الفهرس 
System Model descriptionOnly 14 pages are availabe for public view
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Abstract
Most of balancing systems like inverted pendulum, ball and beam, and ball on
plate system (BOPS) suffer from control problems like point stabilization and
Trajectory tracking problems also these systems suffer from system problems like
high nonlinearity, unstable in open loop, multi-inputs and multi outputs so many
control algorithms is used to solve these problems like PID (ProportionalIntegral-Derivative), Fuzzy, sliding mode, and many other algorithms but there
is a problem is noticed in the BOPS (Ball on Plate System) that most of scientific
papers used a small range of plate angle and this small plate angle make the
system slower and has an overshoot so this thesis uses a large plate angle to make
the system faster, decrease or eliminate the overshoot, enhancement the point
stabilization and trajectory tracking. When using a large plate angle or large
control signal range, the nonlinearity of the system become larger and normal
linear controller can’t run with efficient way, so a T-S (Takagi-Sugeno) fuzzy
model is implemented to treat with the high nonlinearity of the system and
improve the tracking and stabilization of the BOPS. The T-S fuzzy model is
compared with the state space mode to validate it. The servo motor equation is
deduced using identification. The relations between servo motor and plate angle
in x and y direction contain Trigonometric Functions and when solving these
relations in real time with control algorithms like PID of fuzzy or any other
algorithms it takes a time larger than the sample time of the system so this
problem is solved by using the Trigonometric Functions in the form of lookup
table but using the lookup table has some problems like time delay and missing
angles so the Trigonometric Functions is used in the form of ML (machine
learning) model with the designed algorithm. The system depends on machine
vision to determine the position of the ball in x and y direction. The hybrid
T1FLPD/ML (type 1 fuzzy logic proportional derivative / Machine learning)
design depends on a machine learning (ML) algorithm that detect the angle of the
servo motor required to correct the position of the ball on the plate also the
parameters of the PD controller is changed online using the fuzzy logic to enhance
the performance of the trajectory and point tracking of the system. This paper
introduces three different machine learning techniques for predicting servo motor
angle that obtain higher accuracies of 99.95%, 99.908%, and 99.998% for support
vector regression, decision tree regression, and random forest regression,
respectively. Simulation and practical results on a BOPS dynamic model are both
demonstrated to justify the validation of the proposed design scheme. The
experimental practical results justify the feasibility and effectiveness of the
proposed controller design strategy.