Since the invention of Fuzzy logic and fuzzy control, the latter has been growing in spread
and importance in many applications and devices in many life aspects. This maybe due to
the easy use of a fuzzy control system, and for being far of math co
mplications. Even if the
plant model is unknown, a self-organizing fuzzy controller (SOFC) can improve the
response of an already exist linear control table, or even can build a control table from
scratch, by assessing current performance of the controller and adjusting the control table
accordingly. This paper provides a simple article that shows how to design and use a self organizing
fuzzy controller, through a simulation example using MATLAB & Simulink
in which a variable torque loaded DC motor speed regulation is done. The simulation
showed the ability of the controller to provide a good response and decrease speed error by
a notable amount at load torque changing times. This paper can be used as textbook
material for students or researchers interested in the field of adaptive control, especially
self-organizing fuzzy control.
In this study we developed an adaptive model inspired by internal models in the cerebellum and this approach called Feedback Error Learning (FEL). FEL is the origin of Learning Feed-Forward Control (LFFC). It depends on Feedback Controller and Feed-F
orward Controller which is a Neural Network, and this Neural Network uses feedback controller output as training signal. We developed this approach to control a robot arm, and to balance inverted pendulum and to control bus suspension system. We developed this approach by adding a second Neural Network, and this new Neural Network uses FEL controller output as training signal. We simulate these systems by using Matlab and Simulink, and we find that this development improves control performance.
One ofa car's suspension system functions is to isolate vibrations resulting from road on the driver and ensure a comfortable ride. But the design of control systems for semi-active suspension systems is difficult because of the non-linearity of the
constituent elements of these systems which make the researches related to it characterized by complexity. So in order to improve the performance of semi-active suspension systems without bearing the effort of designing a model based controller, a control system is designed using self-organizing fuzzy controller based on the principle of delay-in-penalty to control a semi-active suspension system which uses a magneto rheological damper. The controller tries to enhance system performance using the desired response as it is described in the penalty table. The fuzzy logic controller is based on two inputs namely sprung mass velocity and unsprung mass velocity. Using a quarter car model with 2 degree-of-freedom the system is modeled and simulated in MATLAB &Simulink® and the results are compared to the widely used sky-hook strategy. the simulation showed the ability of the self-organizing fuzzy controller to provide good results in minimizing sprung mass acceleration in variousroad profiles compared to sky-hookstrategy.
The nonlinear model of Unmanned Aerial Vehicle( UAV) has been
recognized. Airosim Matlab toolbox has been used to guarantee a
simulation model for the Aerosonde.In the first stage, a
linearization technique is used to calculate the mathematical
m
odel of the UAV at a specific operation point, then PID controller
is used to stabilize this linear model. At the final stage, an
augmented feedback neural network adaptive controller is
applied to stabilize the overall nonlinear system.
