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2017 17th IEEE International Conference on Communication Technology (ICCT 2017)

作者： Wang Kai Liu Jingzhi Xu Shun Gan Zhichun Battlefield Information Processing Laboratory Chongqing Communication Institute Department of command and control system National Defense Information Institute

ISBN: (纸本)9781509039456;9781509039449

In most of the existing compressed sensing based Massive MIMO channel estimation schemes, additional information on pilot placement should be transmitted by base station since the locations of randomly allocated pilot is required in channel reconstruction at user equipment. In this paper, a deterministic pilot placement scheme is proposed for compressed sensing of massive MIMO channel to cut down the overhead caused by pilot placement information. A deterministic subset of the subcarriers is selected for pilot transmission instead of a random one. This deterministic subset of subcarriers index leads a new kind of block deterministic measurement matrices in compressed sensing model for channel estimation. To establish the theoretical guarantee for the new pilot placement scheme, the measurement property of block deterministic matrix is verified via coherence analysis. Numerical results show that even without the pilot locations information, the proposed compressed sensing method based on deterministic pilot placement can achieve the similar estimation accuracy as conventional method based on random pilot placement.

关键词： massive MIMO channel estimation compressed sensing sparsity coherence

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Toeplitz-Structured Measurement for Compressed Channel Feedback in FDD Massive MIMO system 9

Toeplitz-Structured Measurement for Compressed Channel Feedb...

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2017 9th IEEE International Conference on Communication Software and Networks (ICCSN 2017)

作者： Wang Kai Liu Jingzhi Zhang Bo Zhang Jianjian Wang Kai Battlefield Information Processing Laboratory Chongqing Communication Institute Department of command and control system National Defense Information Institute

ISBN: (纸本)9781509038237;9781509038220

By exploiting the correlations of the massive MIMO channel, Compressed Sensing(CS) has been applied to develop efficient channel feedback scheme. The existing CS-based channel feedback schemes, however, adopt Gaussian random matrices to compress the channel, which will impose an unreachable memory and computation requirement on user equipment(UE). In this paper, a new Toeplitz-structured measurement matrix is proposed to perform efficient channels compression in massive MIMO system. Instead of containing entries of are independent realizations of random variables with certain distributions, Toeplitz matrices have constant diagonals, leading a significant reduction of UE requirement for storing and computation. Based on such matrices, we introduce novel feedback mechanism improving further the feedback efficiency for frequency division duplex(FDD) massive MIMO systems. Simulation results show that the Toeplitz-structured matrices perform comparably with Gaussian random matrices while requiring less independent random variables and computation complexity.

关键词： massive MIMO channel feedback frequency division duplex compressed sensing toeplitz measurement matrix

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Traffic Signal control based on Markov Decision Process

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IFAC-PapersOnLine 2016年第3期49卷 67-72页

作者： Xu, Yunwen Xi, Yugeng Li, Dewei Zhou, Zhao Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control Information Processing Ministry of Education of China Shanghai200240 China

This paper proposes a Morkov state transition model for an isolated intersection in urban traffic and formulates the traffic signal control problem as a Markov Decision Process(MDP). In order to reduce computational burden, a sensitivity-based policy iteration(PI) algorithm is introduced to solve the MDP. The proposed model is stage-varying according to traffic flow variation around the intersection, and the state transition matrices and cost matrices are updated so that a new optimal policy can be searched by the PI algorithm. The proposed model also can be easily extended from an isolated intersection to a traffic network based on the space-time distribution characteristics of traffic flow, so as the PI algorithm. The numerical experiments of a small traffic network show that this approach is capable of reducing the number of vehicles substantially compared with the fixed-time control particularly for high traffic demand, while being computationally efficient. © 2016

关键词： Traffic signals Iterative methods Markov processes Street traffic control Computationally efficient Markov Decision Processes Markov state Policy iteration algorithms Space time distribution State transition Matrix State transition models Traffic signal control

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Model predictive control for constrained uncertain piecewise linear systems

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控制理论与应用（英文版） 2013年第2期11卷 230-236页

作者： Dewei LI Yugeng XI Yuanyuan ZOU Department of Automation and Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology

For constrained piecewise linear （PWL） systems, the possible existing model uncertainty will bring the difficulties to the design approaches of model predictive control （MPC） based on mixed integer programming （MIP）. This paper combines the robust method and hybrid method to design the MPC for PWL systems with structured uncertainty. For the proposed approach, as the system model is known at current time, a free control move is optimized to be the current control input. Meanwhile, the MPC controller uses a sequence of feedback control laws as the future control actions, where each feedback control law in the sequence corresponds to each partitions and the arbitrary switching technique is adopted to tackle all the possible switching. Furthermore, to reduce the online computational burden of MPC, the segmented design procedure is suggested by utilizing the characteristics of the proposed approach. Then, an offline design algorithm is proposed, and the reserved degree of freedom can be online used to optimize the control input with lower computational burden.

关键词： Model predictive control Constrained piecewise linear system Structured uncertainty Segmented designprocedure

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An Improved Robust Model Predictive control Approach to systems with Linear Fractional Transformation Perturbations

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International Journal of Automation and computing 2011年第1期8卷 134-140页

作者： Peng-Yuan Zheng Yu-Geng Xi De-Wei Li Department of Automation Shanghai Jiao Tong University Shanghai 200240 PRC Key Laboratory of System Control and Information Processing Ministry of Education PRC

In this paper, a robust model predictive control approach is proposed for a class of uncertain systems with time-varying, linear fractional transformation perturbations. By adopting a sequence of feedback control laws instead of a single one, the control performance can be improved and the region of attraction can be enlarged compared with the existing model predictive control （MPC） approaches. Moreover, a synthesis approach of MPC is developed to achieve high performance with lower on-line computational burden. The effectiveness of the proposed approach is verified by simulation examples.

关键词： Robust model predictive control linear fractional transformation （LFT） perturbations linear matrix inequalities （LMIs） feedback model predictive control （MPC） framework sequence of feedback control laws.

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Improved Real-Coded Genetic Algorithm Solution for Unit Commitment Problem Considering Energy Saving and Emission Reduction Demands

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Journal of Shanghai Jiaotong university(Science) 2015年第2期20卷 218-223页

作者：潘谦何星蔡云泽王治华苏凡 Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai Jiaotong University State Grid Shanghai Municipal Electric Power Company

Unit commitment(UC), as a typical optimization problem in electric power system, faces new challenges as energy saving and emission reduction get more and more important in the way to a more environmentally friendly society. To meet these challenges, we propose a UC model considering energy saving and emission reduction. By using real-number coding method, swap-window and hill-climbing operators, we present an improved real-coded genetic algorithm(IRGA) for UC. Compared with other algorithms approach to the proposed UC problem, the IRGA solution shows an improvement in effectiveness and computational time.

关键词： genetic algorithm(GA) unit commitment(UC) improved real-number encoding

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H2 Optimal Proportional Navigation Guidance Law 34

H2 Optimal Proportional Navigation Guidance Law

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第三十四届中国控制会议

作者： CHEN Mengxiang ZHANG Weidong Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University

ISBN: (纸本)9781467374439

This paper proposes an H2 optimal control based proportional navigation guidance law, in which the autopilot's dynamic property is taken into account. The derived guidance law is analytical and requires only the missile parameters. Simulation result shows the proposed guidance law can nullify the line-of-sight rate at a fast speed and has better interception performance than the traditional proportional guidance for multiple kinds of targets.

关键词： H2 Optimal control,Optimal Navigation Law,Proportional Navigation

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Missile-Target Situation Assessment Model Based on Reinorcement Learning

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Journal of Shanghai Jiaotong university(Science) 2020年第5期25卷 561-568页

作者： ZHANG Yun LU Runyan CAI Yunze Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationKey Laboratory of Marine Intelligent Equipment and System of Ministry of EducationShanghai Jiao Tong UniversityShanghai 200240China

In situation assessment(SA)of missile versus target fighter,the traditional SA models generally have the characteristics of strong subjectivity and poor dynamic *** paper considers SA as an expectation of future returns and establishes a missile-target simulation battle *** actor-critic(AC)algorithm in reinforcement learning(RL)is used to train the evaluation network,and a missile-target SA model is established in simulation battle *** and comparative experiments show that the model can effectively estimate the expected effect of missile attack under the current situation,and it provides an effective basis for missile attack decision.

关键词： situation assessment(SA) battle model reinforcement learning(RL) actor-critic(AC)algorithm

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A Novel Robust MM Filter for Target Tracking with Glints

A Novel Robust MM Filter for Target Tracking with Glints

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第三十二届中国控制会议

作者： LIU Yong LIANG Yan PAN Quan School of Automation Northwestern Polytechnical University Key Laboratory of System Control and Information Processing Ministry of Education

ISBN: (纸本)9781479900305

Interacting Multiple Model(IMM) filter faces significant outlier-caused *** this paper,the Bayesian probability update in IMM is found equivalent to Dempster's Rule of Combination which cannot handle evidence conflicts caused by ***,a novel robust MM(RMM) filter is proposed through introducing expert rules about mode evolvement and presenting the Likelihood Temporal Ratio(LTR) and building the Induced Combination Rule(ICR).Simulations about target tracking show the effectiveness of the proposed method.

关键词： Interacting Multiple Model Robust Filter Outlier Dempster-Shafer Theory Non-Gaussian Noise

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State of Health Estimation Combining Robust Deep Feature Learning with Support Vector Regression 34

State of Health Estimation Combining Robust Deep Feature Lea...

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第三十四届中国控制会议

作者： LIU QiaoQiao LI JianXun Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (纸本)9781467374439

Combining Stacked Contractive Auto-Encoders(SCAE) with Support Vector Regression(SVR) method based on mass of data, a novel state of health estimation method is proposed in this paper. With the development of SCAE-SVR, SCAE could learn features automatically for SVR instead of extracting hand-designed features. SCAE is a deep machine learning method of unsupervised statistical algorithm that makes the learned features more robust and efficient. Then Support Vector Regression machine is used to estimate quantitative values dealing with the new feature representations. The composite structure of network not only remedies not enough features abstracted by a simplex shallow machine learning net, but also effectively avoid over-fitting in data regression. State of health estimation for Fuel cell systems from Prognostics and Health Management(PHM) 2014 Data Challenge demonstrates that the proposed method outperforms than other state of health estimation methods based on data-driven.

关键词： State of health estimate,CAE,SVR,Fuel cell systems

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