Title :
Nonlinear control of magnetic suspension system based on RBFNN
Author :
Cao, Jianyun ; Lu, Guoping ; Feng, Gang ; Hu, Weili
Author_Institution :
Coll. of Electr. Eng., Nantong Univ., Jjiangsu, China
Abstract :
In this paper, nonlinear characteristic of a multivariable magnetic suspension system is formulated, and a modified scheme of nonlinear multivariable internal model control (IMC) is put forward, based on neural network inversion. With radial basis function neural network (RBFNN) approximating inversion of the system, the compound system is linearised and decoupled, and IMC can be applied here for this nonlinear magnetic suspension system. In the end, simulations can prove the effectiveness of the improved approach.
Keywords :
control system analysis; inverse problems; multivariable control systems; neurocontrollers; nonlinear control systems; radial basis function networks; suspensions (mechanical components); internal model control; multivariable magnetic suspension system; neural network inversion; nonlinear control; radial basis function neural network; Coils; Control systems; Equations; Inverse problems; MIMO; Magnetic levitation; Mathematical model; Nonlinear control systems; Power system modeling; Radial basis function networks;
Conference_Titel :
Control and Automation, 2005. ICCA '05. International Conference on
Print_ISBN :
0-7803-9137-3
DOI :
10.1109/ICCA.2005.1528138
