An Adaptive Control Algorithm Based on Q-Learning for UHF Passive RFID Robots in Dynamic Scenarios

Honggang Wang, Ruixue Yu, Ruoyu Pan, Peidong Pei, Zhao Han, Nanfeng Zhang and Jingfeng Yang ()
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Honggang Wang: School of Communications and Information Engineering and School of Artificial Intelligence, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
Ruixue Yu: School of Communications and Information Engineering and School of Artificial Intelligence, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
Ruoyu Pan: School of Communications and Information Engineering and School of Artificial Intelligence, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
Peidong Pei: School of Communications and Information Engineering and School of Artificial Intelligence, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
Zhao Han: School of Communications and Information Engineering and School of Artificial Intelligence, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
Nanfeng Zhang: Huangpu Customs District Technology Center, Guangzhou 510730, China
Jingfeng Yang: Guangzhou Institute of Industrial Intelligence, Guangzhou 511458, China

Mathematics, 2022, vol. 10, issue 19, 1-17

Abstract: The Identification State (IS) of Radio Frequency Identification (RFID) robot systems changes continuously with the environment, so improving the identification efficiency of RFID robot systems requires adaptive control of system parameters through real-time evaluation of the IS. This paper first expounds on the important roles of the real-time evaluation of the IS and adaptive control of parameters in the RFID robot systems. Secondly, a method for real-time evaluation of the IS of UHF passive RFID robot systems in dynamic scenarios based on principal component analysis (PCA)-K-Nearest Neighbor (KNN) is proposed and establishes an experimental scene to complete algorithm verification. The results show that the accuracy of the real-time evaluation method of IS based on PCA-KNN is 92.4%, and the running time of a single data is 0.258 ms, compared with other algorithms. The proposed evaluation method has higher accuracy and shorter running time. Finally, this paper proposes a Q-learning-based adaptive control algorithm for RFID robot systems. This method dynamically controls the reader’s transmission power and the robot’s moving speed according to the IS fed back by the system; compared with the default parameters, the adaptive control algorithm effectively improves the identification rate of the system, the power consumption under the adaptive parameters is reduced by 36.4%, and the time spent decreases by 29.7%.

Keywords: RFID robots; dynamic scenarios; identification state; adaptive control; Q-learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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