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Fuzzy dynamic output-feedback control of non-linear networked discrete-time system with missing measurements

IET CONTROL THEORY AND APPLICATIONS 2015年第3期9卷 327-335页

作者： Li, Hongyi Wu, Chengwei Feng, Zhiguang Bohai Univ Coll Engn Jinzhou 121013 Liaoning Peoples R China Bohai Univ Coll Informat Sci & Technol Jinzhou 121013 Liaoning Peoples R China

This article studies the problem of H dynamic output-feedback control of non-linear networked discrete-time systems with data packet dropouts. Specifically, the data missing happens randomly from the sensor to the controller and the controller to the actuator. A sequence of independent variables obeying the Bernoulli random binary distribution is introduced to account for the measurement missing. The Takagi-Sugeno fuzzy model is used to describe the non-linear plant. In addition, the strategy of parallel distributed compensation is adopted to design the fuzzy dynamic output-feedback controller. Here, attention is focused on obtaining the sufficient conditions to guarantee the closed-loop system to be stochastically stable as well as the prescribed H performance. An approach based on fuzzy Lyapunov function is developed to solve it. To tackle the non-convex problem, the cone-complementarity linearisation procedure is adopted. Finally, a numerical example is employed to show the usefulness of the proposed result.

关键词： fuzzy control feedback control system synthesis nonlinear control systems networked control systems distributed parameter systems discrete time systems H control compensation closed loop systems asymptotic stability Lyapunov methods concave programming linearisation techniques fuzzy dynamic output-feedback controller design nonlinear networked discrete-time system data missing measurements H dynamic output-feedback control problem data packet dropouts independent variables Bernoulli random binary distributed Takagi-Sugeno fuzzy model nonlinear plant parallel distributed compensation closed-loop system asymptotic stability fuzzy Lyapunov function nonconvex problem cone-complementarity linearisation procedure

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A novel deviation density peaks clustering algorithm and its applications of medical image segmentation

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IET IMAGE PROCESSING 2022年第14期16卷 3790-3804页

作者： Zhou, Wei Wang, Limin Han, Xuming Li, Mingyang Changchun Univ Sci & Technol Sch Comp Sci & Technol Changchun 130022 Peoples R China Guangdong Univ Finance & Econ Sch Informat Guangzhou Peoples R China Jinan Univ Coll Informat Sci & Technol Guangzhou Peoples R China Jilin Univ Sch Management Changchun Peoples R China

The density peaks clustering (DPC) algorithm can identify clusters with various shapes and densities in the underlying dataset. However, the DPC algorithm cannot exactly find the true quantity of clustering centers when computing the local density, and it is difficult to handle non-convex datasets. Moreover, the DPC algorithm is difficult to identify boundary points and outliers without a reasonable allocation strategy when dealing with low-density points. To solve these limitations, a novel deviation density peaks clustering (DeDPC) algorithm is proposed. First, the local deviation of the spatial distance of datasets with different structures is utilised to replace the local density to generate a more reasonable clustering center decision graph. Second, a threshold is defined to further divide and process low-density points. Finally, outliers in low-density points can be accurately found to accurately cluster the dataset. To evaluate the performance of the DeDPC algorithm, experiments are conducted on synthetic and real-world datasets and the DeDPC is compared with other clustering methods. The DeDPC is also applied to medical image segmentation to further demonstrate its capability for medical image processing. The simulation results show that the DeDPC method has good validity and utility for both non-convex datasets and medical image segmentation.

关键词： medical image processing image segmentation deviation density peaks clustering pattern clustering concave programming nonconvex datasets graph theory medical image segmentation low-density points clustering center decision graph decision theory DeDPC

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Spatial-temporal waveform design for MIMO radar without approximating the covariance matrix

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IET RADAR SONAR AND NAVIGATION 2020年第3期14卷 381-387页

作者： Zhang, Bo Dai, Fengzhou Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

In this study, a transmit waveform design method based on spatial-temporal optimisation for multiple-input multiple-output (MIMO) radar is proposed. The goal is not only to design the waveforms for beampattern matching, but also to minimise the autocorrelation sidelobes of the spatial synthesis signals of interested direction. Especially, to synthesise the constant envelope waveform directly, an improved sequential convex algorithm is proposed to tackle the non-convex problem in spatial optimisation. Then the autocorrelation sidelobes are reduced by using the steepest descent algorithm in temporal optimisation. Finally, the authors assess the effectiveness of the proposed technique through numerical simulations in comparison with the traditional and the alternating direction method of multipliers method.

关键词： concave programming waveform analysis MIMO radar optimisation radar antennas antenna arrays array signal processing covariance matrices gradient methods convex programming MIMO communication spatial-temporal waveform design MIMO radar covariance matrix transmit waveform design method spatial-temporal optimisation multiple-input multiple-output radar beampattern matching autocorrelation sidelobes spatial synthesis signals interested direction constant envelope waveform improved sequential convex algorithm nonconvex problem spatial optimisation steepest descent algorithm alternating direction method

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Sensitivity-based relaxation and decomposition method to dynamic reactive power optimisation considering DGs in active distribution networks

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IET GENERATION TRANSMISSION & DISTRIBUTION 2017年第1期11卷 37-48页

作者： Ding, Tao Liu, Shiyu Wu, Zhongyu Bie, Zhaohong Xi An Jiao Tong Univ Dept Elect Engn State Key Lab Elect Insulat & Power Equipment Xian 710049 Shaanxi Peoples R China

With the development of active distribution networks, new challenges such as overvoltage and power loss become critical. The reactive power optimisation serves as a voltage control measure to minimise the total transmission loss by coordinating the continuous and discrete reactive power compensators while guaranteeing the specific physical and operating constraints. To address the daily operating times of discrete control variables, the dynamic reactive power optimisation (DRPO) is set up to minimise total energy loss over several time periods when considering the inter-temporal constraints. However, DRPO is in fact a large-scale mixed integer non-linear non-convex programming that is difficult to solve. Therefore, second-order cones are employed to relax the non-convex power flow equations to obtain a mixed integer second order cone programming model. Furthermore, a sensitivity-based relaxation and decomposition method is proposed to further improve the computational performance. Solution quality and computational performance are compared with traditional methods on IEEE-33, 123 and 615-bus systems as well as two real-world distribution networks in China. The Results demonstrate that the fast performance and effectiveness of the proposed technique.

关键词： distribution networks reactive power concave programming integer programming sensitivity-based relaxation dynamic reactive power optimisation DG active distribution networks power loss reactive power optimisation voltage control transmission loss discrete reactive power compensators continuous reactive power compensators inter-temporal constraints mixed integer non-linear non-convex programming nonconvex power flow equations mixed integer second order cone programming model decomposition method computational performance IEEE 615-bus systems China IEEE-615-bus systems

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Lightning flash algorithm for solving non-convex combined emission economic dispatch with generator constraints

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第1期12卷 104-116页

作者： Kheshti, Mostafa Kang, Xiaoning Li, Jiangtao Regulski, Pawel Terzija, Vladimir Xi An Jiao Tong Univ Shaanxi Key Lab Smart Grid Xian Shaanxi Peoples R China Xi An Jiao Tong Univ State Key Lab Elect Insulat & Power Equipment Xian Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Elect Engn Xian Shaanxi Peoples R China Wroclaw Univ Technol Dept Elect Power Engn Wroclaw Poland Univ Manchester Sch Elect & Elect Engn Manchester Lancs England

Due to the importance of global warming and environmental impacts that accumulated from emission of gaseous pollutants of fossil-fuelled power plants, the modern combined emission economic dispatch (CEED) is applied. This study proposes a new evolutionary lightning flash algorithm to solve dual-objective CEED problem considering different scenarios with wind power penetration, multiple fuel options and operation constraints on the generators. The lightning flash algorithm is formulated based on the movements of the cloud to ground lightning strikes in a thunderstorm. This method is tested on 11 benchmark functions and then it is applied on six different practical case study systems for solving non-convex CEED. The results of LFA on benchmark functions and the case study systems are compared with other methods and confirm the effectiveness and applicability of the proposed method with higher quality solution, less emission, less costs and better convergence against other methods for solving non-convex practical economic dispatch, CEED and dynamic dispatch problems.

关键词： wind power plants lightning protection earthing concave programming power generation dispatch evolutionary computation global warming power generation economics power generation protection nonconvex combined emission economic dispatch generator constraints global warming environmental impacts gaseous pollutants emission fossil-fuelled power plants evolutionary lightning flash algorithm dual-objective CEED problem wind power penetration operation constraints multiple fuel options cloud movements lightning strikes grounding thunderstorm nonconvex CEED dynamic dispatch problems

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Flexible, reliable, and renewable power system resource expansion planning considering energy storage systems and demand response programs

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IET RENEWABLE POWER GENERATION 2019年第11期13卷 1862-1872页

作者： Hamidpour, Hamidreza Aghaei, Jamshid Pirouzi, Sasan Dehghan, Shahab Niknam, Taher Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Islamic Azad Univ Semirom Branch Power Syst Grp Semirom Iran Islamic Azad Univ Qazvin Branch Fac Elect Biomed & Mechatron Engn Qazvin Iran

This study presents a flexible, reliable, and renewable power system resource planning approach to coordinate generation, transmission, and energy storage (ES) expansion planning in the presence of demand response (DR). The flexibility and reliability of the optimal resource expansion planning are ensured by means of appropriate constraints incorporated into the proposed planning tool where thermal generation units, ES systems, and DR programs are considered as flexibility resources. The proposed planning tool is a mixed-integer non-linear programming (MINLP) problem due to the non-linear and non-convex constraints of AC power flow equations. Accordingly, to linearise the proposed MINLP problem, the AC nodal power balance constraints are linearised by means of the first-order expansion of Taylor's series and the line flow equations are linearised by means of a polygon. Additionally, the stochastic programming is used to characterise the uncertainty of loads, a maximum available power of wind farms, forecasted energy price, and availability/unavailability of generation units and transmission lines by means of a sufficient number of scenarios. The proposed planning tool is implemented on the IEEE 6-bus and the IEEE 30-bus test systems under different conditions. Case studies illustrate the effectiveness of the proposed approach based on both flexibility and reliability criteria.

关键词： integer programming load flow wind power plants power generation planning stochastic programming nonlinear programming power transmission planning demand side management thermal power stations power transmission lines power generation economics power transmission economics concave programming energy storage AC power flow equations AC nodal power balance constraints first-order expansion stochastic programming maximum available power planning tool IEEE 30-bus test systems reliability criteria flexible power system resource expansion planning renewable power system resource expansion planning energy storage systems energy storage expansion planning demand response optimal resource expansion planning thermal generation units ES systems DR programs flexibility resources mixed-integer nonlinear programming nonconvex constraints demand response programs reliable power system resource expansion planning

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Cooperative look-ahead control of vehicle platoon travelling on a road with varying slopes

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IET INTELLIGENT TRANSPORT SYSTEMS 2019年第2期13卷 376-384页

作者： Zhai, Chunjie Luo, Fei Liu, Yonggui South China Univ Technol Sch Automat Sci & Engn Wushan 381 Guangzhou Guangdong Peoples R China

In the current literature, little research has been done in the eco-driving of vehicle platoon. To increase the fuel efficiency of vehicle platoon and ensure vehicle safety, this study proposes a switching control strategy for vehicle platoon travelling on a road with varying slopes, where the platooning and eco-driving technologies are used. To improve the fuel efficiency of vehicle platoon, the cooperative look-ahead controller is proposed by formulating a cooperative look-ahead control problem of vehicle platoon based on distributed model predictive control (CLCbDMPC), which is a constrained nonlinear non-convex multi-objective optimisation problem. To solve the CLCbDMPC problem quickly, after the minimum fuel consumption table and its corresponding optimal reduction gear ratio table are constructed and calculated off-line, the improved particle swarm optimisation algorithm with multiple dynamic populations is presented. Furthermore, the safety controller acting as the emergency brake to guarantee vehicle safety is also designed. The switch between the look-ahead controller and the safety controller forms the switching control strategy of vehicle platoon. Simulation results demonstrate, compared with benchmarks, the proposed strategy can significantly save up to 21.88% of fuel for vehicle platoon, and vehicle safety can also be guaranteed.

关键词： road vehicles road traffic control concave programming distributed control predictive control particle swarm optimisation switching systems (control) cooperative look-ahead controller vehicle platoon eco-driving technologies fuel efficiency vehicle safety switching control strategy cooperative look-ahead control problem distributed model predictive control CLCbDMPC problem constrained nonlinear nonconvex multiobjective optimisation problem minimum fuel consumption table optimal reduction gear ratio table improved particle swarm optimisation algorithm multiple dynamic populations safety controller

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Filter-and-forward transceiver design for cognitive two-way relay networks

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IET COMMUNICATIONS 2015年第17期9卷 2061-2069页

作者： Maleki, Marjan Vakili, Vahid Tabataba Iran Univ Sci & Technol Sch Elect Engn Tehran Iran

In this study a new approach to filter-and-forward (FF) relay design in an overlay cognitive radio (CR) network with frequency selective channels is proposed. In this system, to implement two-way relaying scheme, a secondary multi-antenna node acts as a relay and helps two primary users (PUs) communicate with each other and in return the secondary node superposes its signal to filtered signals of PUs. The authors intend to design secondary transceiver under two criteria, one from the viewpoint of improving the performance of secondary network and the other to guarantee quality of service (QoS) of primary network. The first criterion is to maximise the signal-to-interference-plus-noise ratio (SINR) of secondary receiver subject to constraints on QoS of PUs and the relay transmitted power. For the other one, the goal is to achieve max-min fairness among PUs through maximising the worse signal-to-interference-plus-noise ratio (SINR) of PUs subject to constraints on QoS of secondary receiver and transmit power of relay. The authors employ an alternating optimisation algorithm to find the beamforming matrices and the allocated power to secondary receiver's message in these non-convex problems. Simulation results show that the proposed FF approach has better performance compared with simple AF relaying strategy.

关键词： relay networks (telecommunication) cognitive radio radio transceivers quality of service overlay networks antenna arrays array signal processing matrix algebra concave programming filter-and-forward transceiver design cognitive two-way relay networks FF relay design overlay CR network frequency selective channels secondary multiantenna node primary users PUs secondary transceiver quality of service QoS max-min fairness secondary receiver optimisation algorithm beamforming matrices nonconvex problems AF relaying strategy

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Efficient method for finding globally optimal solution of problem with weighted L_p norm and L₂ norm objective function

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IET SIGNAL PROCESSING 2016年第4期10卷 366-375页

作者： Li, Ya Xie, Langxiong Ling, Bingo Wing-Kuen Cao, Jiangzhong Dai, Qingyun Guangdong Univ Technol Fac Informat Engn Guangzhou 510006 Guangdong Peoples R China

This study proposes an iterative method to approximate an N-dimensional optimisation problem with a weighted L-p and L-2 norm objective function by a sequence of N independent one-dimensional optimisation problems. Inspired by the existing weighted L-1 and L-2 norm separable surrogate functional (SSF) iterative shrinkage algorithm, there are N independent one-dimensional optimisation problems with weighted L-p and L-2 norm objective functions. However, these optimisation problems are non-convex. Hence, they may have more than one locally optimal solutions and it is very difficult to find their globally optimal solutions. This paper proposes to partition the feasible set of each approximated problem into various regions such that the sign of the convexity of the objective function in each region remains unchanged. Here, there is no more than one stationary point in each region. By finding the stationary point in each region, the globally optimal solution of each approximated optimisation problem can be found. Besides, this study also shows that the sequence of the globally optimal solutions of the approximated problems converge to the globally optimal solution of the original optimisation problem. Computer numerical simulation results show that the proposed method outperforms the existing weighted L-1 and L-2 norm SSF iterative shrinkage algorithm.

关键词： image reconstruction concave programming iterative methods weighted L-p norm objective function weighted L-2 norm objective function iterative method N-dimensional optimisation problem L-1 norm N independent one-dimensional optimisation problem sequence separable surrogate functional iterative shrinkage algorithm non-convex optimisation problems L-2 norm SSF iterative shrinkage algorithm image reconstruction

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Energy-efficient network cooperation joint resource configuration in multi-RAT heterogeneous cognitive radio networks

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ELECTRONICS LETTERS 2016年第16期52卷 1414-1415页

作者： Feng, Li Kuang, Yujun Fu, Xinchuang Dai, Zeyang Univ Elect Sci & Technol China Sch Commun & Informat Engn Xi Yuan Rd 2006 Chengdu 611731 Sichuan Provinc Peoples R China

A novel energy efficiency (EE)-oriented cognitive spectrum sharing scheme in heterogeneous cognitive radio networks, called energy-efficient cognitive cooperation (ECC) is proposed, in which the primary system release a part of its radio spectrum to the secondary system in exchange for secondary user (SU) served as a relay to assist the transmission of the primary user (PU) traffic. On the basis of ECC scheme, an EE maximisation non-convex problem for SUs is formulated under the circuit power consumption and the QoS requirements as well as power constraints. By virtue of parametric Dinkelbach's algorithm and golden section search method, an efficient resource allocation algorithm is developed. Simulation results show that our proposals attain significantly higher EE compared with the non-cooperation transmission without sacrificing the performance of PU's QoS, and thus, a 'win-win' situation is achieved.

关键词： cognitive radio concave programming cooperative communication energy conservation quality of service radio networks radio spectrum management resource allocation search problems telecommunication power management telecommunication traffic ECC scheme EE maximisation nonconvex problem EE-oriented cognitive spectrum sharing scheme PU QoS PU traffic SU circuit power consumption energy-efficient cognitive cooperation energy-efficient network cooperation joint resource configuration golden section search method multiRAT heterogeneous cognitive radio networks noncooperation transmission parametric Dinkelbach algorithm power constraints primary user traffic radio spectrum resource allocation algorithm secondary user

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