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Extra info for Advances in Intelligent Vehicles
H. Man et al. -Y. -J. Lin, Robust nonsingular terminal sliding model control for nonlinear magnetic bearing system, IEEE Transaction on Control Systems Technology 19 (3) (2011) 636e643.  H. F. Li, Terminal sliding mode control for spacecraft formation flying, IEEE Transaction on Aerospace and Electronic Systems 45 (3) (2009) 835e846. S. Guo, C. Li, Terminal sliding mode control for coordinated motion of a space rigid manipulator with external disturbance, Applied Mathematics and Mechanics 29 (5) (2008) 583e590.
Reprinted with permission from “Simulation and testing environments for the DARPA Urban Challenge”, IEEE International Conference on Vehicular Electronics and Safety, 2008. ICVES 2008, 22e24 September 2008, pp. 222e226. As part of the continuing Cyber-Physical Systems (CPS) research program at OSU, the CITR testbed discussed in the following paragraphs was used as a successful teaching tool for high school students. In a series of Summer Institute programs lasting for 2 weeks each summer, groups of students learned the basics of intelligent vehicle research, including how to control a small-scale robot, how to read data from sensors, and how to make decisions based on the sensory data .
The desired Â ÃT Ã equilibrium of the control system is xÃ ¼ 0 lÃ q_ w 0 0 . 01). 1888. The initial conditions are chosen as: l ¼ l_ ¼ qw ¼ q_ w ¼ q_ 1 ¼ 0, q1 ¼ 0:1. Solving the above linear quadratic problem, the gain matrix K was obtained as follows: ! 8. The dashed lines represent the desired value and the solid lines are simulation trajectories. It appears that the LQR controller has the ability to guarantee the inclination angle of the body to be approximately zero in finite time, but the large overshoot and rapid change of states may make passengers feel uncomfortable.
Advances in Intelligent Vehicles by Yaobin Chen and Lingxi Li (Auth.)