Vibration Control With Neural Network Dynamic Compensator

1995 ◽  
Author(s):  
Toshinari Shiotsuka ◽  
Kazuo Yoshida ◽  
Akio Nagamatsu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Control Object ◽  
Time History ◽  
Network Dynamic ◽  
Control Input ◽  
Object A ◽  
Neural Network Controller ◽  
Dynamic Compensator ◽  
Suspension Model

Abstract An approach is presented on designing the dynamic compensator-type controller, using two kinds of neural networks. One is used for identification of system dynamic characteristics of the control object. A time history of response under sine-sweep input is used as the teaching signal of this neural network. The other is used as the neural network controller. The control input is determined with this neural network in order that a performance index concerning the state variable and the input force takes the minimum value. These two neural networks are combined reciprocally in a cascade type in designing the controller. Validity and usefulness of the presented approach are verified by both an computer simulation and an experiment with an active suspension model.

STRUCTURE OF THE DECISION SUPPORT INFORMATION SYSTEM BY THE NPP OPERATOR

2022 ◽  
pp. 8-12
Author(s):  
М.Е. Ушков ◽  
В.Л. Бурковский
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Support System ◽  
Control Object ◽  
Information Support ◽  
Quality Analysis ◽  
Inference System ◽  
Artificial Neural ◽  
Information Support System

Рассматривается структура системы информационной поддержки процессов принятия решений оператором АЭС в оперативных условиях. Анализируются функциональные возможности системы информационной поддержки оператора (СИПО) на примере Нововоронежской атомной электростанции (НВ АЭС). Данная система дает возможность оператору, управляющему распределенным комплексом технологических объектов АЭС, проводить качественный анализ и обработку больших объемов сложностpуктурированной информации и принимать своевременные адекватные решения в темпе реального времени. Кроме того, рассматривается объект управления и его структура, приводятся рекомендации, направленные на увеличение функциональных возможностей СИПО на базе искусственных нейронных сетей. Одной из многочисленных функций СИПО является прогнозирование состояния объекта управления на основе реализации программно-технологического комплекса модели энергоблока (ПТК МЭ). Однако существующая модель не способна учесть все факторы, влияющие на производственный процесс. Альтернативой здесь выступает искусственная нейронная сеть, которая в процессе обучения может сформировать искомые зависимости между большим числом параметров объекта управления и получить более полный и достоверный прогноз. Предложена структура искусственной нейронной сети на базе нечёткой системы вывода, которая реализует возможности нейронных сетей и нечеткой логики We considered the structure of the information support system for decision-making by the NPP operator in operational conditions. We analyzed the functional capabilities of the operator information support system (SIPO) using the example of the Novovoronezh nuclear power plant (NV NPP). This system provides the operator managing the distributed complex of NPP technological facilities to carry out high-quality analysis and processing of large volumes of complex structured information and make timely adequate decisions in real time. In addition, we considered the control object and its structure and made recommendations aimed at increasing the functionality of the SIPO based on artificial neural networks. One of the many functions of the SIPO is to predict the state of the control object based on the implementation of the software and technological complex of the power unit model. However, the existing model is not able to take into account all the factors influencing the production process. An alternative here is an artificial neural network, which in the learning process can form the required dependencies between a large number of parameters of the control object and get a more complete and reliable forecast. The proposed structure of an artificial neural network based on a fuzzy inference system, which implements the capabilities of neural networks and fuzzy logic

scholarly journals Active Shock Absorber Control Based on Time-Delay Neural Network

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1091
Author(s):  
Alexander Alyukov ◽  
Yuri Rozhdestvenskiy ◽  
Sergei Aliukov
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Shock Absorber ◽  
Learning Ability ◽  
Control Input ◽  
Low Level ◽  
External Data ◽  
Good Learning ◽  
Suspension Model ◽  
High Level

A controlled suspension usually consists of a high-level and a low-level controller. The purpose the high-level controller is to analyze external data on vehicle conditions and make decisions on the required value of the force on the shock absorber rod, while the purpose of the low-level controller is to ensure the implementation of the desired force value by controlling the actuators. Many works have focused on the design of high-level controllers of active suspensions, in which it is considered that the shock absorber can instantly and absolutely accurately implement a given control input. However, active shock absorbers are complex systems that have hysteresis. In addition, electro-pneumatic and hydraulic elements are often used in the design, which have a long response time and often low accuracy. The application of methods of control theory in such systems is often difficult due to the complexity of constructing their mathematical models. In this article, the authors propose an effective low-level controller for an active shock absorber based on a time-delay neural network. Neural networks in this case show good learning ability. The low-level controller is implemented in a simplified suspension model and the simulation results are presented for a number of typical cases.

An investigation into the use of neural networks for the semi-active control of a magnetorheologically damped vehicle suspension

2010 ◽  
Vol 224 (7) ◽  
pp. 829-848 ◽  
Author(s):  
H Metered ◽  
P Bonello ◽  
S O Oyadiji
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Inverse Dynamics ◽  
Ride Comfort ◽  
Mr Damper ◽  
Performance Criteria ◽  
Vehicle Suspension ◽  
Neural Network Controller ◽  
Road Disturbance ◽  
Continuous State

Neural networks are highly useful for the modelling and control of magnetorheological (MR) dampers. A damper controller based on a recurrent neural network (RNN) of the inverse dynamics of an MR damper potentially offers significant advantages over conventional controllers in terms of reliability and cost through the minimal use of sensors. This paper introduces a neural-network-based MR damper controller for use in conjunction with the system controller of a semi-active vehicle suspension. A mathematical model of a semi-active quarter-vehicle suspension using an MR damper is derived. Control performance criteria are evaluated in the time and frequency domains in order to quantify the suspension effectiveness under bump and random road disturbance. Studies using the modified Bouc—Wen model for the MR damper as well as an actual damper fitted in a hardware-in-the-loop simulation (HILS) both showed that the inverse RNN damper controller potentially offers a significantly superior ride comfort and vehicle stability over a conventional MR damper controller based on continuous-state control. The neural network controller produces a smoother and lower input voltage to the MR damper coil, ensuring extended damper life and lower power requirement respectively. Further studies performed using an RNN model of the forward dynamics of the MR damper showed that it is a reliable substitute for HILS for validating multi-damper control applications.

scholarly journals Neural Network Controller Application on a Visual based Object Tracking and Following Robot

2019 ◽  
Vol 8 (1) ◽  
pp. 31-40
Author(s):  
Pola Risma ◽  
Tresna Dewi ◽  
Yurni Oktarina ◽  
Yudi Wijanarko
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Object Tracking ◽  
Effective Control ◽  
Computational Time ◽  
Control Input ◽  
Distance Information ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller

Navigation is the main issue for autonomous mobile robot due to its mobility in an unstructured environment. The autonomous object tracking and following robot has been applied in many places such as transport robot in industry and hospital, and as an entertainment robot. This kind of image processing based navigation requires more resources for computational time, however microcontroller currently applied to a robot has limited memory. Therefore, effective image processing from a vision sensor and obstacle avoidances from distance sensors need to be processed efficiently. The application of neural network can be an alternative to get a faster trajectory generation. This paper proposes a simple image processing and combines image processing result with distance information to the obstacles from distance sensors. The combination is conducted by the neural network to get the effective control input for robot motion in navigating through its assigned environment. The robot is deployed in three different environmental setting to show the effectiveness of the proposed method. The experimental results show that the robot can navigate itself effectively within reasonable time periods.

Neural hybrid control of manipulators, stability analysis

Robotica ◽  
2001 ◽  
Vol 19 (1) ◽  
pp. 41-51 ◽  
Author(s):  
N. Saadia ◽  
Y. Amirat ◽  
J. Pontnau ◽  
N.K. M'Sirdi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Adaptive Control ◽  
Stability Analysis ◽  
Position Control ◽  
Hybrid Control ◽  
Control Structures ◽  
Neural Network Controller ◽  
Artificial Neural ◽  
The Stability

The design and implementation of adaptive control for nonlinear unknown systems is extremely difficult. The nonlinear adaptive control for assembly robots performing a peg-in-hole insertion is one such an example. The recently intensively studied neural networks brings a new stage in the development of adaptive control, particularly for unknown nonlinear systems. The aim of this paper is to propose a new approach of hybrid force position control of an assembly robot based on artificial neural networks systems. An appropriate neural network is used to model the plant and is updated online. An artificial neural network controller is then directly evaluated using the updated neuro model. Two control structures are proposed and the stability analysis of the closed-loop system is investigated using the Lyapunov method. Experimental results demonstrate that the identification and control schemes suggested in this paper are efficient in practice.

scholarly journals Real-Time Optimal Approach and Capture of ENVISAT Based on Neural Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Hongjue Li ◽  
Yunfeng Dong ◽  
Peiyun Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Optimal Control ◽  
Real Time ◽  
Rbf Neural Networks ◽  
Optimal Control Strategy ◽  
Neural Network Controller ◽  
Training Samples ◽  
Time Optimal ◽  
Optimal Approach

A neural network-based controller is developed to enable a chaser spacecraft to approach and capture a disabled Environmental Satellite (ENVISAT). This task is conventionally tackled by framing it as an optimal control problem. However, the optimization of such a problem is computationally expensive and not suitable for onboard implementation. In this work, a learning-based approach is used to rapidly generate the control outputs of the controller based on a series of training samples. These training samples are generated by solving multiple optimal control problems with successive iterations. Then, Radial Basis Function (RBF) neural networks are designed to mimic this optimal control strategy from the generated data. Compared with a traditional controller, the neural network controller is able to generate real-time high-quality control policies by simply passing the input through the feedforward neural network.

Optimum Design of Structures for Earthquake Loading by a Cellular Evolutionary Algorithm and Neural Networks

2012 ◽  
pp. 306-322 ◽  
Author(s):  
Saeed Gholizadeh
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Optimum Design ◽  
Optimization Algorithm ◽  
Time History ◽  
Network System ◽  
Hybrid Neural Network ◽  
Neural Network System ◽  
Nonlinear Time History

The present chapter deals with optimum design of structures for earthquake induced loads by taking into account nonlinear time history structural response. As the structural seismic optimization is a time consuming and computationally intensive task, in this chapter, a methodology is proposed to reduce the computational burden. The proposed methodology consists of an efficient optimization algorithm and a hybrid neural network system to effectively predict the nonlinear time history responses of structures. The employed optimization algorithm is a modified cellular genetic algorithm which reduces the required generation numbers compared with the standard genetic algorithm. Also, the hybrid neural network system is a combination of probabilistic and generalized regression neural networks. Numerical results demonstrate the computational merits of the proposed methodology for seismic design optimization of structures.

Research on the Speed of Diesel Engine Based on Improved BP Neural Network Controller

2013 ◽  
Vol 281 ◽  
pp. 105-111 ◽  
Author(s):  
Yong Shi ◽  
Lian Yu Zhang ◽  
Jun Sun ◽  
Hong Guang Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Diesel Engine ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Adaptive Controller ◽  
Regulation System ◽  
Marine Diesel Engine ◽  
Neural Network Controller ◽  
Marine Diesel

Marine diesel engine is of characteristics of non-linear and time-invariant, so it is difficult to be controlled with traditional PID controller. An adaptive controller based on back-propagation (BP) neural networks was put forwarded for marine diesel engine speed control system, where two neural networks are proposed to control the position loop and speed loop. The adaptive controller was improved was improved via introducing relative error in target evaluation function of the BP neural network, and obtain sensitivity function of diesel engine output with respect to its input using a differential equation. The controller has self-learning and adaptive capacity. It can also optimize the PID controller parameters online. The controller was experimentally evaluated on rack position actuator of marine diesel engine simulated based on a diesel hardware-in-loop system of dSPACE. Finally, tests on a diesel engine demonstrated that the controller can satisfy the transient and steady demands of speed regulation system.

On-line control for optimal ignition timing using the pseudolinear radial basis function and the local linear model tree

2005 ◽  
Vol 219 (2) ◽  
pp. 227-240 ◽  
Author(s):  
M Beham ◽  
D L Yu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Radial Basis Function ◽  
Linear Model ◽  
Basis Function ◽  
Ignition Timing ◽  
Neural Network Controller ◽  
Look Up Table ◽  
Local Linear ◽  
Network Controller

A new generation of engines demands new control strategies. The increased number of control variables of variable valve timing engines results in complexity of conventional control structures. This necessitates the integration of new technologies for optimal control of the ignition timing. This paper presents a neural network controller for ignition timing that uses two recently proposed new neural network structures—a pseudolinear radial basis function (PLRBF) network and a local linear model tree (LOLIMOT) network. Tests showed that the relative load signal is not necessary to evaluate the ignition angle, and therefore no air mass meter is necessary. The two neural networks are compared with a conventional look-up table control structure. The network controller improves the conventional look-up table method for calibration by comparison with bilateral look-up tables. The neural controller is implemented and tested in a research car. Experimental results show that the neural networks are very effective in mapping non-linearity. The design of the neural network controller simplifies the structure drastically.

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