Title :
A neurocontroller with adaptive static state decoupling for multivariable systems
Author_Institution :
State Key Lab. of Ind. Control Technol., Zhejiang Univ., Hangzhou, China
Abstract :
The neurocontroller with adaptive static state decoupling for multivariable systems is proposed in this paper. In this new intelligent control system, a recursive least squares method with a changeable forgetting factor is used to obtain the parameters of the low-order model of the multivariable system. The multivariable system is decoupled statically, and then the neurocontroller is used in each input-output path to control the decoupling multivariable system. The simulation test results show that good performance, strong robustness and adaptability are obtained.
Keywords :
adaptive control; industrial control; least squares approximations; multivariable control systems; neurocontrollers; recursive estimation; adaptive static state decoupling; changeable forgetting factor; decoupling multivariable system control; input-output path; intelligent control system; low-order model; neurocontroller; recursive least squares method; static decoupled multivariable system; Adaptation models; Biological neural networks; Control systems; Intelligent control; MIMO; Mathematical model; Neurons;
Conference_Titel :
Advanced Computational Intelligence (ICACI), 2012 IEEE Fifth International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4673-1743-6
DOI :
10.1109/ICACI.2012.6463205
