انجام پروژه متلب | متلبی

 کد مقاله g104
عنوان مقاله:
Neural network based robust hybrid control for robotic system an H∞ approach
سال ارائه:  2011       نوع مقاله: ژورنال        گزارش فارسی: دارد
قضیه لیاپانف
Robotic system, Computed torque, control, Neural network, Variable structure control, H∞ control, Lyapunov stability, پروژه متلب , شبیه سازی با متلب ,

AbstractA novel robust hybrid tracking control
for robotic system is proposed. This hybrid control
scheme combines computed torque control (CTC)
with neural network, variable structure control (VSC)
and nonlinearH∞control methods. It is assumed that
the nominal system of robotic system is completely
known, which is controlled by using CTC method.
Neural network is designed to approximate parameter
uncertainties, VSC is used to eliminate the effect of
approximation error, andH∞control is employed to
achieve a desired robust tracking performance. Based
on Lyapunov stability theorem, it can be guaranteed
that all signals in closed loop are bounded and a speci
fiedH∞tracking performance is achieved by employ
ing the proposed robust hybrid control. The validity of
the control scheme is shown by computer simulation
of a two-link robotic manipulator.
KeywordsRobotic system·Computed torque
control·Neural network·Variable structure control·
H∞control·Lyapunov stability

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Abstract- This paper presents a novel maximum-power-point­
tracking (MPPT) algorithm in wind-turbine generation
systems using neural network compensator based on the slope
of the wind-turbine mechanical power versus rotation speed
to avoid the oscillation problem and effect of uncertain
parameters. Because the characteristics of the wind-turbine
rotation speed is determined by the wind speed and air
density conditions, the technologies of changing the location
of the maximum power point must be developed in the
applications of MPPT control in order to make the wind­
turbine generator get the optimal efficiency from wind
energy at different operating conditions. In this study, the
uncertainties in wind-turbine generation systems are
compensated by a neural network, the duty cycle of dc/dc
converter is determined by a PI controller, and the
parameters is determined by a genetic algorithm with the
help of MATLAB. From the simulation results, the validity of
the proposed MPPT controller can be verified under
variations of wind speed, air density, and the load electrical
characteristics in wind-turbine generator systems.

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