D
Derui Ding
Researcher at University of Shanghai for Science and Technology
Publications - 186
Citations - 9503
Derui Ding is an academic researcher from University of Shanghai for Science and Technology. The author has contributed to research in topics: Computer science & Filter (signal processing). The author has an hindex of 39, co-authored 151 publications receiving 5756 citations. Previous affiliations of Derui Ding include Northeast Petroleum University & Swinburne University of Technology.
Papers
More filters
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Event-based recursive filtering for time-delayed stochastic nonlinear systems with missing measurements
TL;DR: An easy-implemented recursive algorithm with consideration of linearization errors, time-delays, packet losses as well as adopted communication protocols, and an illustrative example to show the effectiveness of the developed filtering algorithm.
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Adaptive Dynamic Programming for Networked Control Systems Under Communication Constraints: A Survey of Trends and Techniques
Xueli Wang,Ying Sun,Derui Ding +2 more
TL;DR: Wang et al. as mentioned in this paper surveyed the latest development of adaptive dynamic programming (ADP) based optimal control with communication constraints and summarized some applications of the ADP method in practical systems.
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Robust model predictive control under redundant channel transmission with applications in networked DC motor systems
TL;DR: In this paper, a model predictive control (MPC) algorithm is proposed to obtain the desired sub-optimal control sequence as well as the upper bound of the quadratic cost function.
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Dissipative control for state-saturated discrete time-varying systems with randomly occurring nonlinearities and missing measurements
TL;DR: In this paper, the dissipative control problem is investigated for a class of discrete time-varying systems with simultaneous presence of state saturations, randomly occurring nonlinearities as well as multiple missing measurements.
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Recursive Filtering of Distributed Cyber-Physical Systems With Attack Detection
TL;DR: A novel distributed filter is constructed and its gain is designed via a set of recursive formulas on the upper bound of covariance of filtering errors, to avoid the calculational challenge of cross-covariance matrices and realize the requirement of distributed implementation, simultaneously.