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.

scholarly journals Experimental implementation of artificial neural network-based active vibration control & chatter suppression

2021 ◽  
Author(s):  
Yong Xia
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Control System ◽  
Vibration Control ◽  
Control Strategies ◽  
Active Vibration Control ◽  
Chatter Suppression ◽  
Active Vibration ◽  
Artificial Neural

Vibration control strategies strive to reduce the effect of harmful vibrations such as machining chatter. In general, these strategies are classified as passive or active. While passive vibration control techniques are generally less complex, there is a limit to their effectiveness. Active vibration control strategies, which work by providing an additional energy supply to vibration systems, on the other hand, require more complex algorithms but can be very effective. In this work, a novel artificial neural network-based active vibration control system has been developed. The developed system can detect the sinusoidal vibration component with the highest power and suppress it in one control cycle, and in subsequent cycles, sinusoidal signals with the next highest power will be suppressed. With artificial neural networks trained to cover enough frequency and amplitude ranges, most of the original vibration can be suppressed. The efficiency of the proposed methodology has been verified experimentally in the vibration control of a cantilever beam. Artificial neural networks can be trained automatically for updated time delays in the system when necessary. Experimental results show that the developed active vibration control system is real time, adaptable, robust, effective and easy to be implemented. Finally, an experimental setup of chatter suppression for a lathe has been successfully implemented, and the successful techniques used in the previous artificial neural network-based active vibration control system have been utilized for active chatter suppression in turning.

Operator-Based Vibration Control System Design of a Flexible Arm Using an SMA Actuator with Hysteresis

2012 ◽  
Vol 5 (2) ◽  
pp. 115-123 ◽  
Author(s):  
Seiji SAITO ◽  
Mingcong DENG ◽  
Mamoru MINAMI ◽  
Changan JIANG ◽  
Akira YANOU
Keyword(s):  
Control System ◽  
Vibration Control ◽  
System Design ◽  
Control System Design ◽  
Flexible Arm ◽  
Sma Actuator

scholarly journals Discrete Globalised Dual Heuristic Dynamic Programming in Control of the Two-Wheeled Mobile Robot

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Marcin Szuster ◽  
Zenon Hendzel
Keyword(s):  
Neural Networks ◽  
Dynamic Programming ◽  
Control System ◽  
Mobile Robot ◽  
Tracking Control ◽  
Dynamic Programming Algorithm ◽  
Wheeled Mobile Robot ◽  
Programming Algorithm ◽  
The Neural Networks ◽  
Heuristic Dynamic Programming

Network-based control systems have been emerging technologies in the control of nonlinear systems over the past few years. This paper focuses on the implementation of the approximate dynamic programming algorithm in the network-based tracking control system of the two-wheeled mobile robot, Pioneer 2-DX. The proposed discrete tracking control system consists of the globalised dual heuristic dynamic programming algorithm, the PD controller, the supervisory term, and an additional control signal. The structure of the supervisory term derives from the stability analysis realised using the Lyapunov stability theorem. The globalised dual heuristic dynamic programming algorithm consists of two structures: the actor and the critic, realised in a form of neural networks. The actor generates the suboptimal control law, while the critic evaluates the realised control strategy by approximation of value function from the Bellman’s equation. The presented discrete tracking control system works online, the neural networks’ weights adaptation process is realised in every iteration step, and the neural networks preliminary learning procedure is not required. The performance of the proposed control system was verified by a series of computer simulations and experiments realised using the wheeled mobile robot Pioneer 2-DX.

A BIOLOGICALLY INSPIRED NEURAL NETWORK FOR DYNAMIC PROGRAMMING

2001 ◽  
Vol 11 (06) ◽  
pp. 561-572 ◽  
Author(s):  
ROSELI A. FRANCELIN ROMERO ◽  
JANUSZ KACPRYZK ◽  
FERNANDO GOMIDE
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Dynamic Programming ◽  
Optimization Problems ◽  
Direct Method ◽  
Fuzzy Decision Making ◽  
Fuzzy Decision ◽  
Biologically Inspired ◽  
The Neural Network ◽  
The Neural Networks

An artificial neural network with a two-layer feedback topology and generalized recurrent neurons, for solving nonlinear discrete dynamic optimization problems, is developed. A direct method to assign the weights of neural networks is presented. The method is based on Bellmann's Optimality Principle and on the interchange of information which occurs during the synaptic chemical processing among neurons. The neural network based algorithm is an advantageous approach for dynamic programming due to the inherent parallelism of the neural networks; further it reduces the severity of computational problems that can occur in methods like conventional methods. Some illustrative application examples are presented to show how this approach works out including the shortest path and fuzzy decision making problems.

scholarly journals NEURAL NETWORKS IN THE MANAGEMENT OF COMPLEX TECHNICAL SYSTEMS

2019 ◽  
Vol 40 (2) ◽  
pp. 68-72
Author(s):  
R.L Panteev
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Neural Control ◽  
Advantages And Disadvantages ◽  
The Neural Networks ◽  
Complex Technical Systems ◽  
Technical Systems ◽  
Dynamic Objects ◽  
Methods Of Application

The methods of application of neural networks for solving the problems of control of dynamic objects are considered. For each type of neural control, the circuits for connecting the neural networks within the control system are presented and the procedures for their training are described in detail. The advantages and disadvantages of the described methods are analyzed.

Robust Control of a Flexible Arm

1994 ◽  
Vol 6 (3) ◽  
pp. 214-219
Author(s):  
Kang-Zhi Liu ◽  
◽  
Koji Higaki ◽  
Tsutomu Mita
Keyword(s):  
Robust Control ◽  
Control Theory ◽  
Vibration Control ◽  
Feedback Controller ◽  
Degree Of Freedom ◽  
Matching Method ◽  
Robust Controller ◽  
Flexible Arm ◽  
Feedforward Controller ◽  
Two Degree Of Freedom

This paper addresses the vibration control of a flexible arm with a two-degree-of-freedom robust controller. The feedback controller is designed by <I>H</I>∞ control theory to achieve robust distrubance attenuation, and the feedforward controller is designed by a signal-matching method to improve the trasient response. An experiment verifies that this methodology is effective.

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