Optimize the Satellite Orientation by Using the Inertial Pulse Method, Intelligent Damper, Dynamics, Kinematics and Proper Neural Network

2012 ◽  
Vol 232 ◽  
pp. 665-673
Author(s):  
Adrian Olaru
Keyword(s):  
Neural Network ◽  
Inverse Kinematics ◽  
Inverse Dynamics ◽  
Pulse Method ◽  
Good Precision ◽  
Small Satellite ◽  
The Neural Network ◽  
Control Orientation ◽  
Inverse Kinematics Problem ◽  
Electrical Motors

In the paper are shown one assisted method of the control orientation inertial mechanism of the small satellite. It is presented some of the general components of the small satellite and the possibility to orientation them between three axes using the inertial pulse of the electrical motors. The angular space position was determines by command of the final point and using the proper neural network to solve the inverse kinematics problem with the minimum of the errors. The precision of the space orientation position was increased by using in the proper controlling schema a direct kinematics module, direct and inverse dynamics, intelligent damper and one multiple inertial pulse method what was described in the paper. Finally was obtained one very good precision, before 2%.The neural network, the inertial pulse method, the results and the virtual LabVIEW instrumentation could be used in many other researches on the field.

Controlling of the 3D Space Trajectory of the Multi Robots Applications by Using the Proper Iterative Pseudo Inverse Jacobian Neural Network Matrix Method

2016 ◽  
Vol 841 ◽  
pp. 227-233
Author(s):  
Adrian Olaru ◽  
Serban Olaru ◽  
Niculae Mihai ◽  
Doru Bardac
Keyword(s):  
Neural Network ◽  
Matrix Method ◽  
Inverse Kinematics ◽  
Parallel Robot ◽  
Coupled Method ◽  
Space Curves ◽  
3D Space ◽  
The Neural Network ◽  
Inverse Kinematics Problem ◽  
Pseudo Inverse

In many applications we used the multi robots with the central coordination of the 3D space trajectory. In the controlling of the space movement of the end effecter of the all robots from this type of applications and the robot’s joints one of the most important problem is to solve the forward and inverse kinematics, that is different from the single robot application. It is important to know with the extreme precision the joints relative displacements of all robots. One of the most precise method to solve the inverse kinematics problem in the robots with redundant chain is the complex coupled method of the neural network with Iterative Jacobian Pseudo Inverse method. In this paper was proposed and used the proper coupled method Iterative Pseudo Inverse Jacobian Matrix Method (IPIJMM) with Sigmoid Bipolar Hyperbolic Tangent Neural Network with Time Delay and Recurrent Links (SBHTNN-TDRL). The paper contents the mathematical matrix model of the forward kinematics of multiple robots applications, mathematical model of the proper iterative algorithm and all proper virtual LabVIEW instrumentation, to obtain the space conventional and unconventional curves in different Euller planes for one case study of three simultaneously robots movement with extreme precision of the end-effecter less than 0.001mm. The paper shown how can be changed the multi robots application in to one application with parallel robot structure with three independent robots. The presented method and the virtual instrumentations (VI) are generally and they can be used in all other robots application and for all other conventional and unconventional space curves.

Assisted Research and Optimization of the Proper Neural Network Solving the Inverse Kinematics Problem

2012 ◽  
Vol 463-464 ◽  
pp. 827-832
Author(s):  
Adrian Olaru ◽  
Serban Olaru ◽  
Dan Paune ◽  
Oprean Aurel
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Inverse Kinematics ◽  
Hyperbolic Tangent ◽  
Variable Parameters ◽  
The Neural Network ◽  
Variable Step ◽  
Inverse Kinematics Problem ◽  
Sensitive Function ◽  
Target Data

Finding the better solution of the neural network design to solve the inverse kinematics problem with the minimum of the trajectory errors is very difficult, because there are many variable parameters and many redundant solutions. The presented paper show the assisted research of the influences of some more important parameters to the final end-effector trajectory errors of the proposed neural network model solving the inverse kinematics problem. We were been studied the number of neurons in each layers, the sensitive function for the first and second layer, the magnifier coefficient of the trajectory error, the variable step of the time delay and the position of this block, the different cases of target data and the case when the hidden target data were adjusted. All obtained results were been verified by applying the proper direct kinematics virtual LabVIEW instrumentation. Finally we were obtained one optimal Sigmoid Bipolar Hyperbolic Tangent Neural Network with Time Delay and Recurrent Links (SBHTNN(TDRL)) type, what can be used to solve the inverse kinematics problem with maximum 4% of trajectory errors.

Second Order Recurrent Neural Network for the Inverse Kinematics of Redundant Manipulators

2013 ◽  
Vol 391 ◽  
pp. 114-117
Author(s):  
Run Sheng Hao
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Inverse Kinematics ◽  
Second Order ◽  
Degree Of Freedom ◽  
Redundant Manipulators ◽  
Good Precision ◽  
Inverse Kinematics Problem ◽  
Simulation Results ◽  
Three Degree Of Freedom

In this paper, the second order recurrent neural network is adopted to study the inverse kinematics problem of three degree-of-freedom planar redundant manipulators. The Simulation results show that the network can effectively solve the inverse kinematics problem of redundant manipulators, and it reaches to good precision of solution and solving speed.

scholarly journals Cerebellum-inspired neural network solution of the inverse kinematics problem

2015 ◽  
Vol 109 (6) ◽  
pp. 561-574 ◽  
Author(s):  
Mitra Asadi-Eydivand ◽  
Mohammad Mehdi Ebadzadeh ◽  
Mehran Solati-Hashjin ◽  
Christian Darlot ◽  
Noor Azuan Abu Osman
Keyword(s):  
Neural Network ◽  
Inverse Kinematics ◽  
Network Solution ◽  
Inverse Kinematics Problem

Manipulator Inverse Kinematics using an Adaptive Back-propagation Algorithm and Radial Basis Function with a Lookup Table

Robotica ◽  
1998 ◽  
Vol 16 (4) ◽  
pp. 433-444 ◽  
Author(s):  
A. S. Morris ◽  
M. A. Mansor
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Inverse Kinematics ◽  
Back Propagation ◽  
Lookup Table ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
The Neural Network ◽  
Radial Basis

This is an extension of previous work which used an artificial neural network with a back-propagation algorithm and a lookup table to find the inverse kinematics for a manipulator arm moving along pre-defined trajectories. The work now described shows that the performance of this technique can be improved if the back-propagation is made to be adaptive. Also, further improvement is obtained by using the whole workspace to train the neural network rather than just a pre-defined path. For the inverse kinematics of the whole workspace, a comparison has also been done between the adaptive back-propagation algorithm and radial basis function.

Neural Network-Based Speed Control of A Two-Mass-Model System

1999 ◽  
Vol 3 (5) ◽  
pp. 427-430 ◽  
Author(s):  
Rached Dhaouadi ◽  
◽  
Khaled Nouri
Keyword(s):  
Neural Network ◽  
Inverse Dynamics ◽  
Mass Model ◽  
Neural Network Learning ◽  
Inverse Control ◽  
Network Learning ◽  
The Neural Network ◽  
Control Scheme ◽  
On Line ◽  
Neural Network Structure

We present an application of artificial neural networks to the problem of controlling the speed of an elastic drive system. We derive a neural network structure to simulate the inverse dynamics of the system, then implement the direct inverse control scheme in a closed loop. The neural network learning is done on-line to adaptively control the speed to follow a stepwise changing reference. The experimental results with a two-mass-model analog board confirm the effectiveness of the proposed neurocontrol scheme.

Obstacle avoidance inverse kinematics solution of redundant robots by neural networks

Robotica ◽  
1997 ◽  
Vol 15 (1) ◽  
pp. 3-10 ◽  
Author(s):  
Ziqiang Mao ◽  
T. C. Hsia
Keyword(s):  
Neural Network ◽  
Obstacle Avoidance ◽  
Inverse Kinematics ◽  
Robot Manipulator ◽  
Solution Technique ◽  
Training Algorithms ◽  
Neural Network Approach ◽  
Work Space ◽  
The Neural Network ◽  
Free Case

This paper investigates the neural network approach to solve the inverse kinematics problem of redundant robot manipulators in an environment with obstacles. The solution technique proposed requires only the knowledge of the robot forward kinematics functions and the neural network is trained in the inverse modeling manner. Training algorithms for both the obstacle free case and the obstacle avoidance case are developed. For the obstacle free case, sample points can be selected in the work space as training patterns for the neural network. For the obstacle avoidance case, the training algorithm is augmented with a distance penalty function. A ball-covering object modeling technique is employed to calculate the distances between the robot links and the objects in the work space. It is shown that this technique is very computationally efficient. Extensive simulation results are presented to illustrate the success of the proposed solution schemes. Experimental results performed on a PUMA 560 robot manipulator is also presented.

scholarly journals Solution for Ill-Posed Inverse Kinematics of Robot Arm by Network Inversion

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Takehiko Ogawa ◽  
Hajime Kanada
Keyword(s):  
Neural Network ◽  
Inverse Problems ◽  
Inverse Kinematics ◽  
Robot Arm ◽  
Joint Angles ◽  
End Effector ◽  
Inverse Kinematics Problem ◽  
Ill Posed ◽  
The Given

In the context of controlling a robot arm with multiple joints, the method of estimating the joint angles from the given end-effector coordinates is called inverse kinematics, which is a type of inverse problems. Network inversion has been proposed as a method for solving inverse problems by using a multilayer neural network. In this paper, network inversion is introduced as a method to solve the inverse kinematics problem of a robot arm with multiple joints, where the joint angles are estimated from the given end-effector coordinates. In general, inverse problems are affected by ill-posedness, which implies that the existence, uniqueness, and stability of their solutions are not guaranteed. In this paper, we show the effectiveness of applying network inversion with regularization, by which ill-posedness can be reduced, to the ill-posed inverse kinematics of an actual robot arm with multiple joints.

Sign in / Sign up

Export Citation Format

Share Document