Fuzzy Control Algorithm Applied on Constant Airflow Controlling of Fans

Abstract

Keeping a certain constant airflow in a lot of applications is critical, such as certain airflow volumes in the cabin of an aircraft, submarines, tunnels, and buildings. All fans change their output airflow and air pressure by changing motor speed in real applications. In order to achieve the target airflow volume, normal operation detects the data first, then provides orders to engineers to adjust manually. Extra airflow volume sensors are equipped to measure the airflow so that people know what the current airflow is and how much needs to be adjusted. Based on normal technology, airflow volume sensors and motor speed inverters are necessary parts of a whole air supply system. This research tries to integrate a fuzzy control algorithm into a motor control technology to build a new kind of fan that can not only self-detect the airflow volume changes but also can self-adjust the airflow volumes automatically. This self-detecting and adjusting fan can lead air supply systems in devices and buildings to need no airflow volume sensors, make the air supply system building cost lower, and make the maintaining and operating cost lower.

1. Introduction

In applications such as air conditioning, heating, cooling, or ventilation systems, fans are necessary and critical parts of the whole system. In the past, the system included fans, airflow volume sensors, and thermostat switch systems. Engineers needed to measure the airflow volume data, then change the motor speed to obtain more or fewer airflow volumes to adjust the temperature up or down, change the airflow volume supplied, or fix the airflow volume sensors in the pipes to maintain online detecting, and connect airflow sensors to control the unit, as shown in Figure 1. This makes air supply system maintenance very complex, and well-trained operators are needed. There is also a lot of waste due to oversupply when adjustments are not made on time. A lot of research now focuses on introducing inverters into the operation of fan airflow volume controls; however, there is still a need for well-trained operators, and control systems are still complicated. Our research is aimed to find a new way to make the air supply system simply structured, easily operated, and even smartly self-adjusted, not only for heating, ventilation, air conditioning, and cooling (HVAC) but also for any air supply applications, such as aircraft cabins and submarines. In order to reach this goal, our research tries to integrate AI control technology, and fuzzy algorithm, into fan control to create a new kind of fan that not only does not need airflow volume sensors to detect the airflow changes and self-adjust its output automatically but also can be energy saving. This research introduces an extra “airflow observer” by testing the traditional structure of the fuzzy algorithm to build a new operation mechanism that makes a fan output constant airflow automatically when the air pressure changes. The method of building this constant airflow fan can also be used to design and manufacture other different performance constant airflow fans.

There is research on constant airflow fans developing already, such as research by Ming Zhou and Mingxiang Chen [1], Biao Jiang and Yacheng Zhou [2], and Zhengli Wu, Xuchun Li, Yisong Lv and Ji He [3]. Their research is focused on finding the direct relationship between either power consumption or current and the use of the external motor driver to detect the power or current changing, then controlling the input of motor power consumption or motor current to control the fan’s output, which is not accurate and online on time, and needs external intervening by operators. As the fuzzy algorithm has a lot of advantages in controlling uncertain and non-linear working mediums such as air and water, more and more researchers are now using the fuzzy algorithm to control working conditions of pumps and fans, such as entropy [4], researched optimizing water pumps systems efficiency by introducing fuzzy algorithm into the whole pumps control system. Yifang Si [5] researched introducing fuzzy algorithm into buildings’ heating and air condition control to make buildings’ environments control more intelligent. This research is mostly focused on how to control pumps, fans, and air conditioning working in parallel or in series to be more efficient, which is more similar to the study of tasks managing and assigning, not a product’s self-controlling. With the development of the fuzzy algorithm, more and more studies prove that fuzzy algorithm can be applied in the field of self-adaptation controlling; researchers such as Li Tang [6], Chengyi Liao [7], and Tianhu Zhang [8], all found that the fuzzy algorithm can perform well on self-adaptation control. Self-adaptation is a focus of our research as our research builds a new fan that not only performs system tasks managing and assigning but can also perform self-adaption. Our research introduces an airflow observer into the fuzzy algorithm and creates a new kind of fan-controlling method, which can make fans work not only smartly but can also perform self-adapting to the working environment changes.

2. Research on the Way of Constant Air Volume Control

As the fan impeller is driven by a motor, in order to control the fan output, we need to build field-oriented control (FOC) into the permanent magnet synchronous motor (PMSM) first [9].

2.1. Build FOC to PMSM

Through building a dq rotating rectangular coordinate system, which is on a rotor magnet, PMSM’s voltage vector is found in Equation [10]:

$$\left\{\begin{array}{l}{V}_q=R_1{i}_q+p{L}_q{i}_q+{\omega }_r\left(L_d{i}_d+{\psi }_f\right)\hfill \\ V_d=R_1{i}_d+p\left(L_d{i}_d+{\psi }_f\right)-{\omega }_r{L}_q{i}_q\hfill \end{array}\right.$$

(1)

where V_q, V_d are the PMSM d_q axis voltage component; i_d, i_q is the d_q axis current component; L_d, L_q are the PMSM d_q axis inductance; R₁ is the phase resistance of the stator winding; ${\psi }_f$ is the permanent magnet flux linkage; and ${\omega }_r$ is the angle speed of the rotor.

Magnet torque produced by the PMSM:

$$T_e=p_p\left({\mathsf{\Psi }}_f{i}_q+\left(L_d-L_q\right)i_d{i}_q\right)$$

(2)

where $p_p$ is the motor poles pair. As we are going to use a surface-mounted magnet motor [11], there is no salient effect, so we can control the exciting current on the above equation = 0, and the magnet torque can be simplified to be:

$$T_e=p_p{\mathsf{\Psi }}_f{i}_q$$

(3)

As one of the normal control methods for PMSM, the basic thought of field-oriented control (FOC) control is building a resolution of the motor’s three-phase alternating current (AC) voltage to V_d and V_q, and building a resolution of AC current to direct current (DC) components i_d and i_q. The i_q corresponds to the motor’s torque current, and i_d corresponds to the excitation current. So, the motor’s magnet torque can be controlled through the independent control of the torque current, which is simple and quick. The FOC control theory enhances AC motor control technology [12] by leaps and bounds. In the constant airflow control system, FOC control is the bottom control unit; it is an indispensable part of the whole framework.

The sensorless FOC control schematics for PMSM are explained above in Figure 2. First, obtain the motor rotor position through a speed and position observer, then obtain the phase angle of the motor, then the CPU samples the actual three-phase currents from the current sensor, then calculate the torque current i_q and the excitation current id through Clark and Park transformation of coordinates. Compare the set data and calculated data of the torque current i_q and the excitation current id separately, and obtain the voltages vector sum through input differences of the current comparisons with the current regulator. The voltage–duty ratio, which are outputs of the current regulator through inverse-Park and inverse-Clark coordinates transformed, are the inputs of the PWM waver generator. The generator produces six channels of PWM waves to control the insulated gate bipolar translator (IGBT) on/off and timing and control the motor effectively [13].

From Equation (3), we can see the output electromagnet torques of the PMSM can be controlled by setting torque currents. If the torque current is constant, the output electromagnet torque of the PMSM motor is constant, too; this is called constant torque control [14]. Obviously, the output of the airflow volume of the fan is not constant when the fan is controlled by constant torque controlling, so we need to research the torque currents controlling strategy when the fan is outputting constant airflow.

2.2. Design of the Airflow Observer

When the motor speed is constant, the output airflow Q and air pressure P’s relationship of the fan is as Figure 3 explains. In Figure 3, motor speed n₃ > n₂ > n₁, when the motor speed is at a certain fixed speed, airflow Q changes when air pressure P changes; this change makes a fixed QP curve, the curve changes when the motor speed changes, it makes one series of PQ curves. The line Qc in Figure 3 represents constant airflow that can be controlled by controlling the motor speed when the air pressure P changes.

Equipping applications with airflow sensors makes the air supply system complex upon installation and increases the cost. In order to control the airflow constant, the air supply system needs to detect some other physical quantities which can be easily detected, such as motor current and motor rotation frequency, instead of airflow Q detecting. Figure 4 is an airflow testing chamber. In this airflow testing chamber, the air pressure P can be adjusted by adjusting the drafting inductance fan inside or the air jet nozzle valves, the airflow of the fan can be measured at different air pressures in this testing chamber, displayed in Figure 5.

The airflow testing schematic is shown below, where P1-Patm is the static pressure drop, and airflow volume is calculated through the pressure difference: Δ_P on the two sides of the nozzles. According to standards of AMCA210-995 [15], air jet nozzles mouth size in OD: 10 mm, 30 mm, 40 mm, 50 mm, and 80 mm each one pc inside, one pressure transmitter: Yokogawa model EJA110, one pressure adjuster inside: Shemaden Model S10 inside. Moreover, the airflow volume testing theory formula is as follows:

$$Q=C_{d}Y{A}_n\sqrt{\frac{2\mathsf{\Delta }p}{\rho \left(1-{\beta }^4\right)}}$$

(4)

where C_d means Nozzle’s discharge co-efficiency [16], An means Nozzle’s area, β means dt/D, Y means Expansion factor, calculated as:

$$Y={\left[\frac{\gamma }{\gamma -1}{\alpha }^{2/\gamma }\frac{1-{\alpha }^{(\gamma -1)/\gamma }}{1-\alpha }\right]}^{1/2}{\left[\frac{1-{\beta }^4}{1-{\beta }^4{\alpha }^{2/\gamma }}\right]}^{1/2}$$

(5)

where γ means air-specific heat at constant pressure vs. constant volume heat capacity; normally, it is set at 1.4. α means the ratio of static pressures on the inlet vs. outlet.

Figure 6 displays the relation of C_d (discharge co-efficiency) and Reynolds number [17], the airflow is calculated through the below process. First of all, $K_Q$ in Figure 6 is computed in the following formula:

$$K_Q=A_n\sqrt{\frac{2\mathsf{\Delta }p}{\rho \left(1-{\beta }^4\right)}}$$

(6)

Then, we assumed C_d as 0.9 [18] and calculated $Q=C_d\times K_Q$ and $R{e}_t=VtDt/V=4Q/\pi d_{t}v$. Finally, C_d in Figure 6 is computed as follows:

$$C_d=0.9986-\frac{7.006}{\sqrt{R{e}_t}}+\frac{134.6}{R{e}_t}$$

(7)

This airflow volume chamber testing capacity is designed between 9M3/TO and 900 M3 per hour.

Using the Figure 4 airflow testing chamber, we performed the below experiments. First, on the basis of Figure 2 Fan’s PMSM FOC control, adding speed loops on the preceding stage, make a speed-controlled FOC control system, then we adjust the fan’s speed at different air pressures to make airflow always be certain data, then record the related torque current i_q and rotation frequency f data. Through a large number of experiments, we finally found that when the airflow is constant data, adjust air pressure P, when P is higher, the motor’s rotation frequency f is higher, which is more important, the torque current i_q controller output and rotation frequency f have a relationship of a quadratic function. This quadratic function changes when the airflow output changes; if the constant airflow needs to be increased, the i_q-f curve needs to be increased accordingly.

From Figure 7, we can see that when the fan is working stably, the airflow could be speculated in reverse through the data of i_q, f, and the table. We name this working mechanism as Fan’s airflow observer.

In the control logic of FOC, the torque current i_q and rotation frequency f are known quantities; through airflow, i_q and f tablet, airflow could be speculated. For example, when the i_q = 0.627 A, f = 145 Hz, the airflow could be speculated through the tablet is 40% × Q_n. Here, Q_n = 500(m³/h), which is the max-rated airflow when the fan is constantly airflow controlled. It should be noted that i_q is the internal detection value of the FOC after the transformation of coordinates; when i_d = 0, i_q is times the motor phase’s current RMS values.

A fan’s airflow is affected by this fan’s house, fan’s impeller angle, fan’s impeller shape, and other mechanical structure factors [18], so the Figure 7 function relationship could only relate to the fan and motor, which is this experiment, but we could obtain other fans’ family curves similar as Figure 7 curve through experiments. Hence, the theory of building an airflow observer is workable on other fans, too.

2.3. Design of Fuzzy PID Control System

2.3.1. Discrete Type Fuzzy PID Controller Design and Its Principles

This paper designed one discrete type fuzzy PID controller [19,20,21,22,23,24,25,26]; its input is the difference from a set airflow Q* to the airflow Q_obs which observed by the observer, the output data of PID is the torque current instruction $i_q^{*}$, torque current instruction $i_q^{*}$ works as the input of the fundamental FOC control variable which controls the PMSM motor. In this process, the PMSM motor is controlled by outputting magnet torque which corresponds to the torque current and drives the fan impeller. At this time, the rotation speed of the PMSM motor is automatically stabilized at the balanced points of the fan loading; this makes a constant airflow control system.

Here, the control back of the PID controller could be represented by the following formula:

$$i_q^{\ast }\left(k\right)=K_{p}e\left(k\right)+K_I{\displaystyle \sum _{i=0}^{k}e\left(i\right)}+K_D\left(e\left(k\right)-e\left(k-1\right)\right)+i_{q0}$$

(8)

where K_P, K_I, and K_D are proportion, integration, and differentiation gains. Moreover, i_q0 is a feed-forward for the PID controller. It is used to increase the start torque and increase the start speed of the fan. In the application of the fan, it can be set as $i_{q0}=\sqrt{3}{I}_n\times 25\%$, where I_n is the rated current 0.75 A of PMSM.

In normal PID control, gain parameters are fixed [27], so in constant airflow dynamic control, it is not possible to self-adjust the gains to the difference in addition to this, the airflow observer (Figure 7) is a testing results summary which is based on steady testing, in a fan’s actual dynamic working, there are always dynamic moment differs on the airflow compared with the observer data, in order to accelerate the constant airflow control system’s response speed, reduce the interference of differs during the dynamic process, we think a fuzzy PID control system introduced in is a suitable choice.

The fuzzy PID control system is represented in Figure 8. The basic theory of constant airflow fuzzy PID control algorithm is getting the differ ‘e’, which is between airflow set to the airflow observed first, using the differ ‘e’ changing ‘ec’ as the input, dynamic check the differ ‘e’ and the differ ‘e’ changing ‘ec’ during the constant airflow system working process, in the meantime, use the fuzzy rules to fuzzy inference, check fuzzy rules tablet, adjust the gain parameters which controlled by PID to meet the self-adjusting requests of ‘e’ and ‘ec’ to PID at different running times, it leads to the controlled object has a good static and dynamic performance.

The fuzzy PID control system has been widely applied in normal control processes; the Fuzzy PID control system self-adjusts the PID gains K_P, K_I, and K_D, according to the differ e and differ changing ‘ec’ values, it processes as below principles:

(1)

When the differ ‘e’ value is quite big: in order to accelerate the response speed, the value of K_P needs to be set big, too., In order to avoid possible differential supersaturation, the value of K_D needs to be set small; in the meantime, in order to prevent the overhead, which makes integral windup, the K_I. needs to be set equal to 0, which removes the integral action;

(2)

When differ e and differ change ‘ec’ value is medium in order to make the control system response has a less overshoot, the value of K_p needs to be set a little smaller, K_1′s value needs to be proper, at this time, the value of K_D has a big affecting to the system response; the value set need to be proper, not big, not small, to guarantee the control system’s response speed;

(3)

When differ ‘e’ is quite small, it means the control system’s output is close to the set value: in order to make the control system have a good steady, we need to increase the value of K_P, and K_I, in the meantime, in order to avoid control system has a vibration near the set value, and increase the control system’s anti-interference ability, the value of K_D is very important, normally, when ‘ec’ is small, the value of K_D needs to be set a little bigger, when ‘ec’ is big, the value of ‘ec’ need be set a little smaller.

According to the above principles, we make the input and output domain of the fuzzy PID control {–6, –5, –4, –3, –2, –1, 0, 1, 2, 3, 4, 5, 6}, its corresponding domain is {NB, NM, NS, ZO, PS, PM, PB}, and set up “delta” subordinating degree function. According to the PID fuzzy adjusting rules on different values, we obtain fuzzy adjustment rules of changeable output ΔK_P, ΔK_I, ΔK_D as below tables:

Table 1. Fuzzy adjustment rules of ΔK_P [28,29].

	E	NB	NM	NS	ZO	PS	PM	PB
Ec
NB		NB	NB	NB	NM	NM	0	0
NM		NB	NB	NB	NM	NM	0	PS
NS		NM	NM	NM	NS	0	PS	PS
ZO		NM	NM	NS	0	PS	PS	PM
PS		NS	NS	0	PS	PM	PM	PM
PM		NS	0	PS	PM	PM	PB	PM
PB		0	0	PM	PM	PM	PB	PB

,

Table 2. Fuzzy adjustment rules of ΔK_I [28].

	E	NB	NM	NS	ZO	PS	PM	PB
Ec
NB		NB	NB	NM	NM	NS	0	0
NM		NB	NB	NM	NS	NS	0	0
NS		NB	NM	NS	NS	0	PS	PS
ZO		NM	NM	NS	0	PS	PM	PM
PS		NM	NS	0	PS	PS	PM	PB
PM		0	0	PS	PS	PM	PB	PB
PB		0	0	PS	PM	PM	PB	PB

and

Table 3. Fuzzy adjustment rules of ΔK_D [28].

	E	NB	NM	NS	ZO	PS	PM	PB
Ec
NB		PS	NS	NB	NB	NB	NM	PS
NM		PS	NS	NB	NM	NM	NS	0
NS		0	NS	NM	NM	NS	NS	0
ZO		0	NS	NS	NS	NS	NS	0
PS		0	0	0	0	0	0	0
PM		0	PS	PS	PS	PS	PS	PB
PB		PB	PM	PM	PM	PS	PS	PB

and Figure 9. Among them, these tables use data from Na et al. [28] and Rui et al. [29].

According to Table 1, Table 2 and Table 3, use differ ‘e’ and differ ‘e’ changing ‘ec’ as the input values, go through the process of quantization and fuzzification, we obtain the fuzzy output values: ΔK_P, ΔK_I, ΔK_D, later obtain accurate output values through the fuzzification and quantifying factors, combine these output values with traditional PID to output the controller outputs, so the adjusting ways of the three parameters of the fuzzy PID controller are:

$$K_P=K_{P0}+\mathsf{\Delta }{K}_P$$

(9)

$$K_I=K_{l0}+\mathsf{\Delta }{K}_I$$

(10)

$$K_D=K_{D0}+\mathsf{\Delta }{K}_{D\psi }$$

(11)

In Formula (4), K_P0, K_I0, and K_D0 are the original values of the PID Controller gains. In the experiment, the values are set as K_P0 = 0.01, K_I0 = 0.5, K_D0 = 0.00002 [28].

2.3.2. Fuzzy PID Control Flow Chart Design

Based on the above fuzzy algorithm principles and rules, the fuzzy PID control flow chart is designed in Figure 10.

3. Experiment Samples Building

3.1. Fuzz Algorithm Controlled Fan Sample Building

Based on the logic of arithmetic control way above, we built up a single inlet OD:160 mm Impeller centrifugal fan to test and verify the effectiveness of the fan controlling. This fan is built up with a 160 mm out-diameter centrifugal fan impeller (Figure 11) with an aluminum fan house (Figure 12 and Figure 13). It is a forward curve centrifugal fan that is widely used in a lot of air supply systems to stimulate fan working conditions. The driving motor is a PMSM motor built with a PCB controller. The controller is mainly fabricated by a 40 MHz Texas Instrument DSP, model TMS320F280230, RAM 4 K, Flash ROM 16K, two current sensors, and a DC bus voltage sensor, the two current sensors are used to sample the three-phase current of the permanent magnet motor, and the DC bus voltage sensor calculates the three-phase output phase voltage through the internal three-phase voltage–duty ratio. The 3/2 conversion of coordinates module, and PWM wave generating module, over current and over temperature protection modules. The upper-level functions control unit is included by airflow observer and fuzzy control module. Figure 10 is the fuzzy PID control flow chart; the fuzzy PID control algorithm calculation is completed in a 10 ms period. For the fundamental FOC Control flow chart, see block diagram 2.

We programmed the constant airflow fuzzy algorithm controlling software inside the motor driver PCB. The basic driving part in the fan is a 200 W FOC-controlled PMSM motor. It includes a computing module of the phase angle, PMSM 3 phases currents sampling module, 3/2 conversion of coordinates module, and PWM wave generating module, over current and over temperature protection modules. The upper-level functions control unit is included by airflow observer and fuzzy control module. Figure 9 is the fuzzy PID control flow chart. The fuzzy PID control algorithm calculation is completed in a 10 ms period. For the fundamental FOC control flow chart, see block diagram 2.

The below specification is the fan motor specifications sheet

Table 4. Fuzzy algorithm controlled Fan’s motor specification.

Motor Max Output Power	200 (W)
Rated phase current	0.75 (A)
Pole numbers of the motor	8 Poles
Motor EMF coefficient	54.67 (V/rpm)
Stator phase resistance	22.4 (Ω)
Stator d axis Inductance	12.80 (mH)
Stator q axis Inductance	12.80 (mH)

in this fuzzy algorithm-controlled constant airflow fan experiment.

3.2. Fuzzy PID and Air Observer Built

In order to build up a suitable fuzzy PID and the air observer of the fan, this research needs to test the fan motor’s torque current, frequency, the voltage at different airflows, and different airflow at different block rates, find the coefficient, and build up their relations. Finally, we build up such a linear relationship between the fan motor’s output torque current coefficient KIT corresponding to airflow ratios.

Figure 14 explains that when a fan output a certain airflow, the fan motor’s one torque current could correspond to different airflows. For example, if the fan motor works at torque current 0.6 A, it could correspond to one airflow output at about 105 Hz for a 100% airflow ratio or one airflow output at about 145 Hz for a 40% airflow ratio or about 175 Hz 10% airflow ratio. Moreover, because the fan’s one torque current could correspond to different airflows outputted, barely relying on controlling the fan motor’s torque current or consumption power could not reach the target of constant airflow control. So compared with that research which focuses on trying to control a fan’s output by barely controlling the motor current or motor power consumption, this research is more accurate on a real fan’s output airflow control.

Figure 14 is used as the air observer in the fuzzy algorithm to quickly calculate and adjust the PID gains so that the fan can self-adjust its real output airflow and make it get closer to the target quickly.

3.3. No Fuzzy Algorithm-Controlled Experiment Fan Sample Building

In the meaning time, we built an AC (alternating current) no fuzzy algorithm-controlled fan (Figure 15) to compare testing.

The bellow specification sheet,

Table 5. Motor specifications used on the no fuzzy controlled fan for the experiment.

Motor Max Output Power	120 (W)
Rated phase current	0.75 (A)
Pole numbers of the motor	4 Poles
Resistance of main phase	31.4 Ohm
Resistance of Aux phase	32.6 Ohm
Capacitor	6 uF/450 VAC

, is the no fuzzy algorithm controlled fan’s AC motor in this experiment.

3.4. Testing Method and Testing Results Comparing

Constant airflow volume verification testing way: block the inlet area of the fan manually, area blocked rate from 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100%. Verifying without an airflow sensor, the fan can automatically adjust its output to keep the airflow volume constant.

Table 6. A total of 160 fuzzy controlled EC (electronic commutation fan (220 V 50 Hz input) vs. 160 no fuzzy controlled fan (220 V 50 Hz input).

Inlet Blocked Rate Sx/S0 (%)	Airflow (m/h)	Current (A)	Fan Impeller Speed (RPM)	Input Power (W)
0%	500 (499.75)	1.116 (0.98)	1959 (1958)	120 (193)
10%	495.2 (450.1)	1.10 (0.953)	1970 (2005)	119 (186)
20%	498 (427.2)	1.13 (0.913)	1980 (2116)	122 (180)
30%	485 (395.1)	1.05 (0.925)	2000 (2172)	115 (175)
40%	480 (375.1)	1.04 (0.905)	2060 (2198)	113 (167)
50%	470 (347.8)	1.02 (0.828)	2127 (2282)	108 (158)
60%	450 (318.1)	1.05 (0.802)	2419 (2367)	115 (151)
70%	370 (293.8)	1.12 (0.734)	2691 (2430)	121 (141)
80%	280 (238.1)	1.10 (0.661)	2922 (2548)	119 (125)
90%	168 (114.8)	0.85 (0.542)	3139 (2761)	86 (88)
100%	30 (30.2)	0.732 (0.514)	3168 (2848)	72 (74)

Note: Airflow is tested when air pressure is 0.

displays the testing results comparing of fuzzy controlled fan and a no fuzzy controlled fan.

Figure 16 is the airflow volume changes when fan’s inlet be different ratio blocked; the red curve is the fuzzy-controlled fan, and blue is the no fuzzy-controlled fan.

Figure 16 shows the power consumption of a fuzzy-controlled fan and no fuzzy-controlled fan when the fan’s inlet is different ratio blocked; the red curve is the fuzzy-controlled fan’s power consumption, and the blue curve is the no fuzzy-controlled fan’s power consumption.

Comparing the above curves, we can easily find that a fuzzy-controlled fan could output almost constant airflow volume when the inlet is blocked between 0% and 60%. When the inlet blocked rate is less than 20%, a fuzzy-controlled fan output airflow volume is kept almost the same, but a no fuzzy-controlled fan airflow output dropped by 14.52%. When the inlet is 60% blocked, a fuzzy-controlled fan airflow volume drops only by 10%, while a no fuzzy-controlled fan airflow only drops by 36.35%. This proves that a Fuzzy algorithm-controlled fan could perform a much better role on constant airflow output.

From the comparison of the two fans’ power consumption curves, we could easily find that a fuzzy algorithm-controlled fan is 37.82% energy saving compared with a no fuzzy-controlled fan when the inlet is 100% open (inlet 0% blocked), but if measuring the energy saving by effective airflow data, when the fuzzy-controlled fan inlet is 60% blocked, it output 450 cbm airflow, which is the same as the output of the no fuzzy algorithm-controlled fan when its inlet 10% blocked, and power consumption is still 38.17% saved. However, we also find that when both fans are 90% inlet blocked, a fuzzy-controlled fan has almost the same power consumption 86 W compared with a no fuzzy-controlled fan 88 W, but even so, it has an advantage on the airflow of about 46.34% more (168 cbm compared with 114.8 cbm).

4. Conclusions

The relationship between a fan motor’s power consumption to its airflow is some kind of quadratic relationship, and even at the same power consumption, when the air pressure changes, the airflow changes accordingly, so there is no direct relationship between airflow to fan motor’s power consumption or torque current. This research innovates by introducing an extra “airflow observer” into the traditional structure of the fuzzy algorithm to build up a new operation mechanism that makes the fuzzy control algorithm build up direct relationships among torque currents, frequencies, and airflows and through the airflow observer built through testing to make PID gains calculation much quicker and efficient so that the controlled fan could quickly perform a self-adaption job and output-constant airflows.

This research also proves that with a fuzzy control algorithm applied in the fan control, the fan could automatically self-detect and self-adjust its airflow volume in a certain range to be constant. This research finds that when the fan’s inlet blocked rate is less than 60% in the tested fan, the air fan could self-detect and self-adjust the airflow volume output to be almost the same. This research would expand the future product design of air supply systems in confined spaces such as aircraft cabins, submarines, tunnels, and buildings HVAC (Heating, Ventilation, air conditioning, cooling) systems without thinking of putting lots of airflow volume sensors and speculators, and ignore the attenuation of air pressure or increasing of the air resistance brought by the blocking of the air filters, or the air transmission pipes, it also helps to reduce the power consumption of energy by an average saving of 37%+, which could reduce the CO₂ emission, and save the costs on the hardware of controllers and sensors, simplify the air supply system operation and maintaining process and costs.

Compared with other control ways, such as purely using the direct relation of the motor’s input currents or powers to control the fan’s output airflows, the method of this research introduced is more accurate on constant airflow control because the fan motor’s one torque current could correspond to different airflows as Figure 14 explains, when a fan output one constant airflow, it could work at different input currents, different input powers and different frequencies if without the airflow observer which built first through testing, the computation of airflow control is very complex and large, but using the method this research introduced, the computation is not high, as measured in this research, the DSP used in the sample fan completes the airflow control through airflow observer and fuzzy PID controller while it is completing the 13 KHz FOC control, the load rate of DSP of whole computation is about 70%.

The limitation of this research is that the PID and air observer is built through testing based on a certain structure fan’s airflows and its electrical parameters; due to the complex no linear relationship between the fan house and the fan blades, there is no one common formula could be found that could be applied to calculate directly a fan’s airflow outputted simply through using this fan motor’s torque currents and frequencies, so we must build up an airflow observer based on this fan’s real airflows testings before we perform the programming. Though the programming is built based on one fan and is not applicable to other fans; however, the method introduced in this paper could be applied in other fans, too.

The future work would be developing several different airflow ranges fans, both on axial and centrifugal fans (from airflow 500CBM to 5000CBM per hour) to cover 90% of civil HVAC applications, and hopefully, we could find some relationships between different airflow ranges in one structure (axial or centrifugal), and relationships between different fan structures and airflows, which could be used in future programming to make this fuzzy algorithm be more applicable in different structures and different airflow range constant airflow fans’ designs.