Sean N. Brennan

Using a scale testbed: Controller design and evaluation

To circumvent the cost and inherent danger in testing aggressive vehicle controllers using full-sized vehicles, a scale vehicle testbed has been developed for use as an evaluation tool to bridge the design gap between simulation studies and full-sized hardware. We describe vehicle dynamic models of the IRS simulation system, along with experimental verification using frequency response and parameter measurements. The next section provides a detailed discussion of dynamic similitude via the Buckingham pi theorem (1914), as well as a graphical comparison between distributions of dynamic scale parameters of scaled and full-sized vehicles. Following that, a yaw rate vehicle controller is introduced to exemplify the type of investigations that can be conducted with the IRS. The controller uses differential torque and brake inputs to assist the driver to control the yaw rate of the vehicle and is designed to achieve model tracking while allowing the driver to maintain control over the front wheels of the vehicle.

The Illinois Roadway Simulator: a mechatronic testbed for vehicle dynamics and control

The Illinois Roadway Simulator (IRS) is a novel, mechatronic, scaled testbed used to study vehicle dynamics and controls. An overview of this system is presented, and individual hardware issues are addressed. System modeling results on the vehicles and hardware are introduced, and comparisons of the resulting dynamics are made with full-sized vehicles. Comparisons are made between dynamic responses of full-scale and IRS-scale vehicles. The method of dynamic similitude is a key to gaining confidence in the scaled testbed as an accurate representation of actual vehicles to a first approximation. The IRS is then used in a vehicle control case study to demonstrate the potential benefits of scaled investigations. The idea of driver-assisted control is formulated as a yaw-rate model-following problem based on the representation of the driver as a known disturbance model. The controller is designed and implemented to show that the vehicles dynamics can be changed to match a prescribed reference model.

Observability and Controllability of Nonlinear Networks: The Role of Symmetry

Observability and controllability are essential concepts to the design of predictive observer models and feedback controllers of networked systems. For example, noncontrollable mathematical models of real systems have subspaces that influence model behavior, but cannot be controlled by an input. Such subspaces can be difficult to determine in complex nonlinear networks. Since almost all of the present theory was developed for linear networks without symmetries, here we present a numerical and group representational framework, to quantify the observability and controllability of nonlinear networks with explicit symmetries that shows the connection between symmetries and nonlinear measures of observability and controllability. We numerically observe and theoretically predict that not all symmetries have the same effect on network observation and control. Our analysis shows that the presence of symmetry in a network may decrease observability and controllability, although networks containing only rotational symmetries remain controllable and observable. These results alter our view of the nature of observability and controllability in complex networks, change our understanding of structural controllability, and affect the design of mathematical models to observe and control such networks.

Determining Gravimetric Bark Content in Cotton with Machine Vision

A method is needed to accurately and rapidly determine the gravimetric bark content of a cotton sample. Gravimetric bark content represents the percent bark mass through out the volume of a cotton sample. The current method for measuring gravimetric bark content is a labor intensive, lengthy process. Machine vision, on the other hand, is a fast, inexpensive method to measure this bulk cotton property. Ten acquired images of surfaces throughout each sample are used. Classical digital image processing tech niques isolate foreign matter regions in monochrome video images. Geometric prop erties (area and perimeter) are used to identify which foreign matter is bark and to predict the gravimetric bark content in forty-eight cotton samples with varying bark and total foreign matter content. We suggest a model with six features and intercept, which has an estimated error of 0.46% bark mass.

Robust Scalable Vehicle Control via Non-Dimensional Vehicle Dynamics

A temporal and spatial re-parameterization of the linear vehicle Bicycle Model is presented utilizing non-dimensional ratios of vehicle parameters called p-groups. Investigation of the p-groups using compiled data from 44 published sets of Vehicle Dynamics reveals a normal distribution about a line through p-space. The normal distribution suggests numerical-values for an ‘average’ vehicle and maximum perturbations about the average. A state-feedback controller is designed utilizing the p-space line and the expected p-perturbations to robustly stabilize all vehicles encompassed by the normal distribution of vehicle parameters. Experimental verification is obtained using a scaled vehicle.

Model-based Prediction of Skid-steer Robot Kinematics Using Online Estimation of Track Instantaneous Centers of Rotation

This paper presents a kinematic extended Kalman filter EKF designed to estimate the location of track instantaneous centers of rotation ICRs and aid in model-based motion prediction of skid-steer robots. Utilizing an ICR-based kinematic model has resulted in impressive odometry estimates for skid-steer movement in previous works, but estimation of ICR locations was performed offline on recorded data. The EKF presented here utilizes a kinematic model of skid-steer motion based on ICR locations. The ICR locations are learned by the filter through the inclusion of position and heading measurements. A background on ICR kinematics is presented, followed by the development of the ICR EKF. Simulation results are presented to aid in the analysis of noise and bias susceptibility. The experimental platforms and sensors are described, followed by the results of filter implementation. Extensive field testing was conducted on two skid-steer robots, one with tracks and another with wheels. ICR odometry using learned ICR locations predicts robot position with a mean error of -0.42i¾?m over 40.5i¾?m of travel during one tracked vehicle test. A test consisting of driving both vehicles approximately 1,000i¾?m shows clustering of ICR estimates for the duration of the run, suggesting that ICR locations do not vary significantly when a vehicle is operated with low dynamics.

Analytical Prediction of Self-Organized Traffic Jams as a Function of Increasing ACC Penetration

Self-organizing traffic jams are known to occur in medium-to-high density traffic flows, and it is suspected that adaptive cruise control (ACC) may affect their onset in mixed human-ACC traffic. Unfortunately, closed-form solutions that predict the occurrence of these jams in mixed human-ACC traffic do not exist. In this paper, both human and ACC driving behaviors are modeled using the General Motors fourth car-following model and are distinguished by using different model parameter values. A closed-form solution that explains the impact of ACC on congestion due to the formation of self-organized traffic jams (or “phantom” jams) is presented. The solution approach utilizes the master equation for modeling the self-organizing behavior of traffic flow at a mesoscopic scale and the General Motors fourth car-following model for describing the driver behavior at the microscopic scale. It is found that, although the introduction of ACC-enabled vehicles into the traffic stream may produce higher traffic flows, it also results in disproportionately higher susceptibility of the traffic flow to congestion.

Terrain-based road vehicle localization on multi-lane highways

This work develops an algorithm for estimating the lateral lane index of road vehicles on multi-lane roadways by correlating vehicle attitude measurements to terrain maps of the individual lanes of travel. To localize a vehicle, a Bayesian belief algorithm and a particle filter algorithm are described and applied off-line using data collected from two lanes along a local highway. Results demonstrate that terrain-based algorithms are capable of measuring lane index. Because these measurements are immune to lighting conditions, this solution is a good complement to existing lane-detection camera systems.

Terrain-based road vehicle localization using particle filters

This work develops a real-time algorithm to localize a vehicle in the direction of travel without the use of GPS. The inputs to the algorithm include a terrain map of road grade and pitch measurements from an in-vehicle pitch sensor. Localization is achieved in real-time using a particle filter described in detail in this work. Simulations and experiments at The Pennsylvania Transportation Institute test track are used to demonstrate the algorithm, observe the speed of convergence, and to determine key parameters for practical implementation. The results indicate that the method can quickly localize a vehicle with one-meter accuracy or better.

The Illinois Roadway Simulator-a hardware-in-the-loop testbed for vehicle dynamics and control

The Illinois Roadway Simulator (IRS) is a novel, hardware-in-the-loop (HIL) scale vehicle testbed used to study vehicle dynamics and controls. An overview of this system is presented, and individual hardware issues are addressed. System modeling results on the vehicles and hardware are introduced, and comparisons of the resulting dynamics are made with full sized vehicles. To address the realism factor of using scaled vehicles, comparisons are made between vehicle responses of full and 1:10 scale vehicles. Finally, the IRS is used to examine the effect of actuator dynamics on a particular vehicle control application.