scholarly journals Dynamic model identification of IPMC actuator using fuzzy NARX model optimized by MPSO

2014 ◽  
Vol 17 (1) ◽  
pp. 62-80
Author(s):  
Anh Pham Huy Ho ◽  
Nam Thanh Nguyen
Keyword(s):  
Dynamic Model ◽  
Model Identification ◽  
Training Data ◽  
The Novel ◽  
Inverse Dynamic ◽  
Modified Particle Swarm Optimization ◽  
Highly Nonlinear ◽  
Actuator System ◽  
Force Variation ◽  
Narx Model

In this paper, a novel inverse dynamic fuzzy NARX model is used for modeling and identifying the IPMC-based actuator’s inverse dynamic model. The contact force variation and highly nonlinear cross effect of the IPMC-based actuator are thoroughly modeled based on the inverse fuzzy NARX model-based identification process using experiment input-output training data. This paper proposes the novel use of a modified particle swarm optimization (MPSO) to generate the inverse fuzzy NARX (IFN) model for a highly nonlinear IPMC actuator system. The results show that the novel inverse dynamic fuzzy NARX model trained by MPSO algorithm yields outstanding performance and perfect accuracy.

STEPPER MOTOR SYSTEM IDENTIFICATION USING INVERSE DYNAMIC NEURAL MIMO NARX MODEL

2010 ◽  
Vol 13 (4) ◽  
pp. 34-44
Author(s):  
Anh Pham Huy Ho ◽  
Lam Huynh Phan
Keyword(s):  
Motor System ◽  
Learning Algorithm ◽  
Model Identification ◽  
Training Data ◽  
Stepper Motor ◽  
Inverse Dynamic ◽  
Highly Nonlinear ◽  
Nonlinear Features ◽  
Mimo Model ◽  
Narx Model

This paper introduces the novel inverse dynamic intelligent MIMO model which is applied for modeling and identifying the stepper motor dynamic model. Hence the highly nonlinear features of stepper motor system are modeled thoroughly based on the inverse neural NARX model identification process using experimental input-output training data. Consequently the proposed inverse neural NARX MIMO model scheme of the nonlinear stepper motor has been investigated. The results showed that the proposed inverse neural NARX MIMO model trained by the back propogation learning algorithm (BP) yields outstanding performance and perfect accuracy.

A NEW APPROACH OF THE 2-AXES PAM ROBOT ARM IDENTIFICATION USING NEURAL MIMO NARX MODEL

2013 ◽  
Vol 22 (01) ◽  
pp. 1250039
Author(s):  
HO PHAM HUY ANH ◽  
KYOUNG KWAN AHN
Keyword(s):  
Learning Algorithm ◽  
Back Propagation ◽  
Training Data ◽  
The Novel ◽  
Robot Arm ◽  
New Approach ◽  
Identification Process ◽  
Highly Nonlinear ◽  
Model Scheme ◽  
Narx Model

In this paper, a novel MIMO Neural NARX model is used for simultaneously modeling and identifying both joints of the 2-axes PAM robot arm's inverse and forward dynamic model. The highly nonlinear cross effect of both links of the 2-axes PAM robot arm are thoroughly modeled through an Inverse and Forward Neural MIMO NARX Model-based identification process using experimental input-output training data. Consequently the proposed Inverse and Forward Neural MIMO NARX model scheme of the nonlinear 2-axes PAM robot arm has been investigated. The results show that the novel Inverse and Forward Neural MIMO NARX Model trained by Back Propagation learning algorithm yields outstanding performance and perfect accuracy.

scholarly journals Identification of Mimo dynamic system using inverse Mimo Neural Narx model

2013 ◽  
Vol 16 (2) ◽  
pp. 13-25
Author(s):  
Anh Pham Huy Ho ◽  
Nam Thanh Nguyen
Keyword(s):  
Learning Algorithm ◽  
Back Propagation ◽  
Artificial Muscle ◽  
Training Data ◽  
The Novel ◽  
Robot Arm ◽  
Dynamic Inverse ◽  
Intelligent Model ◽  
First Time ◽  
Narx Model

This paper investigates the application of proposed neural MIMO NARX model to a nonlinear 2-axes pneumatic artificial muscle (PAM) robot arm as to improve its performance in modeling and identification. The contact force variations and nonlinear coupling effects of both joints of the 2-axes PAM robot arm are modeled thoroughly through the novel dynamic inverse neural MIMO NARX model exploiting experimental input-output training data. For the first time, the dynamic neural inverse MIMO NARX Model of the 2-axes PAM robot arm has been investigated. The results show that this proposed dynamic intelligent model trained by Back Propagation learning algorithm yields both of good performance and accuracy. The novel dynamic neural MIMO NARX model proves efficient for modeling and identification not only the 2-axes PAM robot arm but also other nonlinear dynamic systems.

scholarly journals A novel inverse dynamic model for a magnetorheological damper based on network inversion

2017 ◽  
Vol 24 (15) ◽  
pp. 3434-3453 ◽  
Author(s):  
MJL Boada ◽  
BL Boada ◽  
V Diaz
Keyword(s):  
Dynamic Model ◽  
Vibration Isolation ◽  
Mr Damper ◽  
Inverse Model ◽  
Magnetorheological Damper ◽  
Hysteretic Behavior ◽  
Inverse Dynamic ◽  
Inverse Dynamic Model ◽  
Mr Dampers ◽  
Highly Nonlinear

Semi-active suspensions based on magnetorheological (MR) dampers are receiving significant attention, especially for control of vibration isolation systems. The nonlinear hysteretic behavior of MR dampers can cause serious problems in controlled systems, such as instability and loss of robustness. Most of the developed controllers determine the desired damping forces which should be produced by the MR damper. Nevertheless, the MR damper behavior can only be controlled in terms of the applied current (or voltage). In addition to this, it is necessary to develop an adequate inverse dynamic model in order to calculate the command current (or voltage) for the MR damper to generate the desired forces as close as possible to the optimal ones. Due to MR dampers being highly nonlinear devices, the inverse dynamics model is difficult to obtain. In this paper, a novel inverse MR damper model based on a network inversion is presented to estimate the necessary current (or voltage) such that the desired force is exerted by the MR damper. The proposed inverse model is validated by carrying out experimental tests. In addition, a comparison of simulated tests with other damper controllers is also presented. Results show the effectiveness of the network inversion for inverse modeling of an MR damper. Thus, the proposed inverse model can act as a damper controller to generate the command current (or voltage) to track the desired damping force.

Modified Levenberg–Marquardt Algorithm for Backpropagation Neural Network Training in Dynamic Model Identification of Mechanical Systems

2017 ◽  
Vol 139 (3) ◽  
Author(s):  
Ming Li ◽  
Huapeng Wu ◽  
Yongbo Wang ◽  
Heikki Handroos ◽  
Giuseppe Carbone
Keyword(s):  
Neural Network ◽  
Dynamic System ◽  
Dynamic Model ◽  
Model Identification ◽  
Training Data ◽  
Entire System ◽  
Front End ◽  
The Neural Network ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt

For modeling a dynamic system in practice, it often faces the difficulty in improving the accuracy of the constructed analytical model, since some components of the dynamic model are often ignored deliberately due to the difficulty of identification. It is also unwise to apply the neural network to approximate the entire dynamic system as a black box, when the comprehensive knowledge of most components of the dynamics of a large system are available. This paper proposes a method that utilizes the backpropagation (BP) neural network to identify the unknown components of the dynamic system based on the experimental front-end inputs–outputs data of the entire system. It can avoid the difficulty in getting the direct training data for the unknown components, and brings great benefits in the practical application, since to get the front-end inputs–outputs data of the entire dynamic system is easier and cost-effective. In order to train such neural network for the unknown components of dynamics, a modified Levenberg–Marquardt algorithm, which can utilize the front-end inputs–outputs data of the entire dynamic system, has been developed in the paper. Three examples from different application points of view are presented in the paper, and the results show that the proposed modified Levenberg–Marquardt algorithm is efficient to train the neural network for the unknown components of the system based on the data of entire system. The constructed dynamics model, in which the unknown components are substituted by the neural network, can satisfy the requisite accuracy successfully in the computation.

Inductor Saturation Compensation in Three-Phase Three-Wire Voltage-Source Converters via Inverse System Dynamics-I

2020 ◽  
Author(s):  
Ziya Özkan ◽  
Ahmet Masum Hava
Keyword(s):  
Dynamic Model ◽  
Dynamic Performance ◽  
Current Control ◽  
Inverse System ◽  
Voltage Source ◽  
Voltage Source Converter ◽  
Two Phase ◽  
Inverse Dynamic ◽  
Three Phase ◽  
Steady State Current

In three-phase three-wire (3P3W) voltage-source converter (VSC) systems, utilization of filter inductors with deep saturation characteristics is often advantageous due to the improved size, cost, and efficiency. However, with the use of conventional synchronous frame current control (CSCC) methods, the inductor saturation results in significant dynamic performance loss and poor steady-state current waveform quality. This paper proposes an inverse dynamic model based compensation (IDMBC) method to overcome these performance issues. Accordingly, a review of inductor saturation and core materials is performed, and the motivation on the use of saturable inductors is clarified. Then, two-phase exact modelling of the 3P3W VSC control system is obtained and the drawbacks of CSCC have been demonstrated analytically. Based on the exact modelling, the inverse system dynamic model of the nonlinear system is obtained and employed such that the nonlinear plant is converted to a fictitious linear inductor system for linear current regulators to perform satisfactorily.

Sign in / Sign up

Export Citation Format

Share Document