@inproceedings{0035a9674db2424aa69c09686265900e,
title = "Optimal controls for dual-driven load system with synchronously approximate dynamic programming method",
abstract = "This paper applies a synchronously approximate dynamic programming (ADP) scheme to solve the Nash controls of the dual-driven load system (DDLS) with different motor properties based on game theory. First, a neural network (NN) is applied to approximate the dual-driven servo unknown system model. Because the properties of two motors are different, they have different performance indexes. Another NN is used to approximate performance index function of each motor. In order to minimize the performance index, the Hamilton function is constructed to solve the approximate optimal controls of the load system. Based on parameter error information, an adaptive law is designed to estimate NN weights. Finally, the practical DDLS is simulated to demonstrate that the optimal control inputs can be studied by ADP algorithm.",
keywords = "Approximate dynamic programming, Multi-input system, Nash equilibrium, Neural networks, Servo system",
author = "Yongfeng Lv and Xuemei Ren and Linwei Li and Jing Na",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2019.; Chinese Intelligent Systems Conference, CISC 2018 ; Conference date: 01-01-2019 Through 01-01-2019",
year = "2019",
doi = "10.1007/978-981-13-2288-4\_32",
language = "English",
isbn = "9789811322877",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Verlag",
pages = "319--327",
editor = "Yingmin Jia and Junping Du and Weicun Zhang",
booktitle = "Proceedings of 2018 Chinese Intelligent Systems Conference - Volume I",
address = "Germany",
}