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Chinese Automation Congress (CAC)

作者： Bomeng Wang Junzheng Wang Shoukun Wang Jiehao Li Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

In the research of wheel-legged robots, trajectory tracking control is the inevitable requirement. In this paper, a trajectory tracking control scheme based on model predictive control for the parallel mechanism of six wheel-legged robot is proposed. A dynamic model was employed to reduce the amount of calculations for planning and control. In the model, the dynamic nonlinear constraints such as the tire slip rate and the roll caused by the lateral acceleration need to be considered. Finally, the control method is verified in the dynamic model. The simulation results show that all dynamic constraints can be maintained within a given interval and the control method has obvious tracking effect improvement.

关键词： Manganese

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Probabilistic Plan Recognition Under Temporal Logic Tasks

Probabilistic Plan Recognition Under Temporal Logic Tasks

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第三十八届中国控制会议

作者： Wentao Yu Hao Fang Daiying Tian School of Automation Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

In this paper, we consider the plan recognition problem in the real-time strategy game. A probabilistic plan recognition algorithm is proposed to predict the future goals and identify the temporal logic tasks of the non-cooperative agent based on the observations. In order to model the temporal logic tasks, the plan library is composed of the Finite Transition System and Nondeterministic B ¨uchi Automation. Specially, we provide a unified framework to combine the plan recognition and the planning, and propose the probability calculation algorithm to calculate the posterior probability distribution of the goals and tasks. Finally, we verify the effectiveness of the proposed algorithm by the compared simulations.

关键词： Probabilistic Plan Recognition Linear Temporal Logic Büchi Automation Goal Prediction

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Impedance Modeling of PMSG Wind Farms from the Machine Side DC-Port with Outer Power Loop

Impedance Modeling of PMSG Wind Farms from the Machine Side ...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Jiajun Wang Xiaozhong Liao Bin Liu Xi Chen Xiangdong Liu Xiaoqin Lian Zhen Li School of Automation and Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing 100081 China GEIRI North America CA 95134 USA Beijing Technology and Business University Beijing 100048 China

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

Due to the advantages of large unit capacity, high efficiency and high reliability, direct-drive permanent magnet synchronous generators (PMSGs) wind turbines (WTs) have been widely used in the offshore wind power generation. The power loop controller aims to maintain the DC-Bus voltage of each PMSG-WT is equal to the others, guaranteeing that multiple WTs can be connected in DC serial parallel collection. In this paper, the DC-Port sequence impedance model of the PMSG-WT is established based on the harmonic linearization method, which takes the outer power loop into consideration. The impedance model is further verified by point-by-point scanning in MATLAB/Simulink. The proposed DC-Port impedance model of the PMSG-WT facilitates the establishment of offshore direct-drive wind farms, and is of great significance for analyzing and improving the system stability.

关键词： Impedance Mathematical model Harmonic analysis Wind farms Generators Power system stability Topology

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Learning Hidden Markov Models for Linear Gaussian Systems with Applications to Event-based State Estimation

arXiv

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arXiv 2020年

作者： Zheng, Kaikai Shi, Dawei Shi, Ling State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Kowloon Hong Kong

This work attempts to approximate a linear Gaussian system with a finite-state hidden Markov model (HMM), which is found useful in solving sophisticated event-based state estimation problems. An indirect modeling approach is developed, wherein a state space model (SSM) is firstly identified for a Gaussian system and the SSM is then used as an emulator for learning an HMM. In the proposed method, the training data for the HMM are obtained from the data generated by the SSM through building a quantization mapping. Parameter learning algorithms are designed to learn the parameters of the HMM, through exploiting the periodical structural characteristics of the HMM. The convergence and asymptotic properties of the proposed algorithms are analyzed. The HMM learned using the proposed algorithms is applied to event-triggered state estimation, and numerical results on model learning and state estimation demonstrate the validity of the proposed algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Hidden Markov models

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Distributed stochastic proximal algorithm with random reshuffling for non-smooth finite-sum optimization

arXiv

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arXiv 2021年

作者： Jiang, Xia Zeng, Xianlin Sun, Jian Chen, Jie Xie, Lihua Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China School of Electronic and Information Engineering Tongji University Shanghai200082 China Ministry of Education Beijing100081 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore

The non-smooth finite-sum minimization is a fundamental problem in machine learning. This paper develops a distributed stochastic proximal-gradient algorithm with random reshuffling to solve the finite-sum minimization over time-varying multi-agent networks. The objective function is a sum of differentiable convex functions and non-smooth regularization. Each agent in the network updates local variables by local information exchange and cooperates to seek an optimal solution. We prove that local variable estimates generated by the proposed algorithm achieve consensus and are attracted to a neighborhood of the optimal solution with an (formula presented) convergence rate, where T is the total number of iterations. Finally, some comparative simulations are provided to verify the convergence performance of the proposed algorithm. © 2021, CC BY.

关键词： Optimal systems

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Data Augment Using Deep Convolutional Generative Adversarial Networks for Transient Stability Assessment of Power Systems

Data Augment Using Deep Convolutional Generative Adversarial...

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Chinese control Conference (CCC)

作者： Jiamin Li Hongying Yang Liping Yan Zonghan Li Daowei Liu Yuanqing Xia Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing P. R. China China Electric Power Research Institute Beijing P. R. China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

Real-time and accurate transient stability assessment (TSA) is essential for planning, operation and control of power systems. As a data-driven technology, deep learning method plays an important role in TSA. Nevertheless, the fact that instability situations rarely occur would lead to a challenging class-imbalanced issue, which brings great difficulties to the deep learning methods. Besides, feature extraction from high dimensional input data and transient stability classification seem extremely difficult for conventional classification methods. To address these problems, this paper develops a class-imbalanced TSA method by combining nonlinear data synthesis method with the deep learning classification model. Firstly, deep convolutional generative adversarial network (DCGAN) is conducted to generate unstable instances based on the existing samples to balance the proportion of different classes. Furthermore, the convolutional neural network (CNN) is utilized to extract the nonlinear mapping relationship between the disturbance features and the stability category and realize TSA. Finally, the IEEE 10-machine, 39-bus New England system is utilized to verify the validity and effectiveness of the proposed method.

关键词： Power system stability Feature extraction Generative adversarial networks Transient analysis Machine learning Training

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Shaping effect of phase-locked loop on impedance characteristics of PMSG wind turbines connected to weak-grid

Shaping effect of phase-locked loop on impedance characteris...

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2020 IEEE Power and Energy Society General Meeting, PESGM 2020

作者： Wan, Feiya Li, Zhen Liu, Bin Li, Guanghui Dong, Xiaoliang Chen, Xi Yu, Samson S. Oo, Amanullah M. T. Shan, Zhenyu Liu, Xiangdong Beijing Institute of Technology School of Automation and Key Laboratory for Intelligent Control Decision on Complex Systems Beijing China China Electric Power Research Institute Beijing China State Grid Corporation of China and North China Branch of State Grid Corporation of China Beijing China GEIRI North America United States Deakin University School of Engineering 75 Pigdon Rd Waurn Ponds Victoria Australia North China University of Technology School of Electrical and Control Engineering Beijing China

ISBN: (纸本)9781728155081

With the booming installed capacity of permanent magnet synchronous generator (PMSG) for wind energy generation, the grid becomes weaker and the oscillation events are frequently observed in weak-grid-tied large-scale grid-connected PMSG wind farms. Although the impedance-based method is effective to analyze the stability of grid-connected systems, the mathematical relationship between external impedance characteristics and the internal controllers has not been quantitatively analyzed in depth, which is beneficial to the controller design. This paper studies the influence of phase-locked loop (PLL) on the impedance characteristics based on the analytical sequence impedance model of PMSG with typical topology and control structure. The shaping effect of PLL on the impedance characteristics is described so as to clearly describe the relationship between the external impedance characteristics of the wind turbines and the internal controller. Finally, the stability analysis of the grid-connected PMSG wind turbine system is provided in Matlab and simulation results are given to verify the oscillating mechanism caused by PLL. © 2020 IEEE.

关键词： Phase locked loops

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Multi-agent System Motion Planning under Temporal Logic Specifications and control Barrier Function

Multi-agent System Motion Planning under Temporal Logic Spec...

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第三十九届中国控制会议

作者： Xinyuan Huang Li Li Jie Chen IEEE IFAC Chinese Academy of Engineering Tongji University the State Key Laboratory of Intelligent Control and Decision of Complex Systems the Chinese Association of Automation the Chinese Artificial Intelligence Society College of Electronics and Information Engineering Tongji University Shanghai Reserach Institute for Intelligent Autonomous Systems

In this paper, we provide a novel scheme to solve the motion planning problem of multi-agent systems under highlevel task specifications. First, linear temporal logic is applied to express the global task specification. Then an efficient and decentralization algorithm is proposed to decompose it into local tasks. Finally, we use control barrier function to synthesize the local controller for each agent under the LTL motion plan safety constraint. Simulation results prove the effectiveness and efficiency of our proposed method.

关键词： temporal logic multi-agent system formal methods control barrier function

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Glucose Regulation for Subjects with Type 1 Diabetes Using Active Disturbance Rejection control

Glucose Regulation for Subjects with Type 1 Diabetes Using A...

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第三十八届中国控制会议

作者： Deheng Cai Jiliang Song Junzheng Wang Dawei Shi State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology

In artificial pancreas(AP) systems, control algorithms play an important role in the blood glucose(BG) *** ability of active disturbance rejection control(ADRC), which can deal with various disturbances, enables it to become a promising approach in the control algorithm design for BG regulation when facing many disturbances(e.g. glucose fluctuation caused by meal intakes and exercises). In this paper, an ADRC controller is proposed for the AP systems. To ensure the safety,the insulin on board(IOB) constraints and the insulin delivery constraints are introduced to the ADRC controller. The controller is evaluated on the FDA-accepted UVA/Padova T1 DM simulator with the 10-adult cohort. For both announced and unannounced meals, the controller achieves satisfactory performance in terms of mean glucose(139.9 mg/dl for announced meals, 157.9 mg/dl for unannounced meals) and percentage time in [70, 180] mg/dl(91.5% for announced meal, 68.9% for unannounced meals)without having the risk of hypoglycemia.

关键词： Glucose Regulation Type 1 Diabetes Artificial Pancreas Active Disturbance Rejection control

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Optimal Blood Glucose Prediction based on Intermittent Data from Wearable Glucose Monitoring Sensors

Optimal Blood Glucose Prediction based on Intermittent Data ...

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第三十八届中国控制会议

作者： Lijun Hou Huipeng Zhang Junzheng Wang Dawei Shi State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology

Blood glucose prediction is to predict the glucose trend over time based on historical glucose data, and it plays a crucial role in the closed-loop control of artificial pancreas, which can reduce the risk of complications by regulating insulin dose and injection time. This paper proposes a Kalman-filter-based glucose prediction method through minimizing the mean square prediction error, which assumes that the data is sampled every 15 min from a wearable flash glucose monitoring sensor. This method calculates glucose estimates every 5 min and provides glucose predictions for the next 30 min. The method is evaluated on in-silico data generated from the 10-adult cohort of the US FDA-accepted UVA/Padova T1 DM simulator. The predicted results are compared with CGM data with 5-min sample-period through multiple metrics, including the mean square prediction error and the mean absolute relative deviation. The results show that the performance of the proposed approach with slow-rate glucose data(15 min) is close to that obtained based on fast-rate data(5 min).

关键词： Blood Glucose Prediction Wearable Devices Mean Square Prediction Error Sampled Data Kalman Filtering

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