Báo Cáo Adaptive and intelligent controller using neural network

ABSTRACT
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Intelligent control techniques have emerged to overcome some deficiencies in conventional
control method in dealing with complex real-world systems. These problems include knowledge
adaptation, learning and expert knowledge incorporation. In this paper, a newly proposed intelligent
controller which includes both neural network controller as compensator and an intelligent switching
control algorithm based on learning vector quantization neural network (LVQNN) is used to control of
complex dynamic systems. A superb mixture of conventional PID controller and the neural network’s
powerful capability of learning, adaptive and tackle nonlinearity bring us a good-tracking controller
for such a kind of plants with high nonlinearity and hysteresis. In addition, with the greatly changing
external environments, a learning vector quantization neural network (LVQNN) is applied as a
supervisor of the conventional PID controller, which estimates the external environments and switches
to the optimal gain of the PID controller.

Results of simulating on the complex dynamic systems such as pneumatic artificial muscle (PAM)
manipulator show that the newly proposed intelligent controller presented in this study can making
online control with better dynamic property, strong robustness and suitable for the control of various
plants, including linear and nonlinear process and without regard to the severe change of external
environments.