In this paper, a roll stability controller (RSC) is presented based on an eight degree of freedom dynamic vehicle model. The controller is designed for and tested on a scaled vehicle performing obstacle avoidance maneuvers on a populated test track. A rapidly-exploring random tree (RRT) algorithm is used for the vehicle to execute a trajectory around an obstacle, and examines the geographic, non-homonymic, and dynamic constraints to maneuver around the obstacle. A model predictive controller (MPC) uses information about the vehicle state and, based on a weighted performance measure, generates an optimal trajectory around the obstacle. The RSC uses the standard vehicle state sensors: four wheel mounted encoders, a steering angle sensor, and a six degree of freedom inertial measurement unit (IMU). An emphasis is placed on the mitigation of rollover and spin-out, however if a safe maneuver is not found and a collision is inevitable, the program will run a brake command to reduce the vehicle speed before impact. The trajectory is updated at a rate of 20 Hz, providing improved stability and maneuverability for speeds up to 10 ft/s and turn angles of up to 20°.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-1810 |
| Date | 01 June 2012 |
| Creators | Noxon, Nikola John Linn |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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