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|Title:||A novel resilient control scheme for a class of Markovian jump systems with partially unknown information||Authors:||Zhang, Kun
|Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2021||Source:||Zhang, K., Su, R. & Zhang, H. (2021). A novel resilient control scheme for a class of Markovian jump systems with partially unknown information. IEEE Transactions On Cybernetics. https://dx.doi.org/10.1109/TCYB.2021.3050619||Project:||2013-T1- 002-177||Journal:||IEEE Transactions on Cybernetics||Abstract:||In the complex practical engineering systems, many interferences and attacking signals are inevitable in industrial applications. This paper investigates the reinforcement learning (RL) based resilient control algorithm for a class of Markovion jump systems with completely unknown transition probability information. Based on the Takagi-Sugeno logical structure, the resilient control problem of nonlinear Markovion systems is converted into solving a set of local dynamic games, where the control policy and attacking signal are considered as two rival players. Combining the potential learning and forecasting abilities, the new integral RL (IRL) algorithm is designed via system data to compute the zero-sum games without using the information of stationary transition probability. Besides, the matrices of system dynamics can also be partially unknown, and the new architecture requires less transmission and computation during the learning process. The stochastic stability of the system dynamics under the developed overall resilient control is guaranteed based on Lyapunov theory. Finally, the designed IRL based resilient control is applied to a typical multi-mode robot arm system, and implementing results demonstrate the practicality and effectiveness.||URI:||https://hdl.handle.net/10356/152246||ISSN:||2168-2267||DOI:||10.1109/TCYB.2021.3050619||Rights:||© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TCYB.2021.3050619.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Journal Articles|
Updated on May 17, 2022
Updated on May 17, 2022
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