For the decoupling method of the chilled water control model, the traditional decoupling method mainly has the modern frequency method and the feed forward compensation method. The modern frequency method also includes time domain method. The pre compensation method includes the invariance of the contact, the matrix inversion and the inverse decoupling. In this paper, the decoupling network is designed by using the invariance principle of feed forward decoupling compensation method, as shown in Figure 6:

Order:

y 11 − y 12 = 0 ( m 2 ≠ 0 )

y 21 − y 22 = 0 ( m 1 ≠ 0 )

So:

G 12 ( s ) − D 12 ( s ) G 11 ( s ) = 0

G 21 ( s ) − D 21 ( s ) G 22 ( s ) = 0

So the mathematical model of the decoupling network:

D 12 ( s ) = − G 12 ( s ) G 11 ( s ) (2)

D 21 ( s ) = − G 21 ( s ) G 22 ( s ) (3)

Put expressions (1) input (2), (3):

D 12 ( s ) = 9.12 s − 8.03 s + 9.59 e − 4.3 s

D 21 ( s ) = 20.85 s − 94.59 s − 0.89 e − 3.3 s

Figure 6 build the “2 input −2 output” decoupling network, although solved the problem of coupling between systems, but in order to ensure that the system output value satisfies the set value requirements, also need to set up the network controller based on feed forward compensation decoupling in Figure 6. In the control system, the PID controller is the most commonly used controller. Its principle block diagram is shown in Figure 7.

PID controller is based on the control deviation which on value Rin ( t ) and the Yout ( t ) :

Error ( t ) = Rin ( t ) − Yout (t)

u ( t ) = k p ( e r r o r ( t ) + 1 k i ∫ 0 t e r r o r ( t ) d t + k d d e r r o r ( t ) d t )

G ( s ) = U ( s ) E ( s ) = k p ( 1 + 1 k i s + k d s )

where:

K_p: ratio coefficient;

K_i: integral time constant;

K_d: differential time constant.

In the actual project cases, k p k i k d control parameter values have great influence on the output of the system, because of the central air conditioning system of “multi input and multi output, strong coupling between the system

and other factors, resulting in the actual debugging of central air-conditioning system, control tuning parameters, k p k i k d has become very difficult. Therefore, the control loop of the water system of the central air conditioning system, the choice of the corresponding PID parameters becomes very important. If the parameter selection is not properly, it will not only lead to oscillation of the control loop, and even cause the instability of the control loop of the whole air conditioning system. The selection of control parameters k p k i k d in the actual air conditioning control case is also the difficulty of the project. In this paper, a new method of PID parameter k p k i k d is proposed, which uses the Elman neural network to adjust the parameters of the PID controller k p k i k d . As shown in Figure 8.

Figure 9 for the Elman neural network model structure, from Figure 9, we can see that the model design of the algorithm for the 3 input, 3 output, the middle layer for the 5 layer structure.

1) Initialization model: w j l w i j w k j θ l θ j ;

2) Input training values: x 1 , x 2 ⋅ ⋅ ⋅ x p the output value of the model: y 1 , y 2 ⋅ ⋅ ⋅ y p ;

3) estimate: | t p − y p | < ε ;

4) If the step (3) is satisfied, the weight and the threshold value of the model are corresponding to the model, or the following steps:

5) The following values are calculated

E p l = 1 2 ∑ l = 0 m − l ( t l p l − y l p l ) 2 ;

E a l l = 1 2 ∑ p l = 1 p ∑ l = 0 m − l ( t l p l − y l p l ) 2 ;

w j l ( n + 1 ) = w j l ( n ) − η ∂ E a l l ∂ w j l ;

θ l ( n + 1 ) = θ l ( n ) − η ∂ E a l l ∂ θ l ;

w i j ( n + 1 ) = w i j ( n ) − η ∂ E a l l ∂ w i j ;

w k j ( n + 1 ) = w k j ( n ) − η ∂ E a l l ∂ w k j ;

θ j ( n + 1 ) = θ j ( n ) − η ∂ E a l l ∂ θ j ;

6) calculated the model again: w j l w i j w k j θ l θ j

7) Return step (2)

If the step (3) satisfies the condition and stops the training (step 5), the measured value is input to the model, and the predicted value is obtained.

According to the analysis of simulation program controller of central air conditioning water system, this paper uses MATLAB software as a simulation tool for research, program can be realized by MATLAB in Simulink, can be directly through the MATLAB program simulation directly.

As shown in Figure 10, for the Elman neural network PID control algorithm simulation diagram, procedures in accordance with the Elman neural network

PID control structure based on given the formula (1), (12), (13) the corresponding coefficients corresponding simulation chart module. And the preparation of Elman PID1 and Elman PID2 corresponding to the Elman neural network PIDcontrol operation of the m file.

Figure 11 shows the Elman neural network PID pressure control corresponding to the input-output simulation results, it can be seen from Figure 11, the Elman neural network PID control based on differential pressure, can be very good to achieve the central air conditioning water system pressure difference value of precision control, but the overshoot of Elman neural network PID differential pressure control based on the comparison, the output of the system in a short time quickly from the pressure difference of 0 m water column, up to 17.137 m water column, so the central air conditioning water system is bad, need to give enough attention in the subsequent engineering verification, but also need to see that the Elman neural network PID differential controllers based on the output of the system rise time is very short, the rise time is only 7S, the adjusting time is only 16S, and can be quickly to ensure the system output stable.

Figure 12 shows the Elman PID neural network temperature control corresponding to the input-output simulation results, it can be seen from Figure 12, the PID temperature control based on Elman neural network, the output of the system can not only quickly change follow the preset temperature value changes, and the system is stable, small overshoot. Thus, based on the Elman neural network PID temperature control, can achieve very good operating results.

In practical projects, for the regulation of central air conditioning water system operation, but also to adjust the temperature and pressure regulation by using the temperature difference, therefore, Elman neural network PID differential

pressure control, can realize the central air conditioning water system operation good regulation.