APN05V20-0992
Introduction
Fuzzy Controller for Air Conditioning System
A basic Model
A Modified Model
An Advanced Model for Automobile Passenger Environment
Comments
Temperature control is widely used in various processes. These processes, no matter if it is in a large industrial plant, or in a home appliance, share several unfavorable features. These include non-linearity, interference, dead time, and external disturbances, among others. Conventional approaches usually do not result in satisfactory temperature control.
In this Application Note we provide examples of fuzzy logic used to control temperature in several different situations. These examples are developed using FIDE, an integrated fuzzy inference development environment.
FUZZY CONTROLLER FOR AIR CONDITIONING SYSTEM
In the following discussion, we give examples of air conditioning systems, ranging from a basic model to an advanced model. We do not provide FIU(Fuzzy Inference Unit) source code as we have in previous application notes. Instead, this time we concentrate on the input/output variables of the fuzzy controller for an air conditioning system.
Let us start with the simplest air conditioning system, which is shown in Figure 1. The only control target in this system is temperature. There are two adjustment valves to change temperature. An example provided in directory /fide/examples/fans in the FIDE software package is similar to this basic model.
Figure 1 Air Conditioning System : Basic Model
There is a sensor in the room to monitor temperature for feedback control, and there are two control elements, cooling valve and heating valve, to adjust the air supply temperature to the room.
Figure 2 Fuzzy Controller for Air Conditioning System : Basic Model
Figure 2 diagrams a fuzzy controller for an air conditioning system basic model. Rules for this controller may be formulated using statements similar to:
If temperature is low then open heating valve greatly
Values such as low are defined by fuzzy sets (membership functions). We can use the MF-edit function in FIDE to define the fuzzy sets. Generally, membership functions of fuzzy sets take on a triangular shape because they are effective and easy to manipulate.
In the real world, however, it is usually not enough to manage an air conditioning system with temperature control only. We need to control humidity as well. A modified air conditioning system is shown in Figure 3. There are two sensors in this system: one to monitor temperature and one to monitor humidity. There are three control elements: cooling valve, heating valve, and humidifying valve, to adjust temperature and humidity of the air supply.
Figure 3 Air Conditioning System : Modified Model
A fuzzy controller for this modified air conditioning system is shown in Figure 4. The two inputs to the controller are measured temperature and humidity. The three outputs are control signals to the three valves.
Figure 4 Fuzzy Controller for Air Conditioning System : Modified Model
Rules for this controller can be formulated by adding rules for humidity control to those already formulated for temperature control in the basic model. Additional rules must take the interference between temperature and humidity into account. For example, in the winter, when we use heat to raise temperature, humidity is usually reduced. The air thus becomes too dry. To address this condition, a rule statement similar to the following is appropriate:
If temperature is low then open humidifying valve slightly
This rule acts as a predictor of humidity (it leads the humidity value) and is also designed to prevent overshoot in the output humidity curve. We could have used the following rule:
If humidity is low then open humidifying valve slightly
But it's action, if acting as the only rule for low humidity, will be late when low humidity is already the case.
An Advanced Model for Automobile Passenger Environment
Temperature control in an automobile passenger environment is more complex than that of a static room in a building. To address driver and passenger comfort and safety, many factors must be taken into account. Temperature and humidity should be controlled to provide an enjoyable ride. However, it is also critical to keep windows from being fogged, which is caused by a temperature differential between inside and outside air in combination with the interior humidity. To obtain satisfactory control results, the strength of sunshine radiation and the automobile speed must also be factored in.
Figure 5 shows a fuzzy controller which employs five sensors to obtain data for temperature control and humidity control in an automobile. A recent industry report on the application of such a controller on a new model automobile indicates this controller outperforms conventional control systems substantially. It prevents rapid change of temperature in the car when doors or windows are opened and then closed. It even reacts to weather changes because interior humidity changes caused by the weather can be detected by sensors.
Figure 5 Fuzzy Controller for Air Conditioning System : Advanced Model
Air conditioning systems are essential in most of our daily lives. Our expectations of such systems have been raised to demand more than just temperature control, and it is increasingly desirable to apply these systems in varying situations and environments. A comfortable and safe environment is often difficult to define and affected by sometimes contradictory factors. Fuzzy logic control provides an effective and economic approach this problem. Fuzzy controllers incorporated in the latest model automobiles designed by Japanese auto makers provide proof that temperature control in diverse environments can be solved. The key to a good solution lies in thorough analysis of factors affecting the control target and the kinds of sensors and sensing techniques used to detect these factors.
We did not provide FIU source code in this note. However we give examples of the types of rules required. For further investigation, FIU source code for a temperature control system can be found in the directory /fide/examples/fans in the FIDE system provided with the FIDE or FIDE DEMO package.
For an engineer, an ideal machine would be one in which human requests are automatically interpreted and responded to by adjusting itself appropriately to variations in the environment. Fuzzy logic can help make this ideal a reality. At the least, it makes the effort easier.
For Further Information Please Contact:
FuzzyNet http://www.aptronix.com/fuzzynet
Email: fuzzynet@aptronix.com
Weijing Zhang,
Applications Engineer.
Copyright © 1992 by Aptronix Inc.
Revised: October 31, 1996.