This thesis describes a methodology for creative learning that applies to man and machines. Creative learning is a general approach used to solve optimal control problems. The theory contains all the components and techniques of the adaptive critic learning family but also has an architecture that permits creative learning when it is appropriate. The creative controller for intelligent machines integrates a dynamic database and a task control center into the adaptive critic learning model. The task control center can function as a command center to decompose tasks into sub-tasks with different dynamic models and criteria functions, while the dynamic database can act as an information system. The primary contribution of this work was merging the concepts of adaptive critics with a dynamic database and task control center to create a new learning methodology called creative control.
To illustrate ambiguousness of the theory of creative control, several experimental simulations for robot arm manipulators and mobile wheeled vehicles were included. The robot arm manipulator was one experimental example for testing the creative control learning theory. The simulation results showed that the best performance was obtained by using adaptive critic controller among all other controllers. By changing the paths of the robot arm manipulator in the simulation, it was demonstrated that the learning component of the creative controller was adapted to a new set of criteria. The Bearcat Cub robot was another experimental example used for testing the creative control learning. The kinematic and dynamic models of the Bearcat Cub were derived. Additionally, an optimal PID control algorithm for WMR was developed to choose the parameters of the controllers.
The significance of this research was to generalize the adaptive control theory in a direction toward highest level of human learning – imagination. In doing this it is hoped to better understand the adaptive learning theory and move forward to develop more human-intelligence-like components and capabilities into the intelligent robot. It is also hoped that a greater understanding of machine learning will motivate similar studies to improve human learning.
|School:||University of Cincinnati|
|School Location:||United States -- Ohio|
|Source:||DAI-B 79/10(E), Dissertation Abstracts International|
|Keywords:||Adaptive critic, Creative learning, Dynamics, Intelligent robots, Neural networks, Robot control|
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