Matching Items (4)
Description
In this research work, a novel control system strategy for the robust control of an unmanned ground vehicle is proposed. This strategy is motivated by efforts to mitigate the problem for scenarios in which the human operator is unable to properly communicate with the vehicle. This novel control system strategy consisted of three major components: I.) Two independent intelligent controllers, II.) An intelligent navigation system, and III.) An intelligent controller tuning unit. The inner workings of the first two components are based off the Brain Emotional Learning (BEL), which is a mathematical model of the Amygdala-Orbitofrontal, a region in mammalians brain known to be responsible for emotional learning. Simulation results demonstrated the implementation of the BEL model to be very robust, efficient, and adaptable to dynamical changes in its application as controller and as a sensor fusion filter for an unmanned ground vehicle. These results were obtained with significantly less computational cost when compared to traditional methods for control and sensor fusion. For the intelligent controller tuning unit, the implementation of a human emotion recognition system was investigated. This system was utilized for the classification of driving behavior. Results from experiments showed that the affective states of the driver are accurately captured. However, the driver's affective state is not a good indicator of the driver's driving behavior. As a result, an alternative method for classifying driving behavior from the driver's brain activity was explored. This method proved to be successful at classifying the driver's behavior. It obtained results comparable to the common approach through vehicle parameters. This alternative approach has the advantage of directly classifying driving behavior from the driver, which is of particular use in UGV domain because the operator's information is readily available. The classified driving mode was used tune the controllers' performance to a desired mode of operation. Such qualities are required for a contingency control system that would allow the vehicle to operate with no operator inputs.
ContributorsVargas-Clara, Alvaro (Author) / Redkar, Sangram (Thesis advisor) / McKenna, Anna (Committee member) / Cooke, Nancy J. (Committee member) / Arizona State University (Publisher)
Created2015
Description
This dissertation treats a number of related problems in control and data analysis of complex networks.
First, in existing linear controllability frameworks, the ability to steer a network from any initiate state toward any desired state is measured by the minimum number of driver nodes. However, the associated optimal control energy can become unbearably large, preventing actual control from being realized. Here I develop a physical controllability framework and propose strategies to turn physically uncontrollable networks into physically controllable ones. I also discover that although full control can be guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control energy to achieve actual control, and my work provides a framework to address this issue.
Second, in spite of recent progresses in linear controllability, controlling nonlinear dynamical networks remains an outstanding problem. Here I develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another. I introduce the concept of attractor network and formulate a quantifiable framework: a network is more controllable if the attractor network is more strongly connected. I test the control framework using examples from various models and demonstrate the beneficial role of noise in facilitating control.
Third, I analyze large data sets from a diverse online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors: linear, “S”-shape and exponential growths. Inspired by cell population growth model in microbial ecology, I construct a base growth model for meme popularity in OSNs. Then I incorporate human interest dynamics into the base model and propose a hybrid model which contains a small number of free parameters. The model successfully predicts the various distinct meme growth dynamics.
At last, I propose a nonlinear dynamics model to characterize the controlling of WNT signaling pathway in the differentiation of neural progenitor cells. The model is able to predict experiment results and shed light on the understanding of WNT regulation mechanisms.
First, in existing linear controllability frameworks, the ability to steer a network from any initiate state toward any desired state is measured by the minimum number of driver nodes. However, the associated optimal control energy can become unbearably large, preventing actual control from being realized. Here I develop a physical controllability framework and propose strategies to turn physically uncontrollable networks into physically controllable ones. I also discover that although full control can be guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control energy to achieve actual control, and my work provides a framework to address this issue.
Second, in spite of recent progresses in linear controllability, controlling nonlinear dynamical networks remains an outstanding problem. Here I develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another. I introduce the concept of attractor network and formulate a quantifiable framework: a network is more controllable if the attractor network is more strongly connected. I test the control framework using examples from various models and demonstrate the beneficial role of noise in facilitating control.
Third, I analyze large data sets from a diverse online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors: linear, “S”-shape and exponential growths. Inspired by cell population growth model in microbial ecology, I construct a base growth model for meme popularity in OSNs. Then I incorporate human interest dynamics into the base model and propose a hybrid model which contains a small number of free parameters. The model successfully predicts the various distinct meme growth dynamics.
At last, I propose a nonlinear dynamics model to characterize the controlling of WNT signaling pathway in the differentiation of neural progenitor cells. The model is able to predict experiment results and shed light on the understanding of WNT regulation mechanisms.
ContributorsWang, Lezhi (Author) / Lai, Ying-Cheng (Thesis advisor) / Wang, Xiao (Thesis advisor) / Papandreoou-Suppappola, Antonia (Committee member) / Brafman, David (Committee member) / Arizona State University (Publisher)
Created2017
Description
Control algorithm development for quadrotor is usually based solely on rigid body dynamics neglecting aerodynamics. Recent work has demonstrated that such a model is suited only when operating at or near hover conditions and low-speed flight. When operating in confined spaces or during aggressive maneuvers destabilizing forces and moments are induced due to aerodynamic effects. Studies indicate that blade flapping, induced drag, and propeller drag influence forward flight performance while other effects like vortex ring state, ground effect affect vertical flight performance. In this thesis, an offboard data-driven approach is used to derive models for parasitic (bare-airframe) drag and propeller drag. Moreover, thrust and torque coefficients are identified from static bench tests. Among the two, parasitic drag is compensated for in the position controller module in the PX4 firmware. 2-D circular, straight line, and minimum snap rectangular trajectories with corridor constraints are tested exploiting differential flatness property wherein altitude and yaw angle are constant. Flight tests are conducted at ASU Drone Studio and results of tracking performance with default controller and with drag compensated position controller are presented. Root mean squared tracking error in individual axes is used as a metric to evaluate the model performance. Results indicate that, for circular trajectory, the root mean squared error in the x-axis has reduced by 44.54% and in the y-axis by 39.47%. Compensation in turn degrades the tracking in both axis by a maximum under 12% when compared to the default controller for rectangular trajectory case. The x-axis tracking error for the straight-line case has improved by 44.96% with almost no observable change in the y-axis.
ContributorsNolastname, Kashyap Sathyamurthy (Author) / Zhang, Wenlong (Thesis advisor) / Yong, Sze Zheng (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2020
Description
This thesis will cover the basics of 2-dimensional motion of a parafoil system to determine and
design an altitude controller that will result in the parafoil starting at a location and landing within the
accepted bounds of a target location. It will go over the equations of motion, picking out the key
formulas that map out how a parafoil moves, and determine the key inputs in order to get the desired
outcome of a controlled trajectory. The physics found in the equations of motion will be turned into
state space representations that organize it into differential equations that coding software can make
use of to make trajectory calculations. MATLAB is the software used throughout the paper, and all code
used in the thesis paper will be written out for others to check and modify to their desires. Important
aspects of parafoil gliding motion will be discussed and tested with variables such as the natural glide
angle and velocity and the utilization of checkpoints in trajectory controller design. Lastly, the region of
attraction for the controller designed in this thesis paper will be discussed and plotted in order to show
the relationship between the four input variables, x position, y position, velocity, and theta.
The controller utilized in this thesis paper was able to plot a successful flight trajectory from
10m in the air to a target location 50m away. This plot is found in figure 18. The parafoil undershot the
target location by about 9 centimeters (0.18% error). This is an acceptable amount of error and shows
that the controller was a success in controlling the system to reach its target destination. When
compared to the uncontrolled flight in figure 17, the target will only be reached when a controller is
applied to the system.
design an altitude controller that will result in the parafoil starting at a location and landing within the
accepted bounds of a target location. It will go over the equations of motion, picking out the key
formulas that map out how a parafoil moves, and determine the key inputs in order to get the desired
outcome of a controlled trajectory. The physics found in the equations of motion will be turned into
state space representations that organize it into differential equations that coding software can make
use of to make trajectory calculations. MATLAB is the software used throughout the paper, and all code
used in the thesis paper will be written out for others to check and modify to their desires. Important
aspects of parafoil gliding motion will be discussed and tested with variables such as the natural glide
angle and velocity and the utilization of checkpoints in trajectory controller design. Lastly, the region of
attraction for the controller designed in this thesis paper will be discussed and plotted in order to show
the relationship between the four input variables, x position, y position, velocity, and theta.
The controller utilized in this thesis paper was able to plot a successful flight trajectory from
10m in the air to a target location 50m away. This plot is found in figure 18. The parafoil undershot the
target location by about 9 centimeters (0.18% error). This is an acceptable amount of error and shows
that the controller was a success in controlling the system to reach its target destination. When
compared to the uncontrolled flight in figure 17, the target will only be reached when a controller is
applied to the system.
ContributorsTeoharevic, Filip (Author) / Grewal, Anoop (Thesis director) / Lee, Hyunglae (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05