Distributed Fusion Filtering for Nonlinear Time-Varying Systems Over Amplify-and-Forward Relay Networks: An H∞ Quantized Framework

National Natural Science Foundation of China (Grant Number: 61973102, 61933007 and U22A2044); China Postdoctoral Science Foundation (Grant Number: 2022M710683); Jiangsu Funding Program for Excellent Postdoctoral Talent of China (Grant Number: 2022ZB128); Royal Society of the U.K., the Alexander von Humboldt Foundation of Germany

Distributed Recursive Filtering over Sensor Networks with Non-Logarithmic Sensor Resolution

National Natural Science Foundation of China under Grant 62003083, Grant 61873059, Grant 61873148, Grant 61922024 and Grang 61933007; Shanghai Science and Technology Program of China under Grant 20JC1414500; Shanghai Sailing Program of China under Grant 19YF1402400; Program of Shanghai Academic/Technology Research Leader of China under Grant 20XD1420100

Multi-sensor multi-rate fusion estimation for networked systems: Advances and perspectives

National Natural Science Foundation of China under Grants 62103095, 61873058, 61873148 and 61933007; AHPU Youth Top-notch Talent Support Program of China under Grant 2018BJRC009; Natural Science Foundation of Anhui Province of China under Grant 2108085MA07; Royal Society of the UK; Alexander von Humboldt Foundation of Germany

Centralized moving-horizon estimation for a class of nonlinear dynamical complex networks under event-triggered transmission scheme

Data availability statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.This article is concerned with the problem of event-triggered centralized moving-horizon state estimation for a class of nonlinear dynamical complex networks. An event-triggered scheme is employed to reduce unnecessary data transmissions between sensors and estimators, where the signal is transmitted only when certain condition is violated. By treating sector-bounded nonlinearities as certain sector-bounded uncertainties, the addressed centralized moving-horizon estimation problem is transformed into a regularized robust least-squares problem that can be effectively solved via existing convex optimization algorithms. Moreover, a sufficient condition is derived to guarantee the exponentially ultimate boundedness of the estimation error, and an upper bound of the estimation error is also presented. Finally, a numerical example is provided to demonstrate the feasibility and efficiency of the proposed estimator design method.National Natural Science Foundation of China. Grant Numbers: 61873148, 61933007, 62033008, 62073339, 62173343; Natural Science Foundation of Shandong Province of China. Grant Number: ZR2020YQ49; AHPU Youth Top-notch Talent Support Program of China. Grant Number: 2018BJRC009; Natural Science Foundation of Anhui Province of China. Grant Number: 2108085MA07; China Postdoctoral Science Foundation. Grant Number: 2018T110702; Postdoctoral Special Innovation Foundation of Shandong Province of China. Grant Number: 201701015; Royal Society of the UK; Alexander von Humboldt Foundation of Germany

Event-Triggered Recursive State Estimation for Stochastic Complex Dynamical Networks Under Hybrid Attacks

National Natural Science Foundation of China (Grant Number: 61973102, 61933007, 61873148 and 61873058); Royal Society of the U.K.; Alexander von Humboldt Foundation of Germany

Probability-Guaranteed Distributed Secure Estimation for Nonlinear Systems over Sensor Networks under Deception Attacks on Innovations

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI10.1109/TSIPN.2021.3097217, IEEE Transactions on Signal and Information Processing over Networks10.13039/501100004608-Natural Science Foundation of Jiangsu Province (Grant Number: BK20190021); 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61773209, 61973102, 61873148, 61933007 and 71801196); 10.13039/501100010014-Six Talent Peaks Project in Jiangsu Province (Grant Number: XYDXX-033); Alexander von Humboldt Foundation of Germany

A novel PID-like particle swarm optimizer: on terminal convergence analysis

Copyright © 2021 The Author(s). In this paper, a novel proportion-integral-derivative-like particle swarm optimization (PIDLPSO) algorithm is presented with improved terminal convergence of the particle dynamics. A derivative control term is introduced into the traditional particle swarm optimization (PSO) algorithm so as to alleviate the overshoot problem during the stage of the terminal convergence. The velocity of the particle is updated according to the past momentum, the present positions (including the personal best position and the global best position), and the future trend of the positions, thereby accelerating the terminal convergence and adjusting the search direction to jump out of the area around the local optima. By using a combination of the Routh stability criterion and the final value theorem of the Z-transformation, the convergence conditions are obtained for the developed PIDLPSO algorithm. Finally, the experiment results reveal the superiority of the designed PIDLPSO algorithm over several other state-of-the-art PSO variants in terms of the population diversity, searching ability and convergence rate.National Natural Science Foundation of China under Grants 61873148, 61933007 and 620730070; AHPU Youth Top-notch Talent Support Program of China under Grant 2018BJRC009; Natural Science Foundation of Anhui Province of China under Grant 2108085MA07; Royal Society of the UK; Alexander von Humboldt Foundation of Germany

Electric vehicle charging station planning with dynamic prediction of elastic charging demand: a hybrid particle swarm optimization algorithm

Copyright © The Author(s) 2021. This paper is concerned with the electric vehicle (EV) charging station planning problem based on the dynamic charging demand. Considering the dynamic charging behavior of EV users, a dynamic prediction method of EV charging demand is proposed by analyzing EV users’ travel law via the trip chain approach. In addition, a multi-objective charging station planing problem is formulated to achieve three objectives: (1) maximize the captured charging demands; (2) minimize the total cost of electricity and the time consumed for charging; and (3) minimize the load variance of the power grid. To solve such a problem, a novel method is proposed by combining the hybrid particle swarm optimization (HPSO) algorithm with the entropy-based technique for order preference by similarity to ideal solution (ETOPSIS) method. Specifically, the HPSO algorithm is used to obtain the Pareto solutions, and the ETOPSIS method is employed to determine the optimal scheme. Based on the proposed method, the siting and sizing of the EV charging station can be planned in an optimal way. Finally, the effectiveness of the proposed method is verified via the case study based on a test system composed of an IEEE 33-node distribution system and a 33-node traffic network system.National Natural Science Foundation of China under Grants 61703245, 61873148 and 61933007; Natural Science Foundation of Shandong Province of China under Grant ZR2020MF071; AHPU Youth Top-notch Talent Support Program of China under Grant 2018BJRC009; Natural Science Foundation of Anhui Province of China under Grant 2108085MA0y; Royal Society of the UK; Alexander von Humboldt Foundation of Germany

Observer-based monitors and distributed wave controllers for electromechanical disturbances in power systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 242-246).by Ernst Scholtz.Ph.D