integral as the fitness function to evaluate the merits of the PID parameters, and constantly iterating to update and obtain the PID parameters that better satisfy the current working conditions. For coupled pressurizer control systems, the PID parameter tuning method using particle swarm optimization algorithms can give a group of PID controller parameters that make the system operation stable despite the frequent fluctuation of the set value, and compared with the traditional PID controller parameter tuning method, this method can realize the PID parameter self-tuning, eliminating the trouble of manual tuning. Copyright © 2022 by ASME.

关键词： Nuclear power plants

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Distributed Cooperative Decision-Making of Cross-Domain Swarm

Distributed Cooperative Decision-Making of Cross-Domain Swar...

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2023 China Automation Congress, CAC 2023

作者： Liu, Xingyu Yuwen, Cheng Zhang, Wangcheng Sun, Mingyang Jian, Ding Zhen, Ziyang State Key Laboratory of Mechanics and Control for Aerospace Structures China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China CASIC Research Institute of Intelligent Decision Engineering Wuhan China UAV System Company Limited Aviation Industry Corporation of China Chengdu China

ISBN: (纸本)9798350303759

This paper investigates a cooperative decision-making problem of the cross-domain swarm system involving unmanned surface vehicles (USVs) and unmanned aerial vehicles (UAVs). Different from the distributed optimization of multi-agent systems, the cooperative decision-making problem considers the payoff functions that depend on the states of all agents. Each agent can only adjust its own state. To address the cooperative decision-making problem, a two-time scale system is constructed, where the fast model includes an average consensus for gradient estimation of the global function and the slow model incorporates a feedback control of all USVs and UAVs. By the Lyapunov analysis, the optimal solution is globally exponentially stable. Finally, a cooperative encirclement task is utilized to verify the effectiveness of the proposed algorithm. © 2023 IEEE.

关键词： Unmanned surface vehicles

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