Study and Experiment on Neural-network-based Feedback Linearization Motion Control for a Spherical Mobile Robot

ROBOT ◽  
2013 ◽  
Vol 34 (4) ◽  
pp. 455
Author(s):  
Yili ZHENG ◽  
Hanxu SUN ◽  
Jinhao LIU
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Motion Control ◽  
Feedback Linearization ◽  
Spherical Mobile Robot

Robust Control of a Low-Cost Mobile Robot Using a Neural Network Uncertainty Compensator

2014 ◽  
Author(s):  
Aliasghar Arab ◽  
Jingang Yi ◽  
Mohammad Mahdi Fateh ◽  
Soroush Arabshahi
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Mobile Robot ◽  
Feedback Linearization ◽  
Low Cost ◽  
Control Design ◽  
External Disturbances ◽  
Model Free ◽  
Comparison Results ◽  
Modeling Uncertainties

This paper presents a robust control design for a low-cost mobile robot under modeling uncertainties and external disturbances. We use a radial basis function neural network (RBFNN) to estimate and compensate for the model uncertainties and external disturbances. The proposed control design is model-free with guaranteed stability and good path-following performance. The RBFNN weight regulation and adaptive gains are designed based on the Lypanov method. Simulation and experimental results illustrate the design and demonstrate the strength of the proposed control applied to a nonholonomic wheeled mobile robot driven by low-cost permanent magnet dc motors without shaft encoders. The comparison results between proposed control and feedback linearization control confirm the effective role of the compensator in terms of precision, simplicity of design and computations.

Neural Network Compensation Method for Path Tracking Control of a Spherical Mobile Robot

2014 ◽  
Vol 635-637 ◽  
pp. 1325-1328
Author(s):  
Yao Cai ◽  
Feng Gao ◽  
Ze Ning Liu
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Tracking Control ◽  
Degrees Of Freedom ◽  
Control Method ◽  
Path Tracking ◽  
Dynamics Model ◽  
Compensation Strategy ◽  
Spherical Mobile Robot ◽  
Dynamics Models

This paper presents a neural network compensation strategy for the path tracking control of a spherical mobile robot BHQ-2 including a pendulum with two degrees of freedom. Based on our previous work, we propose a simplified method to decompose the dynamics model of BHQ-2 to be two sub-dynamics models. Applying the fuzzy guidance control method and a neural network compensation strategy, a path tracking controller for robot BHQ-2 is designed.

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