By Yaobin Chen and Lingxi Li (Auth.)
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Additional info for Advances in Intelligent Vehicles
H. Choi, Adaptive neural sliding mode control of nonholonomic wheeled mobile robots with model uncertainty, IEEE Transactions on Control System Technology 17 (1) (2009).  J. H. , Sliding-mode velocity control of mobile-wheeled inverted-pendulum systems, IEEE Transaction on Robotics 26 (4) (2010) 750e758.  H. S. Erwin, Sliding control approach to underactuated multibody systems, American Control Conference (2004) 1283e1288. S. S. R. Chang, Modeling and hierarchical tracking control of tri-wheeled mobile robots, IEEE Transaction on Robotics 22 (5) (2006) 1055e1062.
2 Optimal Braking Control Simulation on Flat Ground We verify the effectiveness of optimal braking control by the simulation in MATLAB. The limitation of seat displacement is jlmax j ¼ 0:2. 05. 8 were used. Optimal parameters are computed by GA as lÃ ¼ À0:2, TfÃ0 ¼ 0:01. 10. 8 s. 4 and the same initial conditions in optimal braking control. 10: Simulation Results of Braking a UW-Car System With Optimal Parameters. 11: Simulation Results of Braking a UW-Car System Without Optimal Parameters. 245), in approximately 9 seconds.
Robust control methods might be a good choice for the nonlinear underactuated model. The TSMC method is investigated in the following due to its robustness to model uncertainties and external disturbances. 3. 4 s, Tf2 ¼ 1 s with the same initial conditions in the LQR controller. 1888. 2 d 1 / dt [rad/s] w [rad] 100 d w / dt [rad/s] the discontinuous sign function sgn(s) with a continuous saturation function of small boundary layers. 9. 9: Simulation Results of a UW-Car System Using TSMC Running at a Constant Velocity 7 rad/s.
Advances in Intelligent Vehicles by Yaobin Chen and Lingxi Li (Auth.)