scholarly journals Adaptive Gain Scheduled Semiactive Vibration Control Using a Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Kazuhiko Hiramoto ◽  
Taichi Matsuoka ◽  
Katsuaki Sunakoda
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Performance ◽  
Control Method ◽  
Gain Scheduling ◽  
Semiactive Control ◽  
Control Performance ◽  
Control Laws ◽  
Artificial Neural ◽  
Gain Scheduled

We propose an adaptive gain scheduled semiactive control method using an artificial neural network for structural systems subject to earthquake disturbance. In order to design a semiactive control system with high control performance against earthquakes with different time and/or frequency properties, multiple semiactive control laws with high performance for each of multiple earthquake disturbances are scheduled with an adaptive manner. Each semiactive control law to be scheduled is designed based on the output emulation approach that has been proposed by the authors. As the adaptive gain scheduling mechanism, we introduce an artificial neural network (ANN). Input signals of the ANN are the measured earthquake disturbance itself, for example, the acceleration, velocity, and displacement. The output of the ANN is the parameter for the scheduling of multiple semiactive control laws each of which has been optimized for a single disturbance. Parameters such as weight and bias in the ANN are optimized by the genetic algorithm (GA). The proposed design method is applied to semiactive control design of a base-isolated building with a semiactive damper. With simulation study, the proposed adaptive gain scheduling method realizes control performance exceeding single semiactive control optimizing the average of the control performance subject to various earthquake disturbances.

scholarly journals Switched time delay control based on artificial neural network for fault detection and compensation in robot manipulators

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Dihya Maincer ◽  
Moufid Mansour ◽  
Amar Hamache ◽  
Chemseddine Boudjedir ◽  
Moussaab Bounabi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Time Delay ◽  
Control Method ◽  
High Gain ◽  
Industrial Applications ◽  
Control Laws ◽  
Time Delay Control ◽  
Delay Control ◽  
Artificial Neural

AbstractThis work proposes a switched time delay control scheme based on neural networks for robots subjected to sensors faults. In this scheme, a multilayer perceptron (MLP) artificial neural network (ANN) is introduced to reproduce the same behavior of a robot in the case of no faults. The reproduction characteristic of the MLPs allows instant detection of any important sensor faults. In order to compensate the effects of these faults on the robot’s behavior, a time delay control (TDC) procedure is presented. The proposed controller is composed of two control laws: The first one contains a small gain applied to the faultless robot, while the second scheme uses a high gain that is applied to the robot subjected to faults. The control method applied to the system is decided based on the ANN detection results which switches from the first control law to the second one in the case where an important fault is detected. Simulations are performed on a SCARA arm manipulator to illustrate the feasibility and effectiveness of the proposed controller. The results demonstrate that the free-model aspect of the proposed controller makes it highly suitable for industrial applications.

scholarly journals JUSTIFICATION OF PARAMETERS OF THE CONTROL SYSTEM OF ELECTROMECHANICAL DEBALANCE VIBRODRIVE OF VIBRATION MACHINES ON THE BASIS OF ARTIFICIAL NEURAL NETWORK

2021 ◽  
pp. 52-63
Author(s):  
Leonid Yaroshenko ◽  
Roman Chubyk ◽  
Iryna Derevenko
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Mechanical System ◽  
Predictive Model ◽  
Control Method ◽  
Resonant Mode ◽  
Neural System ◽  
Artificial Neural ◽  
Vibration Parameters

The article analyzes and proposes an approach to the construction of a control system for electromechanical debalance vibrodrive for vibration machines based on an artificial neural network. As a result of the analysis of various methods of managing dynamic objects it is concluded that the most appropriate and perfect for this type of machine is neurocontrol method of predictive model neurocontrol, which allows to expand the functionality of these vibrating machines and significantly save energy for vibratory drive of their oscillations. A direct neuro-emulator is used to predict the future behavior of the oscillating mechanical system of the vibration technological machines and to calculate errors. An important feature of the predictive neurocontrol model in the proposed method of controlling the operation of vibrating technological machines using an artificial neural system is that there is no neurocontroller that needs to be trained, its place is taken by the optimization algorithm. Applying the proposed method of controlling operation of adaptive vibration technology machines using artificial neural network will optimize the electromechanical control of debalanced vibration drive of vibrating machines and provide optimal resonant modes of its operation (which is energy efficient) in all technological modes of vibrating operation. The technical and economic characteristics of this control method are further improved due to the fact that the proposed control method uses the technology of predictive model neurocontrol and as a result is constantly calculated (forecasted) several cycles in advance and determines the best strategy to control the frequency of forced cyclic vibration. As a result, the mechanical system of vibration machines spends less time in non-resonant mode. This method of control also minimizes the duration of transients when changing the load mass of the working body vibration or changing the mode of vibration parameters and the parameters of their technological process.

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