انجام پروژه متلب | متلبی

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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کد مقاله g186

عنوان مقاله:

Development of Self-Tuning Intelligent PID Controller Based on BPNN for Indoor Air Quality Control

سال ارائه:  2013       نوع مقاله: ژورنال                             گزارش فارسی: دارد

کلید واژه : کنترل کننده PID با شبکه عصبی ، سیستم حلقه بسته با کنترل کننده ی PID تطبیقی 

Back-propagation, neural network, PID control, IAQ control, stability analysis

Abstract—For those who spend most of their time working
indoors, the indoor air quality (IAQ) could affect their
working efficiency and health. This paper presents an
intelligent proportional-integral-derivative (PID) controller
for IAQ control. Different from the traditional PID controller,
this novel controller combined with Back-Propagation Neural
Networks (BPNN) technology will regulate the PID
parameters k
p
, k
i
, kd automatically. In the present study, the
algorithm of the BPNN-based PID controller is first discussed
in details, and the control performance is then tested by
simulation using MATLAB. The difficulty in IAQ control is
the existence of control disturbance, time delay and
measurement errors. The results show that the combined
control algorithm has better performance on the systemic
stability, disturbance resistance, fast response rate and small
overshoot compared with traditional PID controller.
Keywords—Back-propagation, neural network, PID
control, IAQ control, stability analysis.

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