3.1 Optimization method
In the chapter 2, ISO 2631-1(1997) used method (RMS) is applied in
this paper and define the cost function. About the optimization algorithm, Genetic Algorithm (GA) is applied to the optimization of the suspension parameters in there [1]. GA is categorized as global search heuristics, and use techniques inspired by evolutionary biology such us inheritance, selection, crossover, and mutation [1]. Figure 3.1 shows the GA-based algorithm.Then the selected parents and offspring construct the new population used in the next iteration of algorithm. The scheme of the procedure of GA is shown in Figure 3.2. [1] [9].
The genetic algorithm program contain following parts: initial population, cost evaluation, mate selection, crossover and mutation [1] [17].
In this paper, the programming of GA is written using MATLAB and combined with the ATV model build with simulink [1].
In this paper is using pre-obtained global optimization method of the Genetic Algorithm, calculated and simulated optimization of passive suspension parameters for ATV.
3.2 Experiment structure
The framework block diagram is shown in Figure 3.3. The block
diagram illustrated this research structure. These block diagrams have tworesult have already known (blue real line). The experiment section (red dotted line) is this research thesis scope. We have optimization result of passive suspension parameters (spring coefficient k and damping
coefficient c) for ATV. Due to the experiment, we get experiment data and compare before optimization result, and finding out the parameters of the ATV suspension optimize value.
Figure 3.4 are the experiment structure block diagram target and details. The experiment section (red dotted line), and the experiment section block diagrams have five items:
1. Road profile data.
2. The IMON corp. 6-axis motion platform.
3. Measurement system.
4. System identification for experiment equipment.
5. Compare & prove optimization data. – Experiment target.
About the above items, we explained in the following chapter.
3.3 Road profile data
The road profile data is collected from an ATV rally game made by IMON corp. [1]. Figure 3.5 are two kind of road profiles data from A, B, C and D. The road profile data of the game contains kinds of different road conditions. For example: off-road profile including loess, timberland, highway, sand beach and jouncing areas. When driver or player driving ATV on these areas, the setting of suspension parameters k and c will affect the feeling of the whole body vibration transferred from the road [1]. In order to improve the comfort of driver for different road conditions, we use
the road profile data as inputs
Z JK
A,
Z JK
B,
Z JK
C,
Z JK
Dthen proceed the optimization procedure to find the optimized values of k and c[1]. In the chapter 4, we will explain for road profile data inputs.
3.4 Experiment equipment-Stewart platform
This experiment of research, we use Stewart platform to simulation and analysis ATV to travel on off-road using road profile data of the game.
The Stewart platform is also known as parallel platform. The Stewart platform diagram is shown in Figure 3.6. The figure illustrated a Basic Stewart platform conformation.
The Stewart platform can be called the parallel type robot, parallel type platform or parallel type mechanism. Theoretically, as a 6 degree of freedom parallel mechanism, our used 6-axis motion platform is also classified as "Stewart motion platform"[19], and suitable for the processing of high precision or 3D curved surface. The characteristics are high precision, high rigidity and smaller. It application is extremely extensive too. For example: fly simulated training, video game or toy, milling machine/ drilling machine/ vertical & horizontal type 3 axle machine center, strength and strength distance measure, tank driver training etc.
About experiment, we cooperate with IMON corp. (Internet Motion Navigator Corp.) and use 6-axis platform- Hexglider that IMON corp.
develop. Figure 3.7 is the IMON corp. 6-axis motion platform and figure 3.8 illustrated IMON corp. 6-axis motion platform / control unit / IPC.
Hexglider is the first 6-axis motion base developed by IMON. It adopts a PC-based controller for use in motion control of multiple axes [19]. Online
actual testing result proves strong robustness and high reliability of Hexglider [19]. However, this platform is quite unique in linkage design and the way of movement is different than traditional hydraulic/pneumatic Stewart platform [19].
The IMON corp. 6-axis motion platform details list in table 3.1 and the main characteristics are as follows: [19]
1. The new design of universal joint allows larger kinematics angle with higher strength.
2. One-piece linkage strengthens the ratio of strength/inertia.
3. Ball screw and linear guide way enhance transmission precision.
4. Larger travel distance and angle rotation facilitate motion simulation reality of various carriers.
5. Module design of mechanism components makes assembly and maintenance more convenient.
6. Components designed and manufactured in high precision make the platform more rigid, reliable and robust.
So that, we use IMON corp. 6-axis motion platform to simulation and dynamic analysis ATV to travel on off-road using road profile data of the game. Measure passive suspension parameters of ATV and compare optimization of passive suspension parameters.
3.5 Experiment data measure-measurement system
Figure 3.4 is the experiment structure block diagram details among. In the measurement system, we measure the IMON 6-axis motion platform using gyroscope. We hope to get some measure data, the ATV to travel on
off-road using road profile data of the game. About measurement system
detailed showing is as follows.
In the chapter 2, we talk about the yaw angle (
γ
) very small and can be neglected when driving alone a straight lane. Therefore, we want to measure roll (α
), pitch (β
) Angular speed, we choose the gyroscope and measure angular speed.In addition to we talk about
Z K
0is the origin point of local reference in the chapter 2. Then we can derive the vertical displacement of each suspension, and for simplicity, we applied linearization by assuming
α
andβ
were small. Therefore, we want to measure Z-axis acceleration.About experiment, we catch from the 6-axis motor encoder data and use numerical method for the forward kinematics to the IMON 6-axis motion platform movement posture. In the chapter 4, we will explain for forward kinematics.
3.6 System identification for IMON 6-axis motion platform
In chapter 2.2 and 3.4, we are derived of dynamic equations for ATV and use IMON 6-axis motion platform to simulation and analysis ATV to travel on off-road using road profile data of the game. But we must
measure posture of IMON 6-axis motion platform. What’s transfer function of the platform? So that, we defined the system identification procedure of IMON 6-axis motion platform have four steps:
1. Spectrum: we use the road profile data of ATV game, use Matlab FFT (Fast Fourier Transform) calculate spectrum. The road profile data
spectrum during 0~5Hz.
2. Input data: we use sweep sine signal and input to IPC. The sweep sine during 0~5Hz and 5~0Hz. Let the IMON 6-axis motion platform with sweep sine signal movements. The sweep sine and output data after forward kinematics to the IMON 6-axis motion platform movement posture is shown in Figure 3.9.
3. Output data: we get from the 6-axis motor encoder data and forward kinematics to the IMON 6-axis motion platform movements posture. In the chapter 4, we will explain for forward kinematics.
4. Estimates output: we use Matlab toolbox-system identification tool, n4sid algorithms calculation.
About the estimates output, we find out the roll, pitch and vertical axis transfer function (H platform(s)) of the IMON 6-axis motion platform.
For vertical vibration of the IMON 6-axis motion platform, the transfer functions are H platform-roll(s) and H platform-pitch(s). A second order shaped curve of the form
2 has been used to approximate the weighting curve (bode diagram)and shown in Figure 3.10 and 3.11.
For rotation vibration of the IMON 6-axis motion platform, the transfer function is H platform-z(s). A second order shaped curve of the form
2 has been used to approximate the weighting curve (bode diagram)and shown in Figure 3.12.
Finally, optimization of passive suspension parameters, simulation and measurement results ( ,
z α
,β
) multiplication new frequency weighting (Hplatform(s) *H human(s)) and shown in Figure 3.13. We can get be closer to the frequency of the human body and experiment platform experiences in fact.
3.7 Experiment target
The experiment items and target are shown in figure 3.3 and 3.4.
According to the experiment items and target diagram. First, we must installing gyroscope on the IMON corp. 6-axis motion platform. Follows above, we will input road profile data and suspension parameters of k and c to IPC. Third, measure and calculate velocity & acceleration. Fourth,
compare GA optimization data. Finally, prove optimization of passive suspension parameters (spring coefficient k and damping coefficient c).
Finding out the parameters of the ATV suspension optimize value.