There are several existing examples of using scaled vehicle as the testing facility for vehicle safety system design, e.g., Longoria, A1-Sharif, and Patil at Univ. of Texas at Austin construct a 1/5 scale vehicle testing system for ABS (Anti-lock Braking System) evaluations, and they emphasize on the difference in tire-ground contact effect between scaled vehicles full size vehicles [26]. Kachroo at the Virginia Institute of Technology applies their FLASH (Flexible Low-cost Automated Scaled Highway) system to the ITS (Intelligent Transportation System) investigations. The author employs several sensors on the scaled vehicle and successfully commands the scaled vehicle to follow prescribed trajectory [27]. Brennan and Alleyne, and their colleagues at the University of Illinois at Urbana-Champaign, develop the IRS (Illinois Roadway Simulator) system, and conclude the dynamic similitude between scaled vehicle and full size vehicle [28]. The scaled vehicle is controlled by the PC through the real-time kernel running on the CPU. The vehicle’s longitudinal motion is countered by the motion of the conveyer belt. They also report the evaluation of active safety system using the scaled vehicle testbed [29].
In this project it is proposed to construct a similar test-bed to conduct vehicle simulation experiments to evaluate the designed safety systems. Furthermore, it is planned to enhance the setup to incorporate human steering control interface so that a simulated experiment with both human driver and hardware (scaled vehicle) in the loop can be performed. The proposed adaptive steering assist controller will be evaluated on the scaled vehicle using different testing scenarios. The results will be compared with the PC-based driving simulator results.
The system consists of three parts: the roadway simulator (treadmill), the position measurement system, and the scaled vehicle. A Delta Electronics VFD-M controller is used to control the speed of the treadmill, and the controller set point can be adjusted via an interface with the PC. The position measurement is accomplished by the angles of serial-link arms, as has been done in [28]. The treadmill and the arms are shown in the photo below.
The computation can be illustrated in the figure below. From the angles measured by the encoders, using simple geometry, the following equations yield the desired vehicle position.
θ θ ψ θ
θ θ θ
θ θ θ
3 2 1
2 1 2 1 1
2 1 2
1 1
) sin(
. ) sin(
.
) cos(
. ) cos(
. + +
=
+ +
=
+ +
=
l y l
l x l
The scaled vehicle
The Tamiya Mercedes-Benz CLK-DTM 1/10 is the basis of the vehicle body. The vehicle is modified to be rear wheel driven, and the DC motor is replaced with a more suitable model.
In steering control an Oriental Motor CFK545AP2 Stepper Motor has been employed to replace the original steering servo, as the dynamic behavior significantly improve the steering actuation. The modified vehicle is shown in the following picture.
The DC motors are first tested with its speed control. The results are shown below:
0 0.5 1 1.5 2 2.5 3
0 0.5 1 1.5 2
2.5 Vehicle Velocity Control
Time
Vehicle Velocity (m/s)
Vehicle Velocity Target Velocity
The steering command is achieved by a stepper motor. The stepper motor will be commanded to steer the front wheels to the desired value by a series of pulse train. The following plot shows the testing of the positioning control of the stepper motor. This corresponds to the
actuator dynamics of the steering mechanism.
Parameters of the scaled vehicle model
The bicycle model is used to model the vehicle lateral dynamics, and is briefly summarized below:
( ) ( ) ( )
y: Lateral position of the vehicle on the road φ: The yaw angle of the vehicle
df: The steering angle of the front tires dr: The steering angle of the rear tires m: The vehicle’s mass
L: The length of the vehicle
U: Longitudinal velocity
Cαf: Front wheel corning stiffness Cαr: Rear wheel corning stiffness a: Distance from C.G. to the front axle b: Distance from C.G. to the rear axle
Iz: The moment of inertial of the vehicle about z-axis
From Buckingham Π Theorem, the dimensional analysis can be performed on the model parameters and a set of Π groups are obtained. The Π groups are the basis for dynamic similitude.
The parameters of the scaled vehicle model that need to be matched are:
Variable Symbol Dimension
Length from front axle to C.G.
Length from rear axle to C.G.
Front tire forces produced per unit slip Rear tire forces produced per unit slip
Z-axis moment of inertia Mass of vehicle Length of the vehicle
Speed of the vehicle
U
L
The dimensional analysis can be performed in a matrix form, and the results are:
Relevant Variables
Dimension Analysis
Dimensionless Parameters (Π Group)
0
Based on the Buckingham Π Theorem, it is found that if the scaled vehicle’s Π group matches the values of full sized vehicle, their dynamic similitude can be ensured.
Preliminary testing of the scaled vehicle
Longitudinal Control
For the longitudinal control, the vehicle is required to track the treadmill speed and maintain the vehicle in the center of the conveyer. A PID controller is used to adjust the vehicle speed based on the measurement of vehicle longitudinal position. The plot below shows the experimental results of a treadmill speed of 2.5 m/s. It is seen that at steady state there are ripple in the vehicle velocity. The reason is the transmission mechanism consists of several gears, and the matching between gears is not very smooth. This issue is addressed in the next revision.
Average
Steering Control
To ensure the steering capability of the system, a simple dual-loop driver model is selected as the steering controller of the vehicle. The vehicle is required to undergo a lane change maneuver. The two plots shown below present the experimental results of such tests. It is seen that the steering control is successfully implemented on the scaled vehicle testbed.
Driven by human
After testing the system with a steering controller, the system is tested by a human with the encoder serving as the steering wheel. The figure below shows the results of the human trying to perform a double lane change maneuver. It is seen that the vehicle trajectory is not very smooth. The reason is that the encoder shaft is too small and is easily affected by jitters of human hands. A full size steering wheel is constructed with encoders connected to the shaft and will be used for human driving the scaled vehicle.
0 10 20 30 40 50 60 70 80 90 100 110
-30 -20 -10 0 10 20
30 Vehicle Yaw Angle
Time
Yaw Angle
Target Actual
0 10 20 30 40 50 60 70 80 90 100 110
0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
1 Vehicle Trajectory
Y-Axis
Time
Target Actual
0 5 10 15 20 25
-0.5 0 0.5 1 1.5 2 2.5 3
Time (sec) Vehicle Velocity
Velocity (m/s)
0 10 20 30 40 50 60 70 80 -1
-0.5 0 0.5 1
1.5 Human Input- Vehicle Trajectory
Time (sec)
Vehicle Trajectory Steering Angle
IV Conclusions
In this research the adaptive steering assist control with respect to driver model uncertainty is investigated. The on-line driver steering model estimation algorithm based on ARMAX structure is investigated. On-line simulation with existing driving simulator data is conducted and the results show that it is possible to derive meaningful clues from the input/output data of the driver. Adaptive controller design is performed using both MRAC and STC methods. The simulations show that this approach is effective in handling driver model parametric variations. Different observer designs are also investigated in MRAC that needs full state feedback. Due to the difficulty in the driving simulator, human-in-the-loop experiments to verify the on-line driver modeling algorithm and adaptive controller are not complete yet, and will be finished in the near future. The scaled vehicle testbed is complete, and tested using simulations and experiments. This setup will serve as an extra proving testbed for the controllers and modeling algorithms. It can also be used in future active safety systems evaluations.
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