Practical point stabilization of a nonholonomic mobile robot using neural networks

Author(s):  
R. Fierro ◽  
F.L. Lewis
Author(s):  
Şahin Yildirim ◽  
Sertaç Savaş

The goal of this chapter is to enable a nonholonomic mobile robot to track a specified trajectory with minimum tracking error. Towards that end, an adaptive P controller is designed whose gain parameters are tuned by using two feed-forward neural networks. Back-propagation algorithm is chosen for online learning process and posture-tracking errors are considered as error values for adjusting weights of neural networks. The tracking performance of the controller is illustrated for different trajectories with computer simulation using Matlab/Simulink. In addition, open-loop response of an experimental mobile robot is investigated for these different trajectories. Finally, the performance of the proposed controller is compared to a standard PID controller. The simulation results show that “adaptive P controller using neural networks” has superior tracking performance at adapting large disturbances for the mobile robot.


2000 ◽  
Vol 33 (27) ◽  
pp. 135-140
Author(s):  
Arbnor Pajaziti ◽  
Ahmet Shala ◽  
Agron Pajaziti ◽  
Ramë Likaj

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