Damping multimodel adaptive switching controller design for electronic air suspension system
Xiaoqiang Sun^{1} , Yingfeng Cai^{2} , Shaohua Wang^{3} , Long Chen^{4}
^{1, 2, 3, 4}School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, China
^{4}Corresponding author
Journal of Vibroengineering, Vol. 17, Issue 6, 2015, p. 32113223.
Received 8 June 2015; received in revised form 21 August 2015; accepted 28 August 2015; published 30 September 2015
JVE Conferences
This paper presents the design and verification of a damping multimodel adaptive switching controller for electronic air suspension (EAS) system. In order to improve the convergence rate of identification algorithm of conventional adaptive controller, multiple local linear fullcar vehicle models of EAS system with fixed parameters are established according to the actual damping control process of EAS for different vehicle driving conditions and an adaptive model whose initial value of parameters can be reassigned is introduced to enhance the system control precision. The model switching control strategy based on minimum error is used to select the best matching model online and the optimum damping force is regulated by adaptive control algorithm, thus constituting the damping multimodel adaptive control for EAS. Simulation results show that the control method proposed in this paper can improve the damping regulating performance of EAS effectively in wide range driving conditions, especially for the case of sudden change in driving conditions.
Keywords: electronic air suspension, damping control, multimodel adaptive switching control, fullcar model.
1. Introduction
As an important component of vehicle chassis, suspension plays a pivotal role to guarantee the ride comfort, handling stability and driving safety for the moving vehicle [1]. In the past few decades, active or semiactive suspension, which utilize advanced electronic control technology, have received extensive attentions in automotive industry [24]. Active suspension can improve suspension performance by controlling the force generated by means of an actuator, but high cost, large energy consumption and control time delay become important factors to restrict its development [5, 6]. Relatively speaking, semiactive suspensions are widespread in industrial application because of their performance increase compared to passive suspensions and their lower energy consumption and control cost compared to active systems [7]. Many methods for controlling semiactive suspension systems have been proposed, while although they have been confirmed that have the ability to offer performance benefits for semiactive suspensions, the great majority researches are conducted based on the spring stiffness is not variable.
EAS system uses electronic technology to control vehicle height and semiactive damping force, thus achieving automatic modulation of suspension characteristics appropriate to riding, handling and road conditions [810]. An important difference between the air suspension and the traditional passive suspension is that the stiffness of air spring is variable, which brings new challenges for the controlling of damping force for EAS system [11]. Adaptive control is one of the advanced control methods that are aimed at a class of controlled objects which have varying degrees of model uncertainty [12, 13]. It can provide high quality control performance and achieve optimal regulation of system states on condition that the model parameters are incomplete or poorly understood. However, due to the obvious time varying and sudden change phenomena of model parameters of EAS system, the identification algorithm convergence rate of conventional adaptive controller can’t follow the actual change speed of model parameters, which gives rise to the inaccurate of identification model and then lead to poor performance of the adaptive controller based on this model.
In this paper, we describe how the multimodel adaptive switching control framework can be successfully applied for solving the damping control problem of EAS in a systematic way. Nine linear fullcar models with fixed parameters and an adaptive model of EAS system are established and a model switching control strategy is designed. The simulation response of the multimodel adaptive switching controller is compared with that of the conventional adaptive controller to evaluate corresponding performances finally.
2. Damping control process analysis of EAS
2.1. Variable stiffness characteristics of air spring
The air spring considered in this study is a diaphragm air spring with its piston cylindrical. It contains head, piston and sleeve as shown in Fig. 1. The base of the rubber sleeve is fastened to the top of the piston and rolls along the lateral surface of the cylindrical piston.
Fig. 1. Diaphragm air spring
Equation of the vertical elastic force of air spring is given by the following:
where $F$ is the vertical elastic force of air spring, ${p}_{as}$ is the absolute pressure inside air spring, ${p}_{a}$ is the atmospheric pressure, ${A}_{e}$ is the crosssectional area of air spring, ${p}_{e}$ is the relative pressure inside air spring. According to the definition of spring stiffness, the stiffness of air spring can be obtained as follows [14]:
where $k$ is the stiffness of air spring, $s$ is the vertical displacement of air spring.
The gas process that defines the relationship between pressure and specific volume in air spring is given by the polytropic relationship:
where ${V}_{as}$ is the specific volume of air spring, $n$ is the polytropic index. Differentiation of Eq. (3) of $s$ gives:
Therefore the differentiation of ${p}_{e}$ of $s$ can be obtained as:
For the diaphragm air spring with its piston cylindrical, the crosssectional area is assumed to be constant in normal working stroke, so the air spring stiffness can be further obtained as:
where ${H}_{as}$ is the vehicle height. From Eq. (6), it can be seen obviously that the absolute pressure inside air spring and the air spring specific volume have direct influence on the air spring stiffness. Among of them, the ${p}_{as}$ mainly depends on the vertical load of air spring, while the ${V}_{as}$ is impacted by the length of air spring.
2.2. The influence of vehicle height adjustment on air spring stiffness
Vehicle height adjustment is one of the main features of EAS and it consists of two parts: the general adjustment and the stable adjustment. Fig. 2 shows the vehicle height adjustment strategy. The general adjustment is aimed at regulating the vehicle height for different driving conditions, for example, when the car is driving on offroad way, the vehicle height adjustment system can lift the vehicle height from “Normal mode” to “Offroad mode”, thus improve the ride comfort and reduce the probability of suspension reaches the mechanical end stop, while for highspeed driving condition, lowering the vehicle body from “Normal mode” to “Highway mode” can not only reduce the air drag, but also improve the driving stability [15]. Meanwhile, after choosing the suitable vehicle height mode, the stable adjustment will be used to maintain the vehicle height to prevent deviation between the actual vehicle height and the target vehicle height, e.g. when the vehicle load is largely changed.
Fig. 2. Vehicle height adjustment strategy
According to the vehicle height adjustment principle, the influence mechanism on air spring stiffness of the two vehicle adjustment functions is different. The general adjustment can be regarded as influencing the air spring stiffness by regulating the air spring length on the premise that the absolute pressure inside air spring is unchanged. However, for the stable adjustment, it can impact the air spring stiffness through changing the absolute pressure inside air spring while the air spring length remains unchanged.
3. Dynamic modeling of EAS system
The EAS system model established for damping control is a fullcar model. The system consists of the vehicle body, four air suspensions, four unsprung masses and four tires as shown in Fig. 3. Assumptions of the modeling are as follows: the air spring is modeled as a linear spring with its stiffness timevarying; the adjustable damping shock absorber is equivalent to a nominal passive damping and a controllable damping force; the tire, which is always in contact with the road surface, is simplified as a linear spring with its damping effect neglected.
Equations of motion for fullcar model are given by the following [16]:
$\left\{\begin{array}{l}{F}_{fl}={k}_{fl}\left(t\right)\left({z}_{fl}{z}_{fl1}\right)+{c}_{fl}\left({\dot{z}}_{fl}{\dot{z}}_{fl1}\right)+{f}_{fl},\\ {F}_{fr}={k}_{fr}\left(t\right)\left({z}_{fr}{z}_{fr1}\right)+{c}_{fr}\left({\dot{z}}_{fr}{\dot{z}}_{fr1}\right)+{f}_{fr},\\ {F}_{rl}={k}_{rl}\left(t\right)\left({z}_{rl}{z}_{rl1}\right)+{c}_{rl}\left({\dot{z}}_{rl}{\dot{z}}_{rl1}\right)+{f}_{rl},\\ {F}_{rr}={k}_{rr}\left(t\right)\left({z}_{rr}{z}_{rr1}\right)+{c}_{rr}\left({\dot{z}}_{rr}{\dot{z}}_{rr1}\right)+{f}_{rr},\\ {z}_{fl1}={z}_{s}a\phi {d}_{1}\theta ,\\ {z}_{fr1}={z}_{s}a\phi +{d}_{1}\theta ,\\ {z}_{rl1}={z}_{s}+b\phi {d}_{1}\theta ,\\ {z}_{rr1}={z}_{s}+b\phi +{d}_{1}\theta ,\end{array}\right.$
where ${m}_{s}$ is the vehicle body mass, ${m}_{fl}$, ${m}_{fr}$, ${m}_{rl}$ and ${m}_{rr}$ are the unsprung masses at four corners of the vehicle, ${I}_{x}$ and ${I}_{y}$ are the roll inertia and pitch inertia of the vehicle body respectively, ${z}_{s}$ is the displacement of the vehicle body, $\theta $ and $\phi $ are the roll angle and pitch angle of the vehicle body respectively, ${z}_{fl}$, ${z}_{fr}$, ${z}_{rl}$ and ${z}_{rr}$ are the displacement of the unsprung masses, ${F}_{fl}$, ${F}_{fr}$, ${F}_{rl}$ and ${F}_{rr}$ are the four air suspension forces, ${d}_{1}$ is half of the distance between the left wheel and the right wheel $d$, $a$ and $b$ are the distance of front shaft and rear shaft to vehicle center of gravity (C.G.), ${k}_{t}$ is the stiffness of tire, ${z}_{fl0}$, ${z}_{fr0}$, ${z}_{rl0}$ and ${z}_{rr0}$ are the road displacement inputs at four tires, ${k}_{fl}\left(t\right)$, ${k}_{fr}\left(t\right)$, ${k}_{rl}\left(t\right)$ and ${k}_{rr}\left(t\right)$ are the equivalent timevarying stiffness of four air springs, ${z}_{fl1}$, ${z}_{fr1}$, ${z}_{rl1}$ and ${z}_{rr1}$ are the displacement of the four corners of vehicle body, ${c}_{fl}$, ${c}_{fr}$, ${c}_{rl}$ and ${c}_{rr}$ are the damping of the four nominal dampers, ${f}_{fl}$, ${f}_{r}$, ${f}_{rl}$ and ${f}_{rr}$ are the four controllable damping forces, ${d}_{1}$ is a half of $d$.
Fig. 3. Fullcar model of EAS
After choosing the state variables and the output variables as:
Eq. (7) can be written in the following statespace form as:
where $\mathbf{u}$ represents the external controllable damping force of the EAS system, $\mathbf{w}$ represents the disturbance caused by road roughness. $\mathbf{A}$, $\mathbf{B}$, $\mathbf{C}$, $\mathbf{D}$, $\mathbf{F}$ and $\mathbf{G}$ are the relevant constant matrices, which are omitted here for lack of space.
By taking Laplace transformation for Eq. (9), we obtain:
Then, the mapping relationship between the system output and the system input is given as:
Substituting the constant matrices into Eq. (11) and conducting discretization by using Euler method, we can get the system discrete time model with timevarying parameters as:
where:
$m$, $n$ and $p$ are the model order, which are set according to the simulation precision requirement, ${q}^{1}$ is the backward shift operator, [${a}_{1i}\left(t\right)$,…, ${a}_{mi}\left(t\right)$; ${b}_{0i}\left(t\right)$,…, ${b}_{ni}\left(t\right)$; ${c}_{0i}\left(t\right)$,…, ${c}_{pi}\left(t\right)$] are the parameters of the timevarying model, that are directly related to the parameters of EAS system.
4. Multimodel adaptive switching controller design
If the difference between the initial values of identifier in adaptive controller and the actual values of model parameters is too large, the convergence of identification process will need a long time, thus has an adverse impact on the control performance. Therefore, for control objects like EAS, whose parameters are easy to vary suddenly due to the change of driving conditions, the conventional adaptive controller can’t achieve a satisfying performance. This phenomenon and the better performace requirements above inspire to consider establishing multiple models according to the actual damping control process of EAS and choosing the proximate values of model parameters as the initial values of identifier, so the identification parameters can converge to the actual values quickly and the optimal control performance of adaptive controller can be guaranteed.
4.1. Establishment of the multiple models
From the analysis in Section 2, because of the influence of vehicle height adjustment, the air spring stiffness shows obvious timevarying and sudden change characteristics. Hence, to build the fixed model set for damping adaptive control, nine fixed fullcar models of EAS system are considered to be established based on the variable stiffness of air spring, which are computed on the basis of the typical vehicle height modes and different load conditions in each height mode.
Combining with the following simulation in the next section, a EAS system which is used for limousine is chosen as the study object. The equivalent lengths of the four air springs in three vehicle height modes are 150 mm, 175 mm and 205 mm respectively, the crosssectional area of the air spring is 0.018 m^{2}, the normal load of the front wheel is 630 kg, the normal load of the rear wheel is 510 kg and the polytrophic index is 1.3. Substituting these parameters into Eq. (6), we can obtain the air spring stiffnesses for different vehicle height modes and different load conditions. In each height mode, three load conditions are set, i.e. the overload condition (120 % × normal load), the normal load and the under load condition (80 % × normal load). The variable stiffnesses of the air spring in different conditions are shown in Table 1.
Table 1. The air spring variable stiffnesses
Load condition

Height mode

Air spring stiffness (N/m)


${k}_{fl}$

${k}_{fr}$

${k}_{rl}$

${k}_{rr}$


Overload condition

Highway mode

65520

65520

53040

53040

Normal mode

56160

56160

45463

45463


Offroad mode

47941

47941

38810

38810


Normal load

Highway mode

54600

54600

44200

44200

Normal mode

46800

46800

37886

37886


Offroad mode

39951

39951

32341

32341


Underload condition

Highway mode

43680

43680

35360

35360

Normal mode

37440

37440

30309

30309


Offroad mode

31961

31961

25873

25873

The establishment of the fixed models can ensure the transient response speed of control, meanwhile, a adaptive model whose parameters’ initial values can be reassigned is introduced to improve the control precision. The specific identification process of model parameters is described as follows:
Rewrite Eq. (12) as:
$+\cdot \cdot \cdot +{b}_{ni}\left(t\right)\mathbf{u}\left(tn1\right)+{c}_{0i}\left(t\right)\mathbf{w}\left(t1\right)+\cdot \cdot \cdot +{c}_{pi}\left(t\right)\mathbf{w}\left(tp1\right),$
Conduct parameters identification of Eq. (15) by using improved projection algorithm [17]:
where $\widehat{\mathbf{\theta}}\left(t\right)$ represents the identification parameters and:
4.2. Model switching strategy
In order to choose the proximate model for the actual running states of EAS, the actual system outputs are considered to be compared with the outputs of the established multiple models, and a model switching performance index is proposed as follows:
where $\alpha $ is the weighted factor, $t$ is the computational time domain, and:
Because of the outputs of EAS have a big difference in the order of magnitude, so that they can’t be superposed as the performance index directly and the quantization processing in same scale must be conducted. The root mean square values of outputs in computational time domain is counted firstly, and then the same scaling coefficients are determined as:
where ${\beta}_{13}$ is the coefficients of vehicle body accelerations, ${\gamma}_{1}$ is the coefficient of suspension deflection and ${\eta}_{1}$ is the coefficient of dynamic wheel load.
After that, the model switching performance index can be rewritten as:
where $\stackrel{}{y}\left(k\right)$ is the system outputs which have been quantitated in same scale. Thus, at each sampling time, the proximate model parameters can be got based on the performance index (Eq. 22) as follows:
and the model parameters are then assigned to the adaptive model, i.e. $\widehat{\mathbf{\theta}}\left(t\right)={\mathbf{\theta}}_{j\left(t\right)}$.
4.3. Design of the adaptive controller
After getting the proximate model parameters, a adaptive controller is designed to achieve the optimal regulation of damping force and the following aspects are taken into consideration:
Define the auxiliary outputs for Eq. (12):
where $\mathbf{P}$, $\mathbf{Q}$ and $\mathbf{R}$ satisfy the following relationship:
where $\mathrm{\Delta}=1{q}^{1}$.
Now assume that there are two positive integers $L$, $M$ are given, and $L\le M$. If function $\sigma (\cdot )$ is continuous and satisfy:
then the function $\sigma (\cdot )$ is defined as the linear saturation function of $L$ and $M$. Therefore, the nonlinear adaptive controller can be described based on the defined auxiliary outputs and the linear saturation function as:
where ${\sigma}_{i}$ is the linear saturation function for positive integers (${L}_{i}$, ${M}_{i}$) ($i=$ 0, 1,…, $k$1), and the ${L}_{i}$, ${M}_{i}$ are defined as:
The goal we wish to achieve with the multimodel adaptive switching controller is to track the reference system outputs by controlling the adjustable damping force.
4.4. Stability of the controller
To set up the multimodel adaptive switching controller, some assumptions about the model of electronic air suspension system as Eq. (12) should be given first:
A1) The order of the model, i.e. $m$, $n$ is known a priori;
A2) The vector of parameters [${a}_{1i}\left(t\right)$,…, ${a}_{mi}\left(t\right)$; ${b}_{0i}\left(t\right)$,…, ${b}_{ni}\left(t\right)$; ${c}_{0i}\left(t\right)$,…, ${c}_{pi}\left(t\right)$] is piecewise constant (linear time variant, i.e. LTV system with jumping parameters) of sample time $t$ and changes in a compact set, in which ${A}_{1}(t,{q}^{1})$ is guaranteed stable, i.e. ${A}_{1}\left(t,{q}^{1}\right)\ne $0.
Theorem 1. When multimodel adaptive switching control strategy is applied to LTV system with jumping parameters, the switching of model will stop at an adaptive model after finite sample time and the closedloop system is boundedinput boundedoutput (BIBO) stable, the output of system will track setpoint value $r\left(t\right)$ asymptotically.
Before the proof of Theorem 1, a lemma, which has been proved in [18], is given first.
Lemma 1. Parameter identification algorithm as Eq. (17) have the following properties to LTV system with jumping parameters:
1) $\Vert \widehat{\mathbf{\theta}}\left(t\right)\mathbf{\theta}\Vert \le \Vert \widehat{\mathbf{\theta}}(t1)\mathbf{\theta}\Vert \le \Vert \widehat{\mathbf{\theta}}\left(0\right)\mathbf{\theta}\Vert $, $t\ge $ 1;
2) $\underset{t\to \mathrm{\infty}}{\mathrm{l}\mathrm{i}\mathrm{m}}\Vert \widehat{\mathbf{\theta}}\left(t\right)\widehat{\mathbf{\theta}}(tk)\Vert =0$, for any finite integer $k$;
3) $\underset{t\to \mathrm{\infty}}{\mathrm{l}\mathrm{i}\mathrm{m}}\frac{e\left(t\right)}{[1+\mathbf{\varphi}(t1{)}^{T}\mathbf{\varphi}(t1){]}^{1/2}}=0$, where $e\left(t\right)=\stackrel{}{\mathbf{y}}\left(t\right)\widehat{\mathbf{\theta}}(t1{)}^{T}\mathbf{\varphi}(t1)$.
The proof of Theorem 1. When multimodel adaptive control is applied to LTV system with jumping parameters, we can find that the control system is input bounded from Eq. (27), from A2), it can be seen that the system output $y\left(t\right)$ is bounded. According to Eq. (14), we know that $\varphi (t1)$ is bounded. From 3) of Lemma 1 and Lemma 6.2 of [18], the following conclusions can be got as:
From Eq. (22) and Eq. (23), we know that the model switching will stop at an adaptive model, and:
The parameter will converge finally according to Eq. (23) and 1), 2) of Lemma 1, from Theorem 2.3 of [19], adaptive controller will make the output of system:
track $r\left(t\right)$asymptotically. Thus the multimodel adaptive control can make the output of LTV system with jumping parameters track $r\left(t\right)$ asymptotically.
Remark 1. From Theorem 1, we can see that the multimodel adaptive controller can keep the system stability and convergent property of conventional adaptive control algorithm when it is applied to LTI (linear time invariant) system or LTV system with jumping parameters. At the same time, because of the existence of multiple models, the convergent identification speed of system parameters is speed up and the transient response is improved effectively. An LTV system with jumping parameters can be considered as an LTI system in the interval of the two adjacent parameter jumping. According to Theorem 1, when the interval is sufficiently long, the closedloop system is also BIBO stable. Therefore, the multimodel adaptive controller can also improve the control performance of LTV discrete time system with jumping parameters.
5. Simulation results and discussion
To analyze the performance of the multimodel adaptive switching controller described in this paper, we will compare it against the original adaptive controller. Two typical simulation conditions, which fit the actual vehicle driving states, are designed as Table 2 to verify the effectiveness of the proposed control algorithm.
Reviewing the information of the two simulation conditions, we observe that the condition A is designed for the vehicle height general adjustment, while the condition B is designed for the vehicle height stable adjustment. These two conditions can verify the identification ability of the control algorithm when sudden change of the EAS system parameters happened. For our simulation, the other fixed model parameters are selected as Table 3.
Table 2. Typical simulation conditions
Simulation condition

Simulation time/s

Road classification

Vehicle speed/(km.h^{1})

Vehicle height mode

Load condition

Condition A

05

C

60

Normal

Normal

510

B

100

Highway

Normal


1015

E

20

Offroad

Normal


Condition B

05

C

60

Normal

Normal

510

C

60

Normal

Overload


1015

C

60

Normal

Underload

Table 3. System parameters
Two wheels distance in meters

1.63

Tire stiffness in kN/m

192

C.G. to front axle distance in meters

1.45

Front suspension base damping in N·s/m

1460

C.G. to rear axle distance in meters

1.69

Rear suspension base damping in N·s/m

1570

Front wheel unsprung mass in kilograms

120

Roll moment of inertia in kg·m^{2}

679

Rear wheel unsprung mass in kilograms

90

Pitch moment of inertia in kg·m2

2304

The simulation is run utilizing the road profile of a random base excitation with different road class roughness. The vehicle is driven with a constant speed and the time histories data of road irregularity are described by power spectral density (PSD) method [20]. The simulation results of the two conditions are charted in Fig. 4, Fig. 5 and Table 4. We can easily identify that the performance difference between the original adaptive controller (OAC) and the multimodel adaptive switching controller (MASC) is tiny during 05 s, only slight difference due to the randomness of linear saturation function exist. This can be explained by the fact that the initial values of the air spring stiffness is set as same as the actual values for simulation, thus the model parameters of the both two control methods can converge quickly. At 5 s and 10 s, which are the sudden change time of vehicle driving condition, we can see that the MASC is superior in performance over the OAC in the initial stage, that’s due to the OAC can’t converge the model parameters quickly, while the MASC can switch to the proximate model according to the switching index, thus achieve the better control performance.
Fig. 4. Simulation results of condition A
a) Vehicle body vertical acceleration
b) Vehicle body roll acceleration
c) Vehicle body pitch acceleration
Table 4. RMS acceleration comparison and reduction (RED)
Condition

Time/s

Vertical acceleration (m/s^{2})

Roll acceleration (rad/s^{2})

Pitch acceleration (rad/s2)


OAC

MASC

RED

OAC

MASC

RED

OAC

MASC

RED


Condition A

35

1.29

1.31

–1.5 %

0.82

0.79

2.4 %

0.85

0.83

2.3 %

57

0.72

0.63

12.5 %

0.57

0.46

19.3 %

0.52

0.41

21.1 %


710

0.64

0.65

–1.6 %

0.48

0.49

–2.1 %

0.43

0.44

–2.3 %


1012

2.24

1.96

13.1 %

1.59

1.41

10.7 %

1.87

1.69

9.6 %


1215

1.92

1.91

0.52 %

1.45

1.43

1.3 %

1.71

1.68

1.7 %


Condition B

35

1.32

1.33

0.75 %

0.92

0.94

–2.2 %

0.84

0.85

–1.2 %

57

1.47

1.32

10.2 %

1.07

0.92

13.8 %

0.79

0.68

13.9 %


710

1.35

1.34

0.74 %

0.94

0.95

–1.1 %

0.72

0.70

2.8 %


1012

1.27

1.14

10.2 %

0.86

0.76

11.8 %

0.75

0.63

16 %


1215

1.13

1.14

0.88 %

0.78

0.76

2.5 %

0.62

0.60

3.2 %

Fig. 5. Simulation results of condition B
a) Vehicle body vertical acceleration
b) Vehicle body roll acceleration
c) Vehicle body pitch acceleration
6. Conclusions
In this paper it has been discussed about the designing of a novel damping force controller based on multimodel adaptive switching control strategy for EAS system. The multiple system models with fixed parameters and an adaptive model whose initial value of parameters can be reassigned are established based on the fullcar dynamics. Performance simulation is conducted by Matlab/Simulink and the performance comparisons between the original adaptive controller and the multimodel adaptive switching controller are done. The results prove that the EAS system with multimodel adaptive damping controller can improve the ride comfort when sudden change of driving conditions happened, which show the effectiveness of the multimodel adaptive switching control framework for solving the damping control problem of EAS system
Acknowledgements
Research work has been partly supported by National Natural Science Foundation of China (Grant No. 51375212), A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20133227130001), and China Postdoctoral Science Foundation (Grant No. 2014M551518).
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