A New Adaptive Tracking Control For Wheeled Mobile Robot

Barzamini, R.; Yazdizadeh, Alireza; Rahmani, Ahmed

doi:10.1109/ramech.2006.252750

Cited by 12 publications

(7 citation statements)

References 6 publications

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“…This control approach has good performance; however, it depends on model parameters. When model parameters are unknown, an adaptive control for adjusting these parameters is required (Barzamini, R. et al, 2006), . The adaptive tracking control of the robot system is based on .…”

Section: Adaptive Trajectory Tracking Controllermentioning

confidence: 99%

SLAM-based Cross-a-Door Solution Approach for a Robotic Wheelchair

Cheein

Cruz²,

Bastos³

et al. 2009

International Journal of Advanced Robotic Systems

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Section: Adaptive Trajectory Tracking Controllermentioning

confidence: 99%

SLAM-based Cross-a-Door Solution Approach for a Robotic Wheelchair

Cheein

Cruz²,

Bastos³

et al. 2009

International Journal of Advanced Robotic Systems

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“…Wheeled mobile robots are the type of mobile robotic systems that we are considering in this thesis because they are most widely used among the class of mobile robots. This is due to their fast maneuvering, simple controllers and energy saving characteristics [2,3,4,5].The work includes the development and construction of neural network based trajectory tracking controllers. The control algorithm is a NN-based adaptive controller which tunes the gains of the back-stepping controller online according to the robot reference trajectory and its initial posture.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2016

IJERA

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This work primarily addresses the design and implementation of a neural network based controller for the trajectory tracking of a differential drive mobile robot. The proposed control algorithm is an NN-based adaptive controller which tunes the gains of the back-stepping controller online according to the robot reference trajectory and its initial posture. In this method, a neural network is needed to learn the characteristics of the plant dynamics and make use of it to determine the future inputs that will minimize error performance index so as to compensate the back-stepping controller gains. The advantages and disadvantages of theproposed control algorithms will be discussed in each section with illustrations.Comprehensive system modeling including robot kinematics and dynamics modeling has been done. The dynamic modeling is done using Newtonian and Lagrangian methodologies for nonholonomic systems and the results are compared to verify the accuracy of each method. Simulation of the robot model and different controllers has been done using Matlab and Matlab Simulink.

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“…Some authors used backstepping techniques to design the adaptive control law such as in Do, Jiang, and Pan (2004). On the other hand, other authors have used a feedback linearization approach and Lyapunov theories such as in Barzamini, Yazdizadeh, and Rahmani (2006). However, most authors evaluate their proposed controller only by simulations.…”

Section: Introductionmentioning

confidence: 99%

“…This latter characteristic is an advantage when evaluating the control experimentally. Generally, in the literature, adaptive, robust or sliding mode controllers of wheeled mobile robots are proved only by simulations (Barzamini et al, 2006;Do et al, 2004;Fierro & Lewis, 1997;Park, Yoo, Park, & Choi, 2009;Zhang, Hong, Chung, & Velinsky, 1998;Zhu, Dong, Hu, & Cai, 2006). The main difficulty is that they use wheel torques as control signals, which cannot be directly manipulated in most of the real vehicles.…”

Section: Introductionmentioning

confidence: 99%

“…The stability analysis is performed using the Lyapunov and input/output stability theories. The resulting adaptive control is different from Barzamini et al (2006) because robot accelerations are not used, and it is different from Martins et al (2008) because it does not uses a cascade control structure. A projection algorithm is included to avoid parameter drifts, and its effect on the adaptive control is analyzed in the work.…”

Section: Introductionmentioning

confidence: 99%

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