ROBUSTNESS/PERFORMANCE ANALYSIS OF INTERNAL MODEL CONTROL SYSTEM FOR MINIMUM PHASE SYSTEMS

2021 ◽  
Vol 49 (0) ◽  
Author(s):  
Arun R. Pathiran ◽  
Mulushewa Getachew
Keyword(s):  
Control System ◽  
Performance Analysis ◽  
Internal Model ◽  
Internal Model Control ◽  
Minimum Phase ◽  
Model Control ◽  
Phase Systems

Attitude Controller Design of Multiple Independently Targeted Reentry Vehicle Based on Internal Model Control

2013 ◽  
Vol 462-463 ◽  
pp. 809-814
Author(s):  
Fei Zhao ◽  
Fan Li ◽  
Jian Hui Zhao
Keyword(s):  
Control System ◽  
Attitude Control ◽  
Internal Model ◽  
Internal Model Control ◽  
Attitude Dynamics ◽  
Attitude Control System ◽  
Reentry Vehicle ◽  
Model Control ◽  
Internal Model Controller ◽  
Attitude Controller

A Multiple Independently Targeted Reentry Vehicle (MIRV) is a ballistic missile payload containing several warheads each capable of hitting one of a group of targets. In the process of missile flight control, the release of warheads brings about coupling to the missile attitude control system which will lower the flight stability. In order to solve this problem, a missile attitude controller, which combined the α-order integral inverse system with internal model principle, was presented. Firstly, determine the Post Boost Vehicle (PBV) attitude dynamics model. Then, combine the linearization of attitude dynamics equation with feed-forward decoupling method to implement the attitude decoupling. Finally, a two-degree of freedom (TOF) multivariable internal model controller was set up to optimize the control system performance. Simulation results show that the coupling of attitude control system has been eliminated. Compared with the original system, the internal model controller provides the control system better input-tracking performance, robust stability and interference suppression capacity.

scholarly journals Design of Internal Model Control Based on an Optimal Control for a Servo System

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Hiromitsu Ogawa ◽  
Ryo Tanaka ◽  
Takahiro Murakami ◽  
Yoshihisa Ishida
Keyword(s):  
Optimal Control ◽  
Control System ◽  
Transfer Function ◽  
Internal Model ◽  
Dead Time ◽  
Servo System ◽  
Previous Method ◽  
Internal Model Control ◽  
Model Control ◽  
Disturbance Response

This paper describes a design of internal model control based on an optimal control for a servo system. The control system has the feedback based on the proposed disturbance compensator in the disturbance response. The compensator is designed to become the denominator of the transfer function without a dead time in the disturbance responses. The disturbance response of the proposed method is faster than that of the previous method.

scholarly journals An Internal Model Controller for Three-Phase APF Based on LS-Extreme Learning Machine

2014 ◽  
Vol 8 (1) ◽  
pp. 717-722
Author(s):  
Zhenhua Shao ◽  
Tianxiang Chen ◽  
Li-an Chen ◽  
Hong Tian
Keyword(s):  
Control System ◽  
Least Squares ◽  
Extreme Learning Machine ◽  
Internal Model ◽  
Internal Model Control ◽  
Support Vector ◽  
Three Phase ◽  
Model Control ◽  
Internal Model Controller ◽  
Learning Machine

Aiming at the problem that the three-phase APF’s dynamic model is a multi-variable, nonlinear and strong coupling system, an internal model controller for three-phase APF based on LS-Extreme Learning Machine is studied in this paper. As a novel single hidden layer feed-forward neural networks, extreme learning machine (ELM) has several advantages: simple net structural, fast learning speed, good generalization performance and so on. In order to improve the controller’s dynamic responses, a least squares extreme learning machine for internal model control is proposed. A least squares ELM regression (LS-ELMR) model for the three-phase APFS on-line monitoring was built from external factors with in-out datum. Moreover, the relative stable error is presented to evaluate the system performance and the features for the internal model control system based on extreme learning machine, neural network, kernel ridge regress and support vector machine. The experimental results show that the LS-internal model control system based on extreme learning machine has good dynamic performance and strong filtering result.

Design of an Internal Model Control System based on the Database-Driven Modeling for a Hydraulic Excavator

2021 ◽  
Vol 141 (3) ◽  
pp. 295-300
Author(s):  
Tomofumi Okada ◽  
Zhe Guan ◽  
Takuya Kinoshita ◽  
Toru Yamamoto ◽  
Kazushige Koiwai ◽  
...  
Keyword(s):  
Control System ◽  
Internal Model ◽  
Internal Model Control ◽  
Hydraulic Excavator ◽  
Model Control

scholarly journals Permeate Flux Control in SMBR System by Using Neural Network Internal Model Control

Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1672
Author(s):  
Norhaliza Abdul Wahab ◽  
Nurazizah Mahmod ◽  
Ramon Vilanova
Keyword(s):  
Neural Network ◽  
Control System ◽  
Radial Basis Function ◽  
Basis Function ◽  
Membrane Fouling ◽  
Internal Model ◽  
Closed Loop ◽  
Internal Model Control ◽  
Permeate Flux ◽  
Model Control

This paper presents a design of a data-driven-based neural network internal model control for a submerged membrane bioreactor (SMBR) with hollow fiber for microfiltration. The experiment design is performed for measurement of physical parameters from an actuator input (permeate pump voltage), which gives the information (outputs) of permeate flux and trans-membrane pressure (TMP). The palm oil mill effluent is used as an influent preparation to depict fouling phenomenon in the membrane filtration process. From the experiment, membrane fouling potential is observed from flux decline pattern, with a rapid increment of TMP (above 200 mbar). Membrane fouling is a complex process and the available models in literature are not designed for control system (filtration performance). Therefore, this work proposes an aeration fouling control strategy to measure the filtration performance. The artificial neural networks (Feed-Forward Neural Network—FFNN, Radial Basis Function Neural Network—RBFNN and Nonlinear Autoregressive Exogenous Neural Network—NARXNN) are used to model dynamic behaviour of flux and TMP. In this case, only flux is used in closed loop control application, whereby the TMP effect is used for monitoring. The simulation results show that reliable prediction of membrane fouling potential is obtained. It can be observed that almost all the artificial neural network (ANN) models have similar shape with the actual data set, with the highest accuracy of more than 90% for both RBFNN and NARXN. The RBFNN is preferable due to simple structure of the network. In the control system, the RBFNN IMC depicts the highest closed loop performance with only 3.75 s (settling time) for setpoint changes when compared with other controllers. In addition, it showed fast performance in disturbance rejection with less overshoot. In conclusion, among the different neural network tested configurations the one based on radial basis function provides the best performance with respect to prediction as well as control performance.

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