Neural Dynamic Control of a Nonholonomic Mobile Robot Incorporating the Actuator Dynamics

2008 ◽  
Author(s):  
Nardênio A. Martins ◽  
Douglas W. Bertol ◽  
Edson R. De Pieri ◽  
Eugênio B. Castelan ◽  
Maria M. Dias
Keyword(s):  
Mobile Robot ◽  
Dynamic Control ◽  
Neural Dynamic ◽  
Actuator Dynamics ◽  
Nonholonomic Mobile Robot

Artificial Higher Order Neural Networks for Modeling MIMO Discrete-Time Nonlinear System

2013 ◽  
pp. 30-43
Author(s):  
Michel Lopez-Franco ◽  
Alma Y. Alanis ◽  
Nancy Arana-Daniel ◽  
Carlos Lopez-Franco
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Discrete Time ◽  
Higher Order ◽  
Robot Kinematics ◽  
Robot Dynamics ◽  
Plant Model ◽  
Actuator Dynamics ◽  
Nonholonomic Mobile Robot ◽  
Higher Order Neural Networks

In this chapter, a Recurrent Higher Order Neural Network (RHONN) is used to identify the plant model of discrete time nonlinear systems, under the assumption that all the state is available for measurement. Then the Extended Kalman Filter (EKF) is used to train the RHONN. The applicability of this scheme is illustrated by identification for an electrically driven nonholonomic mobile robot. Traditionally, modeling of mobile robots only considers its kinematics. It has been well known that the actuator dynamics is an important part of the design of the complete robot dynamics. However, most of the reported results in literature do not consider all parametric uncertainties for mobile robots at the actuator level. This is due to the modeling problem becoming extremely difficult as the complexity of the system dynamics increases, and the mobile robot model includes the uncertainties of the actuator dynamics as well as the uncertainties of the robot kinematics and dynamics.

Path Planning of Mobile Robot Using Improved Artificial Bee Colony Algorithm

2020 ◽  
Vol 38 (9A) ◽  
pp. 1384-1395
Author(s):  
Rakaa T. Kamil ◽  
Mohamed J. Mohamed ◽  
Bashra K. Oleiwi
Keyword(s):  
Mobile Robot ◽  
Comparative Research ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Dynamic Control ◽  
Computational Time ◽  
Bee Colony ◽  
Shortest Distance ◽  
Minimum Number ◽  
Cubic Polynomial

A modified version of the artificial Bee Colony Algorithm (ABC) was suggested namely Adaptive Dimension Limit- Artificial Bee Colony Algorithm (ADL-ABC). To determine the optimum global path for mobile robot that satisfies the chosen criteria for shortest distance and collision–free with circular shaped static obstacles on robot environment. The cubic polynomial connects the start point to the end point through three via points used, so the generated paths are smooth and achievable by the robot. Two case studies (or scenarios) are presented in this task and comparative research (or study) is adopted between two algorithm’s results in order to evaluate the performance of the suggested algorithm. The results of the simulation showed that modified parameter (dynamic control limit) is avoiding static number of limit which excludes unnecessary Iteration, so it can find solution with minimum number of iterations and less computational time. From tables of result if there is an equal distance along the path such as in case A (14.490, 14.459) unit, there will be a reduction in time approximately to halve at percentage 5%.

Neural Torque Controllers For Trajectory Tracking Problem Of A Nonholonomic Mobile Robot

2007 ◽  
Vol 5 (2) ◽  
pp. 121-136
Author(s):  
N. Martins ◽  
D. Bertol ◽  
W. Lombardi ◽  
E. R. Pieri ◽  
E. Castelan
Keyword(s):  
Mobile Robot ◽  
Trajectory Tracking ◽  
Tracking Problem ◽  
Nonholonomic Mobile Robot
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