Advances in Intelligent Vehicles - download pdf or read online

By Yaobin Chen and Lingxi Li (Auth.)

ISBN-10: 0123971993

ISBN-13: 9780123971999

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H. Choi, Adaptive neural sliding mode control of nonholonomic wheeled mobile robots with model uncertainty, IEEE Transactions on Control System Technology 17 (1) (2009). [18] J. H. , Sliding-mode velocity control of mobile-wheeled inverted-pendulum systems, IEEE Transaction on Robotics 26 (4) (2010) 750e758. [19] 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 là 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.

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Advances in Intelligent Vehicles by Yaobin Chen and Lingxi Li (Auth.)


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