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Engineering

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Control Performance Improvement of Hydro-Viscous Clutch Based on Fuzzy-PID Controller

: As a rotational speed controller, a hydro-viscous clutch (HVC) is usually used in the constant pressure water supply system to maintain the needed water pressure constant. However, when the hydro-viscous clutch is working, it often suffers from the problem of output rotational speed ﬂuctuation since the spool of proportional relief valve can easily get stuck. Consequently, water pressure will ﬂuctuate too. A special pump control system of HVC was proposed based on the Fuzzy-PID controller for the purpose of reducing the ﬂuctuation rate. The MATLAB simulation was carried out according to the mathematical model and the results show that the Fuzzy-PID control strategy is superior to traditional PID control. The corresponding experiment was performed and the result indicate that through applying the Fuzzy-PID controller based pump control system, the rotational output speed ﬂuctuation of HVC can be inhibited from ± 60 π to ± 6 π rad/min, and the water pressure ﬂuctuation is dropped from ± 0.1 to ± 0.002 MPa.

Introduction
Hydro viscous drive (HVD) is a new technology of hydrodynamic transmission developed in the 1970s. It relies on the viscosity of fluid to transmit power through the action of fluid shear force. In some heavy pumps industrial fields in waterworks, since its power is directly proportional to the cube of its rotational speed, a hydro-viscous clutch can save a lot of energy by changing the oil film thickness between two friction disks under different control oil pressure to achieve different output rotational speed [1,2].
In general, valves are used to control oil pressure for HVC because of their excellent dynamic performance. For example, a hydraulic serve valve was used to control pressure for HVC using the twin disk corporation (in the commonwealth of Pennsylvania of USA). The "Ω" valve was proposed by the Niigata Coveter Corporation (in the Niigata of Japan) to regulate system pressure for HVC. The proportional relief valve was chosen by Professor Wei to achieve different pressure for HVC [3,4]. However, valves can easily get stuck and lead to output rotational speed fluctuation. More researchers have focused on the stability of HVC. Mikael et al. [5] researched the engagement of the clutch, and the results showed that the output speed of the clutch can be smoothed through optimizing the controlling method of the propulsion cylinder pressure. Huang et al. [6] analyzed the influences of a small ripple of control oil pressure on the output speed of HVC, and concluded that HVC can work stably when the frequency of the control oil's pressure ripple is higher than 60 Hz and the peak is less than 0.05 MPa. Chen et al. [7] studied the soft start characteristic of HVD, and found that the poor pressure stability of the proportional relief valve under low flow and oil pressure conditions is the reason for the fluctuation of output speed of the hydro-viscous clutch. Furthermore, a special speed regulating valve was developed to improve the start-up steadiness of HVC.
All the above mentioned studies on the stability of HVC are based on the valve control method. However, the pump control system is also widely used in various industrial applications for achieving high energy efficiency and high reliability. FJTE, Ferreira et al. [8] compared the energy savings between a pump control system and a valve control system and pointed out that the reduction of life-circle cost can reach over 25% by using a variable speed pump-controlled system. Nicola et al. [9] studied the energy-saving performance in requirement-oriented irrigation systems supplied by variable speed pumps and concluded that through matching the displacement and the pressure needed by the network characteristic curve, energy savings of about 27-35% may be achieved for the irrigation stations. Mao et al. [10] proposed a hydraulic pump controlled system driven by a variable speed AC servo motor in a hydraulic injection molding machine and the results showed that it can perform good response and robustness against the external disturbance.
In order to improve the pump control system's performance while keeping its high energy savings, some researchers combined the valve control and pump control methods [11]. Zhao et al. [12] put forward a new control mechanism of valve-pump paralled variable structure control which could be accommodated between valve control and pump control to meet the control requirements, and concluded that the overall characteristic of the variable speed pump control system could be significantly improved. Gong et al. [13] proposed a compounded control system (valve control and pump control) for HVC, the pressure control range is increased by 38% under the low pressure condition.
To further improve the control characteristics of pump control system while simplifying the system, more researchers have focused on the controller design method [14][15][16]. Jahmeerbacus et al. [17] presented a flux control system of an AC motor driven pump based on the fuzzy logic strategy. Simulation results show that the fuzzy logic scheme has good tracking ability under strong disturbance. Kastrevc et al. [18] employed a Fuzzy-PID controller to improve the pressure control performance of the pump control system, experiments verified the apparent robustness of Fuzzy-PID controller.
All the above literature gives us a revelation that the performance of the pump control system with appropriate control algorithm is comparable to that of the valve control system. Consequently, a new pump control system for the HVC will be proposed to overcome the speed fluctuation in this work. Furthermore, in order to significantly improve the control performance of the new pump control system, a Fuzzy-PID controller will be established based on its theoretical model. Additionally, performances of both methods such as the tracking ability, overshoot, adjustment time and anti-interference ability will be compared and analyzed. Finally, an industrial experiment will be carried out to verify the simulation results. Table 1 shows the applied technical parameters of HVC. During the engineering application of HVC with a proportional relief valve, we could see the phenomenon that the output rotational speed of HVC will fluctuate wildly every 10 min [19]. As is known to all, frictional disks will wear down after running for a certain time, which will cause particles. Additionally, oil temperature will rise because of slip loss when the HVC is working at the slip condition. Then, sludge deposition will be produced in lubricant oil under a high temperature situation. Thus, impurities such as particles and depositions will result in the clogging problem for valves. Because the HVC was designed as a normal closed structure as is shown in Figure 1, that is to say, the frictional disks of HVC are clamped by preload spring in the initial state, system pressure of HVC will fluctuate according to the different positions of the relief valve's spool [20].

Overall and Detailed Design for HVC's Control System
In some cases, reliability is considered to be more important to dynamic performance. Therefore, the pump control system is usually used to replace the valve control system [21][22][23][24]. Compared to the proportional relieve valve, the gear pump requires less cleanliness of the oil. Therefore, the new pump system composed of a frequency converterelectric motor-gear pump is steadier than the valve control system. Its diagrammatic sketch is as follows. Figure 1 shows the signal transform process: Firstly, water pressure "P" can be transformed by a water pressure converter into electric current signal "I", which will be sent to PLC. Secondly, after dealing with the current signal, the current signal "I" will be sent by PLC to the frequency converter. Thirdly, based on the current signal, the frequency converter produces a specific frequency voltage signal to the electric motor. Fourthly, the gear pump will be powered by the electric motor at a certain speed and supply a required oil pressure for HVC. Finally, HVC outputs a certain speed for the water pump, which can be changed to adjust the water supply, so as to realize the regulation of the pipe pressure of waterworks. During the engineering application of HVC with a proportional relief valve, we could see the phenomenon that the output rotational speed of HVC will fluctuate wildly every 10 min [19]. As is known to all, frictional disks will wear down after running for a certain time, which will cause particles. Additionally, oil temperature will rise because of slip loss when the HVC is working at the slip condition. Then, sludge deposition will be produced in lubricant oil under a high temperature situation. Thus, impurities such as particles and depositions will result in the clogging problem for valves. Because the HVC was designed as a normal closed structure as is shown in Figure 1, that is to say, the frictional disks of HVC are clamped by preload spring in the initial state, system pressure of HVC will fluctuate according to the different positions of the relief valve's spool [20].
In some cases, reliability is considered to be more important to dynamic performance. Therefore, the pump control system is usually used to replace the valve control system [21][22][23][24]. Compared to the proportional relieve valve, the gear pump requires less cleanliness of the oil. Therefore, the new pump system composed of a frequency converter-electric motor-gear pump is steadier than the valve control system. Its diagrammatic sketch is as follows. Figure 1 shows the signal transform process: Firstly, water pressure "P" can be transformed by a water pressure converter into electric current signal "I", which will be sent to PLC. Secondly, after dealing with the current signal, the current signal "I" will be sent by PLC to the frequency converter. Thirdly, based on the current signal, the frequency converter produces a specific frequency voltage signal to the electric motor. Fourthly,the gear pump will be powered by the electric motor at a certain speed and supply a required oil pressure for HVC. Finally, HVC outputs a certain speed for the water pump, which can be changed to adjust the water supply, so as to realize the regulation of the pipe pressure of waterworks. Different from changing the output speed of electric motor by variable frequency speed control technology, the HVD technology is used to change the output speed of HVC, while the output speed of electric motor remains the same. When the water pressure of pipe network need to be increased, we can increase the output seed of HVC by reducing the distance between two friction disks, then the water pump will output more water flow, finally, the water pressure will rise, and vice versa. If we want the pressure of the pipe network to be kept constant during a period, then we should make the output speed of HVC stable and follow the change of the disturbance. Its schematic diagram (shown in Figure 2) is as follows Different from changing the output speed of electric motor by variable frequency speed control technology, the HVD technology is used to change the output speed of HVC, while the output speed of electric motor remains the same. When the water pressure of pipe network need to be increased, we can increase the output seed of HVC by reducing the distance between two friction disks, then the water pump will output more water flow, finally, the water pressure will rise, and vice versa. If we want the pressure of the pipe network to be kept constant during a period, then we should make the output speed of HVC stable and follow the change of the disturbance. Its schematic diagram (shown in Figure 2) is as follows. As shown in Figure 3, HVC is mainly composed of an input axis, a lubricating oil chamber, friction plates, separator plates, a hydraulic cylinder,an output axis and other parts. As shown in Figure 3, HVC is mainly composed of an input axis, a lubricating oil chamber, friction plates, separator plates, a hydraulic cylinder, an output axis and other parts.
As shown in Figure 3, HVC is mainly composed of an input axis, a lubricating oil chamber, friction plates, separator plates, a hydraulic cylinder,an output axis and other parts. The proposed pump control system of HVC is mainly composed of an electric motor, s frequency converter, a gear pump, a PLC, etc.
A branch of hydraulic oil passes through the filter, gear pump and safety valve and then returns to the oil tank. Safety valve 6 is used to set a limited oil pressure for the system. Another branch of the hydraulic oil passes from the electromagnetic valve to the tank, which can sharply drop the control oil pressure to 0 in emergency circumstances.
The third branch of oil goes directly into the cylinder of HVC. According to the different frequency produced by frequency converter, the gear pump can provide control oil with different pressure for HVC.
The speed sensor can read and feedback the speed signal to PLC. Through its analog modules, the speed signal can be converted into a current signal by PLC. Finally, different oil flow and pressure can be achieved by VVVF control.

Modeling and Simulation of the Pump Control System of HVC
As we know, it is difficult for the conventional PID control to achieve high control precision, because the constant pressure water supply system is characterized by nonlinearity, time varying and large inertia. Additionally, when the HVC is working at different output speed, its mathematic model is always changing. Therefore, it is difficult to precisely identify the HVC by the fixed PID parameters. As is known to all, Fuzzy-PID control can deal with such problems well because the PID parameters can be regulated according to the change of system model. Therefore, we designed a Fuzzy-PID controller according to the new pump control system of HVC. Its structural diagram is represented in Figure  4 and its theoretical model of each part can be established as below. The proposed pump control system of HVC is mainly composed of an electric motor, s frequency converter, a gear pump, a PLC, etc.
A branch of hydraulic oil passes through the filter, gear pump and safety valve and then returns to the oil tank. Safety valve 6 is used to set a limited oil pressure for the system. Another branch of the hydraulic oil passes from the electromagnetic valve to the tank, which can sharply drop the control oil pressure to 0 in emergency circumstances.
The third branch of oil goes directly into the cylinder of HVC. According to the different frequency produced by frequency converter, the gear pump can provide control oil with different pressure for HVC.
The speed sensor can read and feedback the speed signal to PLC. Through its analog modules, the speed signal can be converted into a current signal by PLC. Finally, different oil flow and pressure can be achieved by VVVF control.

Modeling and Simulation of the Pump Control System of HVC
As we know, it is difficult for the conventional PID control to achieve high control precision, because the constant pressure water supply system is characterized by nonlinearity, time varying and large inertia. Additionally, when the HVC is working at different output speed, its mathematic model is always changing. Therefore, it is difficult to precisely identify the HVC by the fixed PID parameters. As is known to all, Fuzzy-PID control can deal with such problems well because the PID parameters can be regulated according to the change of system model. Therefore, we designed a Fuzzy-PID controller according to the new pump control system of HVC. Its structural diagram is represented in Figure 4 and its theoretical model of each part can be established as below.

Frequency Converter
According to the electric motor of gear pump, we chose the F1000-G0007S2B type frequency converter, which can output a certain electrical signal. Its mathematical model is as follows where K f is the conversion coefficient of frequency-voltage. K int is the conversion coefficient of Frequency-current. I r is the input current, and U 1 is the output voltage.

Frequency Converter
According to the electric motor of gear pump, we chose the F1000-G0007S2B type frequency converter, which can output a certain electrical signal. Its mathematical model is as follows 1 i n t f r where Kf is the conversion coefficient of frequency-voltage. Kint is the conversion coefficient of Frequency-current. Ir is the input current, and U1 is the output voltage.

Gear Pump and the Cylinder of HVC
The gear pump is a linear component as its output flow can be controlled almost linearly to its output rotational speed. We chose the gear pump (CB-B10) as the hydraulic power source according to the required control oil pressure for HVC, its mathematical model can be obtained by ignoring the leakage: where K1 is the stiffness coefficient of spring (which can be seen as the load for the cylinder of HVC), Q is the flow of the cylinder, A is the area of the piston, βe is the bulk modulus of hydraulic oil, Ve is the equivalent volume of the cylinder, p is the pressure of the cylinder,and n is the output speed of the gear pump. V is the displacement of gear pump, and ηv is the volumetric efficiency of gear pump.

Gear Pump Motor
An asynchronous motor (YVP90S-4) was applied according to the power required by the gear pump. It is hard to obtain an accurate mathematical model of asynchronous motor as it is a high-order nonlinear system. By ignoring transient process of electromagnetic field, its electromagnetic torque equation can be obtained

Gear Pump and the Cylinder of HVC
The gear pump is a linear component as its output flow can be controlled almost linearly to its output rotational speed. We chose the gear pump (CB-B10) as the hydraulic power source according to the required control oil pressure for HVC, its mathematical model can be obtained by ignoring the leakage: where K 1 is the stiffness coefficient of spring (which can be seen as the load for the cylinder of HVC), Q is the flow of the cylinder, A is the area of the piston, β e is the bulk modulus of hydraulic oil, V e is the equivalent volume of the cylinder, p is the pressure of the cylinder, and n is the output speed of the gear pump. V is the displacement of gear pump, and η v is the volumetric efficiency of gear pump.

Gear Pump Motor
An asynchronous motor (YVP90S-4) was applied according to the power required by the gear pump. It is hard to obtain an accurate mathematical model of asynchronous motor as it is a high-order nonlinear system. By ignoring transient process of electromagnetic field, its electromagnetic torque equation can be obtained where m p is the magnetic pole number of the AC asynchronous motor, R 1 is the stator phase resistance of the asynchronous motor, R 2 is the rotor phase resistance equivalent to the stator side of the asynchronous motor, U 1 is the phase voltage of the stator, ω 1 is the rotational speed of the motor, s is the slip ratio of the asynchronous motor, L 1 is the stator leakage inductance of the asynchronous motor, L 2 is the rotor leakage inductance equivalent to the stator side of the asynchronous motor, and T n is the electromagnetic torque of the asynchronous motor.
When the asynchronous motor is working at the rated condition, Therefore, Equation (5) can be simplified as Because s = 1 − n n s , n s = 60 f m p , Therefore, Through ignoring energy loss, we can obtain From Equations (2), (4), (7) and (8), we can finally obtain the transfer function of the asynchronous motor as follows where K pn = β e K 1 β e A 2 + V e K 1

Modeling of HVC
HVC is the key part of the constant pressure water supply system, through which we can finally control the water pressure and flow. HVC transmits torque and power by oil film between its frictional disks. According to the Newton's inner friction law, oil film shear stress τ is related to input rotational speed of HVC n 1 , output rotational speed of HVC n 2 , oil dynamic viscosity µ and oil pressure of cylinder p, and the function can be described as τ = F 1 (µ, n 1 , n 2 , p) Therefore, the output torque transmitted by HVC can be obtained as where n is the number of frictional disks, r 2 is the outside radius, and r 1 is the inside radius. When HVC is working at the stable state, n 1 , µ can be regarded as constant, thus By increment linearization at the work point a(p a , n 2a , T a ), so Assume Thus ∆T l = K pa ∆p 1 + K na ∆n 2 (14) where K pa is the torque-pressure gain in, and K na is the torque-speed gain.
According to the Newton's second law, where K ma is the load factor, and J is the load moment of inertia. After Laplace transformation, Equation (13) becomes From Equations (14) and (16), we can finally obtain the transfer function of HVC as

Programmable Logic Controller
As we known, when working under the mixed friction condition, surfaces of the HVC'S frictional plates begin to contact together. Because of the complex and variable surface topography, the mixed friction sate is unstable. Therefore, closed-loop feedback control is indispensable for reducing the output speed fluctuation of HVC. We chose PLC as the control center, and it can be seen as a proportional coefficient of K n . Finally, we can obtain the transfer function block diagram of the new system shown in Figure 5.

Programmable Logic Controller
As we known, when working under the mixed friction condition, surfaces of the HVC'S frictional plates begin to contact together. Because of the complex and variable surface topography, the mixed friction sate is unstable. Therefore, closed-loop feedback control is indispensable for reducing the output speed fluctuation of HVC. We chose PLC as the control center, and it can be seen as a proportional coefficient of Kn. Finally, we can obtain the transfer function block diagram of the new system shown in Figure 5.  Table 2 is established based on the common fuzzy control regulation which is generally used in the controlling of HVC [25][26][27][28]. Each row of Table  2. from left to right is respectively the fuzzy control regulation of Kp, Ki, and Kd. The structural diagram of the Fuzzy-PID controller is shown in Figure 6, and the membership function and surface viewer of the Fuzzy-PID controller is shown in Figure 7. Selecting some simple membership functions that reflect the results of fuzzy inference can significantly simplify the calculation process of fuzzy inference. The shape of the membership function According to the actual parameters, we can obtain the simplified transfer function of the new pump control system of HVC as follows

Simulation of the New System
Based on the theoretical model, analysis of the conventional PID control and Fuzzy-PID control is carried out through MATLAB simulation. In accord with the parameters of the conventional PID control, the the Fuzzy-PID control's initial values are (K p = 1.7, K i = 0.15, and K d = 0.2) based on the rule of "Ziegler-Nichols", and the quantitative factor and proportional factor are, respectively, K e = 1, K ec = 0.55, ∆K p = 0.6, ∆K i = 0.8, and ∆K d = −0.2. The input (e, ec) and output variables (∆K p , ∆K i , ∆K d ) are described by seven fuzzy subsets {NB, NM, NS, ZO, PS, PM, PB}. Table 2 is established based on the common fuzzy control regulation which is generally used in the controlling of HVC [25][26][27][28]. Each row of Table 2. from left to right is respectively the fuzzy control regulation of K p , K i , and K d . The structural diagram of the Fuzzy-PID controller is shown in Figure 6, and the membership function and surface viewer of the Fuzzy-PID controller is shown in Figure 7. Selecting some simple membership functions that reflect the results of fuzzy inference can significantly simplify the calculation process of fuzzy inference. The shape of the membership function of fuzzy quantity is bell-shaped in theory, but it is often simplified to a triangle in the actual control process. As shown in Figure 7, the input (e, ec) and output variables (∆K p , ∆K i , ∆K d ) obey the triangular membership function. The centroid method has been applied for defuzzification, as it is the most commonly used method.    Three different simulation works were carried out using MATLAB. Through using a square wave as an input signal, three different output curves were indicated in Figure 8, we can see that the Fuzzy-PID control method has the best tracking ability, the smallest overshoot and the shortest adjustment time. Three different simulation works were carried out using MATLAB. T square wave as an input signal, three different output curves were indica we can see that the Fuzzy-PID control method has the best tracking abil overshoot and the shortest adjustment time. Increased an added interference signal by 0.5 after 5 s in each period curves are showed in Figure 9. It can be seen that the conventional PID co no anti-interference ability, while the Fuzzy-PID control is characterized ations and a strong anti-interference ability. Increased an added interference signal by 0.5 after 5 s in each period, the simulation curves are showed in Figure 9. It can be seen that the conventional PID control has nearly no anti-interference ability, while the Fuzzy-PID control is characterized by small fluctuations and a strong anti-interference ability.
Increased an added interference signal by 0.5 after 5 s in each period, curves are showed in Figure 9. It can be seen that the conventional PID con no anti-interference ability, while the Fuzzy-PID control is characterized b ations and a strong anti-interference ability.

Experiment Results
As shown in Figure 4, the following is the realization process of Fuzz in the experiment.
(1) Calculate the error "e" between the actual value of pipe pressure of w the feedback value. The real-time pipe pressure value can be obtaine pressure transmitter, and then the error of pipe pressure can be obtain ing with the target pressure value of the waterworks.

Experiment Results
As shown in Figure 4, the following is the realization process of Fuzzy-PID control in the experiment.
(1) Calculate the error "e" between the actual value of pipe pressure of waterworks and the feedback value. The real-time pipe pressure value can be obtained through the pressure transmitter, and then the error of pipe pressure can be obtained by comparing with the target pressure value of the waterworks. (2) Calculate the error change rate "de/dt". The error is differentiated to calculate the change of error in an A/D sampling period. (3) Fuzzification of input. The obtained accurate error "e" and error change rate "e c " are fuzzed into fuzzy quantities. (4) Establish control rules. Establish the strategies required in the control with the knowledge of experts or the experience of field operators, as shown in Table 2. (5) Fuzzy inference. The fuzzy inference input variables E and EC are used as the inputs of the fuzzy inference part, and then the fuzzy inference is carried out by E, EC and the control rule R according to the inference synthesis rule to obtain the fuzzy control quantity U. (6) Defuzzification. In order to apply accurate control to the controlled object, the fuzzy control quantity must be transformed into an accurate quantity, that is, defuzzification. (7) Real time regulation of PID control parameters. After the fuzzy control quantity is transformed into an accurate quantity, the three control parameters K p , K i and K d of PID are modified in real-time to make the control system reach the optimal control state. (8) Regulation of frequency converter output frequency. The three control parameters of PID are modified in real time to adjust the output frequency of frequency converter and make the oil pump motor of control oil circuit work at an appropriate frequency, so as to realize the regulation of the output speed of HVC. (9) Regulation of the pipe pressure of waterworks. By changing the working frequency of the oil pump motor in the control oil circuit, the output speed of the hydro-viscous clutch (i.e., the speed of the water pump) is changed to adjust the water supply, so as to realize the regulation of the pipe pressure of waterworks. The HVC with the special pump control system has been successfully applied in the 4th waterworks of Shaoyang (Hunan province). The field and experimental data are shown below (Figures 10 and 11).
PID are modified in real time to adjust the output frequency of frequency converter and make the oil pump motor of control oil circuit work at an appropriate frequency, so as to realize the regulation of the output speed of HVC. (9) Regulation of the pipe pressure of waterworks. By changing the working frequency of the oil pump motor in the control oil circuit, the output speed of the hydro-viscous clutch (i.e., the speed of the water pump) is changed to adjust the water supply, so as to realize the regulation of the pipe pressure of waterworks.
The HVC with the special pump control system has been successfully applied in the 4th waterworks of Shaoyang (Hunan province). The field and experimental data are shown below (Figures 10 and 11).   state. (8) Regulation of frequency converter output frequency. The three control parameters of PID are modified in real time to adjust the output frequency of frequency converter and make the oil pump motor of control oil circuit work at an appropriate frequency, so as to realize the regulation of the output speed of HVC. (9) Regulation of the pipe pressure of waterworks. By changing the working frequency of the oil pump motor in the control oil circuit, the output speed of the hydro-viscous clutch (i.e., the speed of the water pump) is changed to adjust the water supply, so as to realize the regulation of the pipe pressure of waterworks.
The HVC with the special pump control system has been successfully applied in the 4th waterworks of Shaoyang (Hunan province). The field and experimental data are shown below (Figures 10 and 11).   From Figure 12 we can see, with the proportional relieve valve, when the transmission ratio is set to 0.84, the output speed of HVC increases from 630 to 660 r/min after 16 min then drops to 604 r/min after 28 min. When the transmission ratio is set to 0.92, the output speed of HVC increases from 690 to 723 r/min after 10 min then drops to 661 r/min after 22 min, and then rises to 716 r/min after 32 min. Correspondingly, as it shows in Figures 13 and 14, when the transmission ratio is 0.84, the pipe pressure of waterworks rises from 0.3 to 0.41 MPa after 16 min then drops to 0.18 MPa after 28 min. When the transmission ratio is 0.92, the pipe pressure of the waterworks rises from 0.32 to 0.44 MPa after 10 min then drops to 0.195 MPa after 22 min, and then rise to 0.43 MPa after 32 min.
From Figures 12-14 we can also see, after the improvement of PID control, the output speed fluctuation of the new pump control system is nearly decreased from ±60π to ±10π rad/min. Moreover, based on the Fuzzy-PID controller, the output speed fluctuation is decreased to ±6π rad/min and the pressure fluctuation of distribution network is decreased to ±0.002 MPa, which verified the previous theoretical analysis and fulfilled the requirements of water works well.
As it shows in Table 3, when reducing the transmission ratio of HVC from 1 to 0.82, the current consumed by DC motor decreased significantly from 2 to 11.9 A, and the smaller transmission ratio of HVC is, the more obvious the energy-saving effect is. creased to ±0.002 MPa, which verified the previous theoretical analysis and fulfilled the requirements of water works well. As it shows in Table 3, when reducing the transmission ratio of HVC from 1 to 0.82, the current consumed by DC motor decreased significantly from 2 to 11.9 A, and the smaller transmission ratio of HVC is, the more obvious the energy-saving effect is.

Conclusions
Traditional HVC generally uses valve control system to control the output spe clutch. Due to the dead zone, oil pollution and other reasons, there is a problem of water pressure fluctuation in the HVC's application of a constant pressure water su system. Because the control accuracy of pump control system is lower than that of ditional valve control system, in order to meet the high-precision requirements of and further expand its application field, the Fuzzy-PID control method is proposed. F all the above analysis, the following conclusions can be drawn.
(1) A new pump control system which is composed of a frequency converter, an-ele motor and a gear pump was used to replace the proportional relief valve for H which can solve the blockage and dead-zone problem of the valve control syste (2) A mathematical model of the pump control system was established, transfer fun of which can be simplified to a second order system. (3) In order to improve the performance of the new pump control system, a Fuzzy controller was designed. The simulation results show that: compared with the ventional PID control, the Fuzzy-PID control method has a better tracking abil

Conclusions
Traditional HVC generally uses valve control system to control the output speed of clutch. Due to the dead zone, oil pollution and other reasons, there is a problem of large water pressure fluctuation in the HVC's application of a constant pressure water supply system. Because the control accuracy of pump control system is lower than that of a traditional valve control system, in order to meet the high-precision requirements of users and further expand its application field, the Fuzzy-PID control method is proposed. From all the above analysis, the following conclusions can be drawn.
(1) A new pump control system which is composed of a frequency converter, an-electric motor and a gear pump was used to replace the proportional relief valve for HVC, which can solve the blockage and dead-zone problem of the valve control system. (2) A mathematical model of the pump control system was established, transfer function of which can be simplified to a second order system. (3) In order to improve the performance of the new pump control system, a Fuzzy-PID controller was designed. The simulation results show that: compared with the conventional PID control, the Fuzzy-PID control method has a better tracking ability, a smaller overshoot, a shorter adjustment time and a stronger anti-interference ability. Therefore, the Fuzzy-PID control method is more suitable for HVC. (4) To verify our theoretical analysis results, a relative industrial experiment was carried out and the tested data show that, by applying the pump control system of HVC based on the Fuzzy-PID controller, the speed fluctuation range of HVC is reduced to ±6π rad/min, the pressure fluctuation of distribution network is reduced to ±0.002 MPa, and the current consumed by DC motor decreased significantly from 28 to 11.9 A.


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Ideal Ratio Mask Estimation using Supervised DNN Approach for Target Speech Signal Enhancement

The most challenging process in recent Speech Enhancement (SE) systems is to exclude the non-stationary noises and additive white Gaussian noise in real-time applications. Several SE techniques suggested were not successful in real-time scenarios to eliminate noises in the speech signals due to the high utilization of resources. So, a Sliding Window Empirical Mode Decomposition including a Variant of Variational Model Decomposition and Hurst (SWEMD-VVMDH) technique was developed for minimizing the difficulty in real-time applications. But, this is the statistical framework that takes a long time for computations. Hence in this article, this SWEMD-VVMDH technique is extended using Deep Neural Network (DNN) that learns the decomposed speech signals via SWEMD-VVMDH efficiently to achieve SE. At first, the noisy speech signals are decomposed into Intrinsic Mode Functions (IMFs) by the SWEMD Hurst (SWEMDH) technique. Then, the Time-Delay Estimation (TDE)-based VVMD was performed on the IMFs to elect the most relevant IMFs according to the Hurst exponent and lessen the low- as well as high-frequency noise elements in the speech signal. For each signal frame, the target features are chosen and fed to the DNN that learns these features to estimate the Ideal Ratio Mask (IRM) in a supervised manner. The abilities of DNN are enhanced for the categories of background noise, and the Signal-to-Noise Ratio (SNR) of the speech signals. Also, the noise category dimension and the SNR dimension are chosen for training and testing manifold DNNs since these are dimensions often taken into account for the SE systems. Further, the IRM in each frequency channel for all noisy signal samples is concatenated to reconstruct the noiseless speech signal. At last, the experimental outcomes exhibit considerable improvement in SE under different categories of noises.

I. INTRODUCTION
In the globalized era, the most essential for SE is rejecting the microphone interferences due to the noises in the speech utterances. Several researchers have been designed many SE techniques for diminishing different categories of noises from the speech signals [1]. The compromised accurate assessment of the noise information is the key challenge in the SE systems. The standard assessments are primarily focused on Voice Activity Detectors (VAD) [2][3]. Later, the power distribution of noise characteristics was chosen as a smooth modification of its prior ranges for the speech duration. This approach suggests fair reliability for stationary noises, but the time-varying spectrum was not precisely assessed. For long speech segments and poor SNR, the difficulty of recognizing non-stationary noises emerges towards more intuitive [4]. These criteria were tackled by different power spectrum-based strategies [5][6].
The SE systems based on Time-Frequency (TF) [7] were suggested in preceding decades by the EMD method [8]. The speech signals were decomposed into a set of oscillatory IMF and a residual feature [9]. This method does not involve the set of essential components to analyze the target signal effectively. As well, it should not limit the stationary signals. So, a new SE technique based on EMD was suggested to resolve issues in non-stationary noisy scenarios [10]. Every IMF's noise characteristics were defined and chosen via its Hurst exponent data. Also, IMF selection and speech recovery were conducted in frame-by-frame via accounting target prediction for characteristics and reliability. Regardless, this technique has a high consumption of time and energy. Therefore, a significant improvement may not have been successful under Babble noise scenarios.
So, the SWEMDH technique [11] was recommended according to the EMD estimation in a fairly limited window that is sliding with the time axis. The window size was dependent on the speech signal frequency spectrum. The specific IMF distortions between windows were eliminated through the number of modes and the filtering iterations. For every element, the number filtering iterations can be adjusted based on the sampling rate, assessed signal, its consistency, and frequency band. This was done via decomposing the signal windows according to the usual method and calculating the actual number of filtering iterations. Also, the window sliding process was achieved by extracting the features related to the window after obtaining the mode in the current iteration. These features were accumulated in the IMFs array with a suitable time index. It may happen that the window dimension was very small for spline interpolation so various window dimensions were needed for every mode. In this system, the default window dimension was used. The time indexes of the initial windows in the current iteration were stored. If the mode was extracted efficiently, then this index was maximized and the window for this mode was sliding forward in the subsequent iteration. If there were not adequate extrema, then the sliding task was terminated and the index of the initial windows for the current mode was not shifted. So in the subsequent iteration, the window was increased 2 times. On the contrary, the SWEMDH was not successful in backgrounds with white noise. As a result, a SWEMD-VVMDH technique was developed for minimizing the difficulty in real-time applications [12]. First, the noisy speech signals were decomposed into IMFs using the SWEMDH technique. Then, the TDE-based VVMD was performed on the IMFs to elect the most relevant IMFs according to the Hurst exponent and lessen the low-as well as high-frequency noise elements in the speech signal. Accordingly, the SE was achieved under various noise scenarios include additive white Gaussian noise, street noise, Babble noise, airport noise, and so on. Nonetheless, these are statistical frameworks that take a long time for computations. Hence, it needs to introduce advanced deep learning techniques to reduce the computational difficulty and improve the speech signal quality efficiently. Therefore in this paper, the SE technique is developed using the DNN that learns the decomposed speech signals via SWEMD-VVMDH for enhancing the Speech Quality (SQ) efficiently. The main goal of SWEMD-VVMDH-DNN is to execute a systematic analysis of the generalizability abilities of the SE technique based on the estimated SQ and Speech Intelligibility (SI). For each signal sequence/frame, the target features are chosen and fed to the DNN that learns these features to estimate the IRM in a supervised manner. The abilities of DNN are enhanced for the categories of background noise, and the SNR of the target speech signals. Also, the DNN framework is differed and measured as a dimension. Besides, the noise category dimension and the SNR dimension are chosen for training and testing manifold DNNs since these are dimensions often taken into account for the SE systems.
Further, the IRM in each frequency channel for all noisy signal samples is concatenated to reconstruct the noiseless speech signal.
The remaining article is prepared as follows: Section II presents the previous studies related to the SE systems using deep learning methods. Section III explains the SWEMD-VVMDH-DNN technique and Section IV displays its performance. Section V summarizes the work.

II. LITERATURE SURVEY
Xu et al. [13] designed a huge training set and fed it to the DNN for classifying the speech signals. Also, global variance equalization was used to avoid the over-smoothing issue of the regression framework. Further, the dropout and noise-aware training methods were employed for enhancing the generalization ability of DNN to invisible noise criteria. However, only a small number of data was used that cannot help to achieve better coverage of various acoustic situations like the speaker and morphological inconsistencies.
Williamson et al. [14] presented a scheme for enhancing the perceptual quality of speech segregated from background noise at low SNRs. First, an IRM was determined which segregates the speech from noise with reasonable sound quality. After, the DNN was used for approximating the clean speech via determining the activation weights from the ratio-masked speech where the weights linearly pool components from a Nonnegative Matrix factorization (NMF) speech framework. But, the performance was not effective.
Lee et al. [15] defined a new Spectro-Temporal Detection-Reconstruction (STDR) scheme in which the speech was extracted from background noise via learning a spectro-temporal feature space continuously. Also, a static nonlinearity was applied for projecting the noisy speech. Then, time-frequency gains were decoded together to adjust the noisy speech for creating clean speech estimation. But, the performance was not effective while considering the small amount of data.
Huang et al. [16] developed a common monaural source segregation scheme for mutually modeling all sources within a combination as targets to a Deep Recurrent Neural Network (DRNN). In this scheme, the limits between the actual combination and output predictions were used via time-frequency mask functions and mutually optimizing the time-frequency functions through DRNN. In contrast, the vanishing gradient properties of DRNN were not discarded that may affect the performance.
Chen et al. [17][18] suggested a DNN-based supervised speech segregation model with largescale training for enhancing speech intelligibility. The trained DNN was employed for segregating the speech from noises such as multi-talker babble and cafeteria noise. But, it was not suitable for real-time uses since it considers only magnitude spectra. Also, the generalizability dimensions were predetermined i.e., the dimensions of feed-forward DNN were fixed.
Amodei et al. [19] developed RNN with one or more convolutional layers followed by many recurrent layers, one fully connected layer, and a softmax layer. The entire network was trained end-to-end via the Connectionist Temporal Classification (CTC) loss function which facilitates direct recognition of the sequences of characters from the input speech signal. Conversely, the training was difficult while increasing the network's size and depth i.e., using more recurrent layers.
Zhang et al. [20] proposed multi-context networks for analyzing the monaural speech segregation. The initial multi-context network was used for averaging the outputs of many DNNs whose inputs use various window lengths. The second multi-context network was a stacked multiple DNNs where every DNN yields the concatenation of actual acoustic features and expanses the soft output of the lower unit as its input to identify the IRM of the target speaker. But, gradient descent-based training may cause vanishing gradient problems and degrade the efficiency.
Vidya et al. [21] preferred Hilbert Huang transform which consists of EMD for producing the IMFs and Hilbert spectral testing for detecting the regional properties of the speech signal.
Also, Hilbert amplitude spectrum and phase spectrum were investigated to prevent the direct amplitude and phase of a speech signal. Moreover, the marginal and normalized Hilbert spectrum was determined. But, it needs to develop the analysis for further processing.
Mukherjee et al. [22] suggested a VAD method that utilizes the Line Spectral Frequencybased Statistical attributes called LSF-S merged with an extreme learning-based classifier.
First, the audio signals were pre-processed and the attributes of multifarious sizes were extracted. These attributes were given to the extreme learning-based classifier to detect the voice and non-voice signals. But, it does not handle the live audio signals and needs to analyze the robustness under different noise scenarios.
Karjol et al. [23] presented a variant of a multiple DNN-based SE scheme which directly estimates the clean speech spectrum as a weighted mean of outputs from manifold DNNs.
Initially, the weights were obtained via a gating network. After, the manifold DNNs and the gating network were trained together. Moreover, the objective function was assigned as the mean square logarithmic error between the target clean spectrum and the estimated spectrum.
In contrast, it requires optimization of DNN parameters for further performance improvement.
Zhao et al. [24] suggested a short-time objective intelligibility factor in the loss along with the Mean Squared Error (MSE) for improving the SE system. The perceptually guided loss was optimized using DNN according to the use of reinforcement learning with the previously defined time-varying rewards and a group of mask templates for further improving the speech intelligibility. However, the efficiency was degraded due to the fixed hyper-parameter during training.

Saleem et al. [25] suggested a Relative Spectral Transform-Perceptual Linear Prediction
(RASTA-PLP) for extracting the acoustic features at the frame level. Also, an Auto-Regressive Moving Average (ARMA) filter was used for smoothing the temporal curves of extracted features. Then, the less aggressive Wiener filter was employed as an extra layer on the top of a DNN for generating an improved magnitude spectrum. At last, the noisy speech phase was used for restoring the enhanced speech. But, the parameters used in DNN were fixed i.e., fixed-size DNN cannot able to effectively improve the performance.
Wang et al. [26] developed the DNN framework for every microphone to improve the recorded noisy speech signal and all the noisy recordings were fused into the huge feature structure. A channel-dependent DNN was applied for enhancing the respective noisy input and all the channel-wise improved outcomes were fed to the DNN fusion framework for creating the almost clean signal. On the other hand, the recording channels were not accurately chosen rather than using all the channels as input.
Khaleelur Rahiman et al. [27] decomposed the noisy speech signal into frames and these were fed to the deep Convolutional Neural Network (CNN) for estimating the frequency channel. Then, the speech-dominated cochlear implant channels were considered for generating the electrical stimulation according to the estimated frequency channels. But, the parameters like window function and stride used in CNNs were fixed.
Llombart et al. [28] developed the progressive SE using convolutional and residual neural network structures. In this system, 2 different conditions were used for optimizing the loss factor such as weighted and homogeneous progressive. But, it needs more signals to provide better performance.

III. PROPOSED METHODOLOGY
In this section, the proposed SWEMD-VVMDH-DNN technique for achieving SE is explained briefly. The major tasks in the SWEMD-VVMDH-DNN technique are depicted in Figure 1.

Speech Signals Decomposition using SWEMD-VVMDH Technique
First, the noisy speech signals are decomposed into the group of IMFs using the SWEMDH scheme [11]. Let be the amount of IMFs extracted. For every IMF, the Hurst exponent is calculated and the IMFs having ≤ Then, the sum of all IMFs including input parameters is fed to the VVMD method to decompose into the group of Narrow-Band Components (NBCs) [12].

Feature Extraction and Labeling
The selection of training targets such as IRMs is desirable. So, the DNN is trained in a supervised manner for estimating the IRM from a feature representation of a noisy speech signal. The TF representation used for making the IRM is according to the gammatone filter bank with 64 filters linearly spaced on a Mel-frequency scale from 50Hz-8kHz and with a bandwidth identical to one Equivalent Rectangular Bandwidth (ERB). The output of the filter bank is split into 20ms frames with 10ms overlap and a sampling frequency of 16kHz, every TF unit denotes a vector of 320 samples. These frames and sampling frequency are highly satisfactory i.e., optimal to achieve a better spectral resolution of speech signals and mostly preferred to reconstruct the speech signal from its short-time magnitude spectrum Consider ( , ) indicate the TF unit of the noiseless speech signal at frame and frequency channel . Also, consider ( , ) be the respective TF unit of the noise signal.
After, the IRM is determined as:

DNN Architecture and Training
The DNNs follow a feed-forward framework with an 1845-dimensional input layer and three hidden layers and 64 output units. Each hidden layer has 1024 hidden units. The activation functions for the hidden units are Rectified Linear Units (ReLUs) and the sigmoid function is used for the output units. The DNN structure for SE is portrayed in Figure 2 wherein 1 , … , are the input units i.e., speech signals and is the estimated ̂( , ).

Figure 2. Architecture of DNN for SE
The hidden layers are initialized via the GlorotUniform scheme [29]. Moreover, the DNN has nearly 4M tunable parameters such as weights and biases. The values of these parameters are determined by the Stochastic Gradient Descent (SGD) following the AdaGrad method [30].
The gradients are calculated by the backpropagation according to the MSE function using a batch size of 1024. Also, 20% dropout is applied to each hidden layer during training for decreasing the overfitting. To further diminish the problem of overfitting, an early-stopping training mechanism is used which terminates the training while the MSE of the validation set is not reduced with greater than 1% for more than 20 epochs.

Speech Signal Restoration
Once DNN training is completed, the IRM is determined for a given test speech signal

IV. EXPERIMENTAL RESULTS
In this section, the SWEMD-VVMDH-DNN technique is implemented for evaluating its efficiency as compared to the SWEMD-VVMDH using MATLAB 2014a which has many audio tools and deep learning functions for execution. In this experiment, the TIMIT database 15dB. These categories of noises are collected from the NOISEX-92 database [32].
Therefore, a total of 36×24=864 testing are carried out (9 categories of noises×4 categories of SNR=36). Figure 3 represents the magnitude spectrogram of a sample speech signal.

Figure 3. Magnitude Spectrogram of Sample Speech Signal
The performance metrics used for evaluation are:  SNR: The rate of a mean power of speech signal ( ) to the mean power of noise ( ) is called the SNR.

 Weighted Spectral Slop Measure (WSSM): It is the interval averaged WSSM
where only the best frames are averaged. It computes the weighted variances of spectral slope over 25 critical frequency bands between any 2 related signal frames.
In Eq. (8), ∆ ( ) and ∆ ̅ ( ) denote the spectral slope at frequency band and ̅ , ( ) refers to the weight in every band and stands for the amount of best frames.

Figure 5. MAE Comparison
The graphical representation of MAE values for EMDH, SWEMDH, VVMDH, and DNN using different acoustic noises is portrayed in Figure 5. The x-axis denotes the different techniques and the y-axis denotes the MAE ranges. This analysis indicates that the DNN can reduce the MAE compared to other methods. If the airport noise circumstance is taken into account with the SNR of 10dB, the MAE of DNN is 62.88%, 31.98%, and 19.04% reduced compared to the EMDH, SWEMDH, and VVMDH. This is because of an accurate tradeoff for mistakes between the significant bands and their respective modes which are concurrently determined via DNN. Table 3 lists the SNR outcomes for EMDH, SWEMDH, VVMDH, and DNN using different acoustic noises that corrupt the speech signal during transmission.   respectively. This is attained due to the narrow-band properties related to the current measure of the mode's center-frequency to the signal estimation residual of all other modes. Table 4 gives the PSNR results for EMDH, SWEMDH, VVMDH, and DNN using different acoustic noises that corrupt the speech signal during transmission.

Figure 7. PSNR Comparison
The graphical representation of PSNR values for EMDH, SWEMDH, VVMDH, and DNN using different acoustic noises is depicted in Figure 7. The x-axis denotes the different techniques and the y-axis denotes the PSNR ranges (in dB). This analysis observes that the DNN can attain the maximum PSNR compared to other methods. E.g., if airport noise with the SNR of 10dB is considered, the PSNR of DNN is 41.92%, 7.81%, and 4.11% maximized compared to the EMDH, SWEMDH, and VVMDH. Table 5 gives the PESQ outcomes for EMDH, SWEMDH, VVMDH, and DNN using different acoustic noises that corrupt the speech signal during transmission. Similarly, Figure   8 displays its graphical representation. Here, the x-axis denotes the different techniques and the y-axis denotes the PESQ ranges. This scrutiny identifies that the DNN can improve the PESQ compared to other methods. E.g., consider the airport noise with the SNR of 10dB, the PESQ of DNN is 15.24%, 10.3%, and 5.47% maximized than EMDH, SWEMDH, and VVMDH. This is realized due to the proper adjustment of the center-frequency of low-and high-frequency harmonics which are identified at the tolerable quality and restored without faults.   Table 6 gives the WSSM results for EMDH, SWEMDH, VVMDH, and DNN using different acoustic noises that corrupt the speech signal during transmission. Additionally, the computational complexity of DNN for features is ( ) + ( ) complex multiplications and additions; whereas, the other techniques need the computational complexity of ( 2 log 2 ) + ( log 2 ) complex multiplications and additions.

V. CONCLUSION
In this article, the SWEMD-VVMDH-DNN technique is proposed for learning the speech  The capabilities of state-of-the-art DNN is discovered with respect to the types of background noise, the gender of the target speaker and the SNR.
 the architecture of DNN may be varied and taken as a dimension. For this proposed approach, the noise type dimension and the SNR dimension are selected to train and test all DNN based speech enhancement systems.

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Authors' contributions
A DNN based approach does have potential to improve Speech Enhancement in a broader range of usage Situations and it support that matching the noise type is critical in acquiring good performance for DNN based SE algorithms.