Abstract
Robots have been a revolutionizing force in manufacturing in the 20th and 21st century but have proven too dangerous around humans to be used in many other fields including medicine. We describe a new control algorithm for robots developed by the Brigham Young University Robotics and Dynamics and Robotics Laboratory that has shown potential to make robots less dangerous to humans and suitable to work in more applications. We analyze the computational complexity of this algorithm and find that it could be a feasible control for even the most complicated robots. We also show conditions for a system which guarantee local stability for this control algorithm.
Degree
MS
College and Department
Physical and Mathematical Sciences; Mathematics
Rights
http://lib.byu.edu/about/copyright/
BYU ScholarsArchive Citation
Friedbaum, Jesse Robert, "Model Predictive Linear Control with Successive Linearization" (2018). Theses and Dissertations. 7063.
https://scholarsarchive.byu.edu/etd/7063
Date Submitted
2018-08-01
Document Type
Thesis
Handle
http://hdl.lib.byu.edu/1877/etd10309
Keywords
Control Theory, Robotics, Numerical Analysis, Model Predictive Control, MPC
Language
english