CAMbot: Customer assistance mobile manipulator robot

Author(s):  
Leo Pauly ◽  
M. V. Baiju ◽  
P. Viswanathan ◽  
Praveen Jose ◽  
Divya Paul ◽  
...  
Author(s):  
Abdelkrim Brahmi ◽  
Maarouf Saad ◽  
Brahim Brahmi ◽  
Ibrahim El Bojairami ◽  
Guy Gauthier ◽  
...  

In the research put forth, a robust adaptive control method for a nonholonomic mobile manipulator robot, with unknown inertia parameters and disturbances, was proposed. First, the description of the robot’s dynamics model was developed. Thereafter, a novel adaptive sliding mode control was designed, to which all parameters describing involved uncertainties and disturbances were estimated by the adaptive update technique. The proposed control ensures a relatively good system tracking, with all errors converging to zero. Unlike conventional sliding mode controls, the suggested is able to achieve superb performance, without resulting in any chattering problems, along with an extremely fast system trajectories convergence time to equilibrium. The aforementioned characteristics were attainable upon using an innovative reaching law based on potential functions. Furthermore, the Lyapunov approach was used to design the control law and to conduct a global stability analysis. Finally, experimental results and comparative study collected via a 05-DoF mobile manipulator robot, to track a given trajectory, showing the superior efficiency of the proposed control law.


Author(s):  
Abdelkrim Brahmi ◽  
Maarouf Saad ◽  
Guy Gauthier ◽  
Wen Hong Zhu ◽  
Jawhar Ghommam

Author(s):  
Guy Gauthier ◽  
Wen Hong Zhu ◽  
Jawhar Ghommam ◽  
Abdelkrim Brahmi ◽  
Maarouf Saad

Kybernetes ◽  
2014 ◽  
Vol 43 (2) ◽  
pp. 281-306
Author(s):  
Long Thang Mai ◽  
Nan Yao Wang

Purpose – The purpose of this paper is to improve the flexibility and tracking errors of the controllers-based neural networks (NNs) for mobile manipulator robot (MMR) in the presence of time-varying uncertainties. Design/methodology/approach – The conventional backstepping force/motion control is developed by the wavelet fuzzy CMAC neural networks (WFCNNs) (for mobile-manipulator robot). The proposed WFCNNs are applied in the tracking-position-backstepping controller to deal with the uncertain dynamics of the controlled system. In addition, an adaptive robust compensator is proposed to eliminate the inevitable approximation errors, uncertain disturbances, and relax the requirement for prior knowledge of the controlled system. Besides, the position tracking controller, an adaptive robust constraint-force is also considered. The online-learning algorithms of the control parameters (WFCNNs, robust term and constraint-force controller) are obtained by using the Lyapunov stability theorem. Findings – The design of the proposed method is determined by the Lyapunov theorem such that the stability and robustness of the control-system are guaranteed. Originality/value – The WFCNNs are more the generalized networks that can overcome the constant out-weight problem of the conventional fuzzy cerebellar model articulation controller (FCMAC), or can converge faster, give smaller approximation errors and size of networks in comparison with FNNs/NNs. In addition, an intelligent-control system by inheriting the advantage of the conventional backstepping-control-system is proposed to achieve the high-position tracking for the MMR control system in the presence of uncertainties variation.


Author(s):  
Víctor Hugo Andaluz ◽  
Washington X. Quevedo ◽  
Fernando A. Chicaiza ◽  
José Varela ◽  
Cristian Gallardo ◽  
...  

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