2 edition of **Performance analysis of neural networks applied to robot trajectory following systems.** found in the catalog.

Performance analysis of neural networks applied to robot trajectory following systems.

Mingwei Wang

- 109 Want to read
- 4 Currently reading

Published
**2001**
.

Written in English

The Physical Object | |
---|---|

Pagination | 114 leaves. |

Number of Pages | 114 |

ID Numbers | |

Open Library | OL20032210M |

ISBN 10 | 0612590178 |

Neural Network Application in Robotics “Development of Autonomous Aero-Robot and its Applications to Safety and Disaster Prevention with the help of neural network” Sharique Hayat1, R. N. Mall2 1. Final Year CIM, MMMEC Gorakhpur. The structure of neural network controller is shown in Figure 2. The detail mathematical description of the neural network is given by 2 n V f (Wq) (5) where T n n n R 2 q [1 2 1 2, T ] (6) -Figure 1. Black diagram of a robot control system with a linear controller and neural network controller. 2 + Linear Robot Controller Neural Network.

Bagheri A, Karimi T and Amanifard N () Tracking performance control of a cable communicated underwater vehicle using adaptive neural network controllers, Applied Soft Computing, , (), Online publication date: 1-Jun rst robot to demonstrate insect-scale ight, as well as the most capable ying robotic insect to date. Controlled hover, trajectory-following, and perching have been accomplished by means of onboard sensors and actuators fabricated with the robot using a pop-up book MEMS process based on smart composite microstructures.

In the following, we provide a literature review on application of neural networks for robot motion control. Selmic and Lewis [2] proposed a dynamical inversion compensation scheme by using a backstepping technique with neural networks, which was applied to mechanical systems. With the advancement in research on parallel robots, control theory is increasingly applied in the field of robotics. Owing to its robustness, sliding mode variable structure control is extensively.

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This paper presents the application of adaptive neural networks to robot manipulator control. The main methodologies, on which the approach is based, are recurrent neural networks.

use of neural networks in control systems has increased in recent years, since, as they do not require any detailed Saeed Pezeshki, Sajad Badalkhani and Ali Javadi: 1 Performance Analysis of a Neuro-PID Controller Applied to a Robot Manipulator ARTICLE Int J Adv Robotic Sy,Vol.

9, Neural networks, which feature high-speed parallel distributed processing, and can be readily implemented by hardware, have been recognized as a powerful tool for real-time processing and successfully applied widely in various control systems. Particularly, using neural networks for the control of robot manipulators have attracted much Cited by: 56 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL.

12, NO. 1, JANUARY Fig. A multilayer NN. joint angles. The following properties of the robot dynamics are. This work presents a novel fuzzy adaptive sliding mode-based feedback linearization controller for trajectory tracking of a flexible robot manipulator. To reach this goal, after deriving the dynamical equations of the robot, the feedback linearization approach is utilized to change the nonlinear dynamics to a linear one and find the control law.

Then, the sliding mode control strategy is Author: M. Mahmoodabadi, N. Nejadkourki. neural Network, IEEE Neural Networks, Vol. 1, pp. [17] Efe MO, Kaynak O, Yu X. Sliding mode control of three degrees of freedom anthropoid robot by driving the controller parameters to an equivalent regime.

Journal of Dynamic Systems, Measurement and Control (ASME) ; – Neural Networks in Robotics is the first book to present an integrated view of both the application of artificial neural networks to robot control and the neuromuscular models from which robots were created.

The behavior of biological systems provides both the inspiration and the challenge for robotics. The goal is to build robots which can emulate the ability of living. using neural networks for robot control is based on the following properties of neural net- works: (i) The ability of the neural network to "learn" (through a repetitive training process) (McClelland & Rumelhart, ) enables a controller incorporated with a neural network to improve its performance.

set of desired/input trajectories is used to train a neural network off-line (Neural Network I, NN-I, in Fig. 1) to enable generalization beyond the training set. For safety and speed, the ILC is performed in a dynamical simulator of the robot. However, when the NN feedforward control is applied to the physical robot, the tracking performance.

This paper presents a novel trajectory planning method for a flexible Cartesian robot manipulator in a point-to-point motion. In order to obtain an exact mathematical model, the parameters of the equation of motion are determined from an identification experiment.

Wang, J., et al.: Trajectory tracking control based on adaptive neural dynamics for four-wheel drive omnidirectional mobile robots.

Engineering Review: Međunarodni časopis namijenjen publiciranju originalnih istraživanja s aspekta analize konstrukcija, materijala i novih tehnologija u području strojarstva, brodogradnje, temeljnih tehničkih znanosti, elektrotehnike. This study proposes an adaptive fuzzy neural network (AFNN) based on a multi-strategy artificial bee colony (MSABC) algorithm for achieving an actual mobile robot navigation control.

During the navigation control process, the AFNN inputs are the distance between the ultrasonic sensors and the angle between the mobile robot and the target, and the AFNN outputs are the robot.

As examples, the proposed neural network is applied to trajectory formation for a mobile robot in solving maze-type problems, dynamically tracking moving target, and avoiding varying obstacles.

The efficiency of the proposed approach is demonstrated through simulation and comparison studies. The trajectory tracking errors obtained in these three cases are shown in Fig. Similar to the various initial value selection of the weight matrices W ^ (0), the initial value selection of the basis function does not differ significantly in controller a result, compared with the fuzzy logic and other methods, the domain selection in the process of constructing the.

robot with unknown dynamics, and examples include dy-namics approximation by wavelet networks [9], Gaussian Processes [10], Locally Weighted Projection Regression (LWPR), fuzzy logic systems [11] and neural networks [12].

NNs are used to either approximate the robot forward dynam-ics [13] or inverse dynamics [14] for the controller design.

The proposed method also includes a new adaptive neural network control scheme where the objective for the robot end effector can be specified as a dynamic region, instead of the desired position or trajectory.

The stability of the closed-loop system is analyzed using Lyapunov-like analysis. To achieve precise trajectory tracking of robotic manipulators in complex environment, the precise dynamic model, parameters identification, nonlinear characteristics, and disturbances are the factors that should be solved.

Although parameters identification and adaptive estimate method were proposed for robotic control in many literature studies, the essential factors, such. 2 days ago In the following Materials and Methods section an overview on the dataset is given, followed by a brief description of feature selection, LSTM neural networks and the data analysis performed.

In the Results section, an overview of the contributions of single components to the principal component analysis is given, as is the prediction accuracy.

A branch of machine learning, neural networks (NN), also known as artificial neural networks (ANN), are computational models — essentially algorithms. Neural networks have a unique ability to extract meaning from imprecise or complex data to find patterns and detect trends that are too convoluted for the human brain or for other computer techniques.

which reduces wear and tear on the robot [4], but this is at the expense of large oscillations of the trajectory. Trigonometric splines can be used to provide a less oscillatory interpolating curve [33].

Neural networks applied to robot path planning Neural networks have been applied to many fields of engineering, and the field of robotics is. A neural network is a network or circuit of neurons, or in a modern sense, an artificial neural network, composed of artificial neurons or nodes.

Thus a neural network is either a biological neural network, made up of real biological neurons, or an artificial neural network, for solving artificial intelligence (AI) problems. The connections of the biological neuron are modeled as .now are doing rigorous analysis of NN controllers using a variety of techniques [5], [14]–[18].

In [14] a multilayer NN controller with guaranteed performance has been developed and successfully applied to control of rigid robot manipulators, ﬂexible-link robotic systems and position/force control. In this.The neural network-based control of mobile robots has recently been the subject of intense research (Corradini et al., ).

It is usual to work with kinematic models of mobile robot to obtain stable motion control laws for trajectory following or goal reaching (Jiang, ; Ramírez & Zeghloul, ).