Premium
POWELL'S METHOD APPLIED TO LEARNING NEURAL CONTROL OF THREE UNKNOWN DYNAMIC SYSTEMS
Author(s) -
Li C. James,
Yan Lilai,
Chbat Nicolas W.
Publication year - 1995
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/j.1099-1514.1995.tb00017.x
Subject(s) - control theory (sociology) , artificial neural network , controller (irrigation) , inverted pendulum , computer science , control engineering , tracking error , pendulum , control (management) , tracking (education) , engineering , nonlinear system , artificial intelligence , mechanical engineering , psychology , pedagogy , physics , quantum mechanics , agronomy , biology
SUMMARY This paper describes a direct neural network (NN) learning controller that is capable of improving its performance in the control of a non‐linear system whose dynamics are unknown. This controller is able to improve its performance without having to identify a model of the plant, which is a necessity for most existing neural network controllers. This characteristic is obtained with a gradient‐free neural network learning algorithm, Powell's method. The performance of this new controller in the control of three non‐linear systems, a pendulum, a double pendulum and a robot, was evaluated by simulations and experiments. The new controller has shown fast learning and small tracking error in the control of these systems.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom