scholarly journals Self-Tuning Vibration Control of a Rotational Flexible Timoshenko Arm Using Neural Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Minoru Sasaki ◽  
Toshimi Shimizu ◽  
Yoshihiro Inoue ◽  
Wayne J. Book
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Vibration Control ◽  
Direct Method ◽  
Rate Of Change ◽  
Feedback Controller ◽  
Flexible System ◽  
Flexible Arm ◽  
Self Tuning ◽  
The Neural Networks

A self-tuning vibration control of a rotational flexible arm using neural networks is presented. To the self-tuning control system, the control scheme consists of gain tuning neural networks and a variable-gain feedback controller. The neural networks are trained so as to make the root moment zero. In the process, the neural networks learn the optimal gain of the feedback controller. The feedback controller is designed based on Lyapunov's direct method. The feedback control of the vibration of the flexible system is derived by considering the time rate of change of the total energy of the system. This approach has the advantage over the conventional methods in the respect that it allows one to deal directly with the system's partial differential equations without resorting to approximations. Numerical and experimental results for the vibration control of a rotational flexible arm are discussed. It verifies that the proposed control system is effective at controlling flexible dynamical systems.

Design and implementation of a self-tuning control using artificial neural networks of second order

2006 ◽  
Author(s):  
Mario Ibarra-manzano ◽  
Dora Almanza-ojeda ◽  
Andres Hernandez-Gutierrez ◽  
Juan Amezquita-sanchez ◽  
Luis Lopez-martinez
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Second Order ◽  
Design And Implementation ◽  
Self Tuning ◽  
Artificial Neural

scholarly journals Neural networks on-line optimized PID controller with wind gust rejection for a quad-rotor

2021 ◽  
Author(s):  
Chiraz Ben Jabeur ◽  
Hassene Seddik
Keyword(s):  
Neural Networks ◽  
Experimental Results ◽  
Wind Gust ◽  
Pd Controller ◽  
Hostile Environment ◽  
Modeling And Control ◽  
Self Tuning ◽  
On Line ◽  
Hostile Environments ◽  
And Control

Abstract In this paper a complete methodology of modeling and control of quad-rotor aircraft is exposed. In fact, a PD on-line optimized Neural Networks Approach (PD-NN) is developed and applied to control the attitude of a quad-rotor that is evolving in hostile environment with wind gust disturbances and should maintain its position despite of these troubles. Whereas PD classical controllers are dedicated for the positions, altitude and speed control. The main objective of this work is to develop a smart Self-Tuning PD controller for attitude angles control, based on neural networks capable of controlling the quad-rotor for an optimized performance thus following a desired trajectory. Many problems could arise if the quad-rotor is evolving in hostile environments presenting irregular troubles such as wind gusts modeled and applied to the overall system. The quad-rotor has to rapidly achieve tasks while guaranteeing stability and precision and must behave quickly with regards to decision making fronting turbulences. This technique offers some advantages over conventional control methods such as PD controllers. Simulation results are achieved with the use of Matlab/Simulink environment and are established on a comparative study between PD and PD-NN controllers founded on wind disturbances application. These obstacles are applied with numerous degrees of strength to test the quad-rotor comportment. Experimental results are reached with the use of the V-REP environment with which some trajectories are tracked and then applied on a BLADE Inductrix FPV+. These simulations and experimental results are acceptable and have confirmed the efficiency of the proposed PD-NN approach. In fact, this controller has fairly smaller errors than the PD controller and has an improved ability to reject troubles. Moreover, it has confirmed to be extremely vigorous and efficient fronting disturbances in the form of wind disturbances.

scholarly journals Neural Networks for Self-tuning Control Systems

10.14311/514 ◽  
2004 ◽  
Vol 44 (1) ◽  
Author(s):  
A. Noriega Ponce ◽  
A. Aguado Behar ◽  
A. Ordaz Hernández ◽  
V. Rauch Sitar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Control Systems ◽  
Control Algorithm ◽  
Set Point ◽  
Tuning Mechanism ◽  
Self Tuning ◽  
The Moment ◽  
Weighting Coefficients ◽  
Previous Training

In this paper, we presented a self-tuning control algorithm based on a three layers perceptron type neural network. The proposed algorithm is advantageous in the sense that practically a previous training of the net is not required and some changes in the set-point are generally enough to adjust the learning coefficient. Optionally, it is possible to introduce a self-tuning mechanism of the learning coefficient although by the moment it is not possible to give final conclusions about this possibility. The proposed algorithm has the special feature that the regulation error instead of the net output error is retropropagated for the weighting coefficients modifications. 

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