Authors
Abstract
Abstract:
A neural network-based feedforward controller and self-tuning PID controller
with optimization algorithm is presented. The scheme of the controller is based on two
unknown models that describe the system and optimization algorithm. These models are
modified Elman recurrent neural network and NARMA-L2. The modified Elman
recurrent neural network (MERNN) model and NARMA-L2 model are learned with
two stages off-line and on-line, in order to guarantee that the output of the model
accurately represents the actual output of the system. The aim from the NARMA-L2
model is to find the Inverse Feedforward Controller (IFC) which controls the steadystate
output of the system. The MERNN model after being learned is called the
identifier. The feedback PID self tuning control signal for N-step ahead can be
calculated the PID parameters by using the optimization algorithm with the quadratic
performance index which is quadratic in the error between the desired set point and the
model output, as well as quadratic of the control action. The paper explains the
algorithm for a general case, and then a specific application on non-linear dynamical
plant is presented.
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