Title :
Adaptive Control of the Grinding Process Based on Fuzzy RBF Neural Network
Author_Institution :
Coll. of Comput. Sci., Political Sci. & Law Inst., Lanzhou, China
Abstract :
A method of intelligent PID control was proved and it´s based on RBF neural network and fuzzy theory, which constructs RBF neural network identifier online and identifies a controlled object online by means of adopting the receding horizon optimization methods, and adjusts parameters of PID controller online and realizes decoupling control of multivariable, nonlinear and time variation system. The analysis course is briefness, the time of network learning and training is little, learning precision is high, estimate value very close in upon analysis value. The simulation researches have verified the proposed approach which can be control systems where it is difficult to build accurate math model.
Keywords :
adaptive control; fuzzy control; fuzzy neural nets; fuzzy set theory; grinding; learning (artificial intelligence); multivariable control systems; neurocontrollers; nonlinear control systems; radial basis function networks; adaptive control; decoupling control; fuzzy RBF neural network; fuzzy theory; grinding process; intelligent PID control; multivariable system; network learning; nonlinear system; receding horizon optimization methods; time variation system; Artificial neural networks; Automation; Computational modeling; Control systems; Integrated circuit modeling; Mathematical model; Process control; Fuzzy Theory; Grinding Process; PID control; RBF Nervous Network;
Conference_Titel :
Electrical and Control Engineering (ICECE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-6880-5
DOI :
10.1109/iCECE.2010.91
