SETTING OF THE PID CONTROLLER BASED ON
RBF NEURAL NETWORK DESIGN AND
SIMULATION
Abstract
At present, because the PID control has a simple control structure,
through adjusting the proportional integral and differential gain basic
satisfactory control performance, and is relatively easy to setting in
practical application, so widely used in process control and motion control,
especially in the accurate model can be built more deterministic control
system application. With the increasingly complex of the modern industrial
process, however, increased step by step to control requirements (e.g.,
stability, accuracy and quickness, etc.), classical control theory is faced
with severe challenges. Non-linear systems in industrial control field, using
the traditional PID control can not obtain satisfactory control effect.
Optimized RBF neural network based on gradient descent algorithm, it will be
integrated neural network and PID control technology, with a conventional PID
controller has simple structure, physical meaning is clear advantages, at the
same time with neural network self-learning, adaptive function. Therefore,
this article through to the RBF neural network structure and the calculation
method of learning, to design a setting of the PID controller based on RBF
neural network, constructs its model, and then write M language program.
Using the MATLAB software to design the RBF neural network setting of PID
control algorithm simulation research. Data and then further through
simulation experiment, the control system stability, robustness,
anti-interference ability, etc.
Keywords:PID; RBF neural network; Parameter setting
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