Department News
A Combined Proportional Plus Integral (PI) and Neural Network Controller for
Seminar Date
2000-10-19
Author
손문숙
Date
2000-10-19
Views
1018
1. 제 목 : A Combined Proportional Plus Integral (PI) and Neural Network Controller for
a Heating Coil
2. 연 사 : Professor Douglas C. Hittle
Associate Head for Graduate Studies
Department of Mechanical Engineering, Colorado State University
3. 일 시 : 8월 14일(월) 오후 2시-3시
4. 장 소 : 301동 1512호
5. 내 용 : A brief tutorial will be presented on neural networks followed by a discussion of this
new application. A neural network is trained to produce the steady state output of a
PI controller that is modulating a control valve on a heating coil to maintain a
constant discharge air temperature. Inputs to the network are the set point, the inlet
air temperature, the inlet water temperature and the air flow rate. Training is
accomplished on a three-layer network of sigmoid units using back propagation.
Once the network is trained, it is placed in parallel with the PI controller. In this
configuration, the neural network can produce the desired steady state control signal
instantly, essentially replacing the integrator of the PI controller. Some proportional
gain is needed for good response to set point changes. The advantage of the
combined controller is that a PI controller can be tuned conservatively to avoid risk
of instability as plant gains change. While performance of such a controller would
initially be poor, once the network is trained and made part of the controller,
performance improves and provides better response than can be achieved with a PI
controller alone (even a carefully tuned one). The neural network acts as a non-linear
feed forward component and is a well matched augmentation to PI control for non-
linear heating, ventilating and air-conditioning (HVAC) systems. Results from
simulation studies and from experiments on a full scale HVAC system are presented.
6. 문 의 : 기계항공공학부 김민수 교수 (☏ : 880-8362)
a Heating Coil
2. 연 사 : Professor Douglas C. Hittle
Associate Head for Graduate Studies
Department of Mechanical Engineering, Colorado State University
3. 일 시 : 8월 14일(월) 오후 2시-3시
4. 장 소 : 301동 1512호
5. 내 용 : A brief tutorial will be presented on neural networks followed by a discussion of this
new application. A neural network is trained to produce the steady state output of a
PI controller that is modulating a control valve on a heating coil to maintain a
constant discharge air temperature. Inputs to the network are the set point, the inlet
air temperature, the inlet water temperature and the air flow rate. Training is
accomplished on a three-layer network of sigmoid units using back propagation.
Once the network is trained, it is placed in parallel with the PI controller. In this
configuration, the neural network can produce the desired steady state control signal
instantly, essentially replacing the integrator of the PI controller. Some proportional
gain is needed for good response to set point changes. The advantage of the
combined controller is that a PI controller can be tuned conservatively to avoid risk
of instability as plant gains change. While performance of such a controller would
initially be poor, once the network is trained and made part of the controller,
performance improves and provides better response than can be achieved with a PI
controller alone (even a carefully tuned one). The neural network acts as a non-linear
feed forward component and is a well matched augmentation to PI control for non-
linear heating, ventilating and air-conditioning (HVAC) systems. Results from
simulation studies and from experiments on a full scale HVAC system are presented.
6. 문 의 : 기계항공공학부 김민수 교수 (☏ : 880-8362)