1 Structure and test method of vehicle inertia loading device

In a variety of automotive tests, inertial dynamic loading is required. Take automotive synchronizer testing as an example. The dynamic load on the output shaft of the transmission must be achieved. 1 is the structural diagram of the vehicle synchronizer testing device. It uses a DC motor, rotating shaft Connect the transmission output shaft through the coupling. The speed sensor and torque sensor are used to measure the speed and the torque transmitted on the shaft. The shift process is completed by a robot controlled by the computer. To detect this, the position of the robot and the shift force are detected by the robot. The control computer issues shift instructions. Signals related to the gearbox are connected to the system master computer through signal conditioning and conversion modules and interface modules, and information is exchanged between the two computers.

Inertia loading is mainly achieved by a DC motor speed control system.

The suppression of the disturbance caused by the large mechanical inertia formed by its own mass during the actual shifting of the vehicle makes the vehicle speed hardly change due to the synchronous torque appearing in the synchronizer. The synchronization time of the shift is very short, generally 0 31 2s, and the performance of the synchronizer is evaluated based on the characteristic parameter data during the synchronization. Since the motor inertia is small, if the dynamic response speed of the speed control system is slow, it may easily cause the motor speed to fluctuate violently within a short time, and the measured characteristic parameter data of the synchronizer is distorted.

2 dynamically loaded models

2.1 Description of the state equation of the loading model

In order to study the inertial loading method with high dynamic response, the loading part is studied separately. The input torque on the shaft is represented by T s, the moment of inertia is expressed by J s, the angular velocity is represented by s, and the control structure model is shown in FIG. 2. Where Jm is the motor inertial moment; Tp is the transmission torque on the shaft; Tm is the motor output torque; m is the motor angular speed; Damping coefficient. The equation of state of the loading section can be expressed as follows:

X= AX + BU( 1)

Y = CX( 2)

A = 0 - J - 1 s J - 1 s 0 J - 1 m K - K - DJ - 1 s D (J - 1 s - J - 1 m) 0 B = TC = T where the state vector X = T , Y= ,U= .

Although J s, J m, K, and D are constants, other than J m, the values ​​of other constants and state variables are difficult to determine or measure. A variety of methods can be used to solve the problem, such as designing state observers, using state observation methods, and parameter identification methods. However, these methods all require a large number of calculations. The accuracy of the algorithm affects the accuracy of the control. Second, the time required for a large number of calculations can hardly guarantee the real-time control.

2. 2 Model Reference Control System Structure

Observing the dynamic loading model, it is not difficult to find that, compared with the actual car movement, in terms of structure, the vehicle's movement inertia is in the position of the motor's moment of inertia, and less input of the motor torque; in terms of quantity, the car's movement inertia is larger than the motor. Moment of inertia. Starting from the similarity of this structure, using the model reference control method can simplify the design of the loading system. The structure of the loading system is as shown.

Among them, T

m represents the torque command;

Indicates the flux linkage command; s indicates the stator flux linkage; s indicates the stator flux linkage angle; I s indicates the stator current; U s indicates the stator voltage; Te indicates the electromagnetic torque; P indicates the differential operator.

The model reference control not only applies to the design of the traditional linear steady-state system, but also obtains satisfactory performance in system design including nonlinear and time-varying parameters. The system model does not require an actual hardware device and can be a mathematical model generated in the computer. In 3, the induction motor is driven using a direct torque control method in order to obtain a fast torque control response. The output torque of the motor can be either resistance torque or acceleration torque. The key lies in the magnitude of the inertia and the movement state. In general, the inertia of the vehicle is greater than the mechanical inertia of the test system. During the acceleration process, the torque Te is a resistance torque, which delays the acceleration process. The system performs as the starting process of the large inertia system. During deceleration, the output torque of the motor becomes the acceleration torque, which slows down the deceleration process. When the vehicle inertia is less than the inertia of the test system, if it is in the acceleration process, the motor output torque becomes the acceleration torque, so as to meet the requirements of the dynamic load of the vehicle inertia. The direct torque control torque command is output by the controller.

3 Expert PID-based controller design

3.1 Analysis of Motion Control System

The equation of motion of the test system is T - TL = J dd t where T is the drive torque; J is the rotational inertia of the test system; TL is the load torque of the test system; it is the angular velocity of the test system.

When the test system drive torque T and the moment of inertia J are both constant, the system acceleration can be controlled by artificially controlling the load torque TL, that is, adding an additional load torque. The additional load torque increases, the system acceleration decreases, the additional load torque decreases, and the acceleration increases. Since the load torque of the test system is output by the motor, the core of dynamic loading is to require the electric drive system to precisely control the motor output torque. The literature describes that the use of vector control variable frequency speed control system can be a good improvement in the dynamic characteristics of asynchronous motors. In the asynchronous motor, the torque and the magnetic flux are coupled, and the vector control decouples the two. Therefore, the effect is better than the general frequency control, but the decoupling control makes the control system structure complex. This paper adopts direct torque control method, which does not require decoupling, and uses a nonlinear controller to form a closed-loop control of flux linkage and torque, shortening the system response time, and further improving the dynamic performance of the system. The mathematical model of the flux linkage and torque of the asynchronous motor in the -coordinate system is as follows:

S =

( u S - i SRS)dt( 3)

S =

( u S - i SRS)dt( 4)

T d = 3 2 p(S i S - S i S)( 5)

In the formula, S and S are the motor stator shaft and shaft flux chain respectively; u S and u S are the stator shaft and shaft voltage respectively; i S and i S are the stator shaft and shaft current respectively; T d is the electromagnetic torque; Is the pole pair number; RS is the motor stator winding resistance.

The direct torque control adopts the space voltage vector (SVM) modulation method, and the actual electromagnetic torque is controlled by selecting an appropriate voltage vector to control the angle between the stator flux linkage and the rotor flux linkage. The torque control model is T d = 1 L

3 |S | | r | In sin, L is the leakage inductance; S and r are the stator flux and the rotor flux, respectively; it is the angle between the stator flux and the rotor flux.

In the loading model, the vehicle inertia model is a computer-generated mathematical model that produces a desired output speed for a given input torque. The actual driving torque is applied to the shaft of the motor to make the motor generate motion. The output of the model is compared with the output of the object and the difference is used to generate the control signal.

3. 2 control strategy

The traditional PID parameter setting is based on the characteristics of the object. When the object characteristic is fixed, the PID parameter is selected accurately, and the ideal control effect can be obtained. However, the characteristics of the actual system may change dynamically. For example, when the vehicle is loaded, the loading of different conditions and different quality of the vehicle is carried out, and the characteristics of the object will have a large change, making the adaptability of the PID parameter worse. Expert PID design controller can solve this problem very well. Let the error at the current sampling time be e(k). The errors of the previous and the first two sampling moments are e(k-1) and e(k-2), which are also set as follows:

%e(k) = e( k) - e( k - 1)( 6)

%e( k - 1) = e( k - 1) - e(k - 2)( 7)

Then the control method is as follows: (1) At time 1 in 4 there is e(k) > 0, %e(k) (2) at time 2 in 4, there is e(k) 0, indicating that the error is in The change in the direction of the increase in the absolute value of the error must be appropriately controlled as soon as possible to reverse its change. If the absolute value of e(k) is still small, take a weaker control, with u(k) = u( k - 1) + kp( e(k) - e(k - 1)) + kie( k) + kd( e( k) - 2e(k) + e(k - 2)) If the absolute value of e(k) is larger, take stronger control, with u(k) = u( k - 1) + k 1 k 1 > 1 Where kp, ki, and kd are the proportional, integral, and differential coefficients, respectively.

(3) At the middle time 3, e(k) 0,e(k) %e(k) Conventional PID control, when the process is started or stopped or the setting is greatly increased or decreased, the system output is generated in a short time. Large deviations result in the accumulation of integrals in the PID control, causing large overshoot of the output and even oscillations. Therefore, in the expert PID, the integral separation is introduced. When the deviation is large, the integral action is cancelled. When the controlled variable approaches a given value, the integral control is introduced to eliminate the static error. Therefore, when e(k) is smaller than the one set in advance For small positive numbers, the controller output is u(k) = k 1 e(k) + k 2 a (k) where a(k) is the discrete integral of the error.

4 Simulation Research

4.1 Dynamic Loading System Operation Simulation

Based on the structure, a simulation model was established. The motor used for dynamic loading adopts direct torque control technology, and the output of expert PID controller is a torque instruction. Such a system design is to obtain fast response. The relevant parameters of the experiment were set as follows: target inertia 0 35kg m2, motor inertia moment 0. 089kg m2, power supply voltage 380V, power supply frequency 50Hz, motor power 22kW. The experimental results are shown.

It can be clearly seen from Fig. 6 that although the acceleration slope of the speed reference function is 1800r/(min s), the actual running speed rises at a more gradual rate, which reflects the real process of accelerating the large inertia system. It can be seen that, in the acceleration process, the output torque of the motor is a negative value, that is, contrary to the reference direction, the positive direction of the reference direction is the direction in which the motor actually rotates, and the output torque of the motor is the dynamic loading torque.

7 shows the speed error curve during dynamic operation. The visible error is very small. After 0 275s, the speed rises to the rated speed, and the subsequent speed error is less than 1r/min, which is less than 1%.

8 is the dynamic compensation torque map, from which we can see that inertia loading and other forms of loading are the biggest difference: first, the magnitude of the loading torque is not a constant value, but changes dynamically, and its value is obtained by the torque compensation algorithm; In steady-state steady-state operation, the loading torque is very small, almost zero, which is the inherent pulsating torque of the system. The reason is that the nature of the inertia load is proportional to the acceleration.

4.2 Comparative Simulation

In order to facilitate the study of the control effect of the controller designed by the expert PID, two comparison methods are used. The first is the static loading process of the pure mechanical inertia. At present, the traditional loading method adopts the inertia flywheel set. Due to the disadvantages of this method, The use of mechanical inertial electrical simulation becomes an advanced loading mode. The comparison between the experimental results of the two methods is to measure the availability of the latter. Second, the same control model is used to verify the loading conditions of different automobile models and to verify the adaptability of the control model. The experimentally relevant parameters are shown in Table 1, and the experimental results are shown in 9, 10 respectively.

Comparative study Simulation experiment parameter setting comparison study 1 mechanical moment of inertia (kg m 2) 0. 35 starting speed (r/min) 0 target speed (r/ min) 400 comparison study 2 dynamic loading inertia (kg m 2) 0. 2 mechanical moment of inertia (kg m 2) 0.2 starting speed (r/min) 0 target speed (r/ min) 400

In 9, the main parameters of the loading experiment rise time is 0 37s, and the rise time of the electrical inertia loading is also 0 37s, the two curves are basically the same shape, the error is very small, indicating that the control model is available and the accuracy of the controller is very good .

In the experiment shown in 0, compared with the parameter settings, the 0 experiment only modified the inertia size. Comparing the experimental results of 0a and 0b, the rise time is 0 276s, indicating that the control model has good adaptability.

5 ends

In the dynamic load experiment of the car, this article adopts the electronically controlled loading mode and uses the asynchronous motor to generate the required load torque.

In order to obtain good dynamic performance, direct torque control technology is used for loading. In the realization of the controller, the expert PID technology with integral separation is used to control the experimental error in a very small range. The availability and adaptability of the designed control model are verified through two sets of comparative tests, and the high-performance vehicle dynamics are achieved. load.

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