scholarly journals Internal model controller based PID with fractional filter design for a nonlinear process

2020 ◽  
Vol 10 (1) ◽  
pp. 243
Author(s):  
Hemavathy P.R. ◽  
Mohamed Shuaib Y ◽  
S.K. Lakshmanaprabu
Keyword(s):  
Internal Model ◽  
Process Model ◽  
Filter Design ◽  
Linear Process ◽  
Piecewise Linearization ◽  
Time Model ◽  
First Order ◽  
Non Linear ◽  
Internal Model Controller ◽  
Fractional Filter

In this paper, an Internal model Controller (IMC) based PID with fractional filter for a first order plus time delay process is proposed. The structure of the controller has two parts, one is integer PID controller part cascaded with fractional filter. The proposed controller has two tuning factors λ, filter time constant and a, fractional order of the filter. In this work, the two factors are decided in order to obtain low Integral Time Absolute Error (ITAE). The effectiveness of the proposed controller is studied by considering a non linear (hopper tank) process. The experimental set up is fabricated in the laboratory and then data driven model is developed from the experimental data. The non linear process model is linearised using piecewise linearization and two linear regions are obtained. At each operating point, linear first order plus dead time model is obtained and the controller is designed for the same. To show the practical applicability, the proposed controller is implemented for the proposed experimental laboratory prototype.

scholarly journals Model Predictive Control of Non-Linear Systems Using Tensor Flow-Based Models

2020 ◽  
Vol 10 (11) ◽  
pp. 3958
Author(s):  
Rómulo Antão ◽  
José Antunes ◽  
Alexandre Mota ◽  
Rui Escadas Martins
Keyword(s):  
Neural Networks ◽  
Process Model ◽  
Linear Process ◽  
Yeast Fermentation ◽  
Loop Control ◽  
Model Based ◽  
Non Linear ◽  
Predictive Controller ◽  
Recent Trends ◽  
Response Performance

The present paper proposes an approach for the development of a non-linear model-based predictive controller (NMPC) using a non-linear process model based on Artificial Neural Networks (ANNs). This work exploits recent trends on ANN literature using a TensorFlow implementation and shows how they can be efficiently used as support for closed-loop control systems. Furthermore, it evaluates how the generalization capability problems of neural networks can be efficiently overcome when the model that supports the control algorithm is used outside of its initial training conditions. The process’s transient response performance and steady-state error are parameters under focus and will be evaluated using a MATLAB’s Simulink implementation of a Coupled Tank Liquid Level controller and a Yeast Fermentation Reaction Temperature controller, two well-known benchmark systems for non-linear control problems.

Non-Linear Process Model for CMP-APC

2011 ◽  
Author(s):  
Toshihiro Morisawa ◽  
Hiromichi Kobayashi ◽  
Yukio Takeda
Keyword(s):  
Process Model ◽  
Linear Process ◽  
Non Linear

Boiler Combustion System of Multivariable Internal Model Control Application Research

2013 ◽  
Vol 648 ◽  
pp. 305-310
Author(s):  
Ji Liang Shang ◽  
Da Hai Ren
Keyword(s):  
Internal Model ◽  
Process Model ◽  
Controller Design ◽  
Design Method ◽  
Industrial Process ◽  
Internal Model Control ◽  
Model Control ◽  
Internal Model Controller ◽  
System Output ◽  
Control Application

In the industrial process of common multivariable time delay input/output system, a multivariable decoupling internal model controller design method is put forward based on the internal model control structure, the method for the design of internal model controller has the function of decoupling and controller. The advantage has the ability to achieve approximate or complete decoupling in nominal system output response, the method is used on the multivariable strong coupling of the boiler combustion control system to design and simulation study, the simulation results prove the effectiveness of the method. And in the process model and process mismatch showed strong robustness and anti jamming ability.

Design of Ship Dynamic Positioning Controller Based on Internal Model Control

2014 ◽  
Vol 926-930 ◽  
pp. 1505-1508
Author(s):  
Bao Yu Ye ◽  
Xiong Fei Huang ◽  
Fang Zhu ◽  
Chuan Sheng Liu
Keyword(s):  
Internal Model ◽  
Internal Model Control ◽  
Dynamic Positioning ◽  
Positioning System ◽  
Dynamic Positioning System ◽  
First Order ◽  
Model Control ◽  
Internal Model Controller ◽  
Coupling Characteristics ◽  
The Mathematical Model

According to the characteristics of ship dynamic positioning system, internal model controller is presented based on the mathematical model of ship dynamic positioning system. First, first-order internal model controller is designed in the surge motion. Then, aiming at the coupling characteristics on the movement of sway and yaw, decoupling internal model controller is designed. Simulation results show that the designed controller is effective for the dynamic positioning of ships.

scholarly journals Influence of the Porosity of Polymer Foams on the Performances of Capacitive Flexible Pressure Sensors

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1968 ◽  
Author(s):  
Sylvie Bilent ◽  
Thi Hong Nhung Dinh ◽  
Emile Martincic ◽  
Pierre-Yves Joubert
Keyword(s):  
Experimental Data ◽  
Linear Model ◽  
Dielectric Material ◽  
Pressure Sensors ◽  
Volume Ratio ◽  
Measurement Range ◽  
Polymer Foams ◽  
First Order ◽  
Non Linear ◽  
Flexible Pressure Sensors

This paper reports on the study of microporous polydimethylsiloxane (PDMS) foams as a highly deformable dielectric material used in the composition of flexible capacitive pressure sensors dedicated to wearable use. A fabrication process allowing the porosity of the foams to be adjusted was proposed and the fabricated foams were characterized. Then, elementary capacitive pressure sensors (15 × 15 mm2 square shaped electrodes) were elaborated with fabricated foams (5 mm or 10 mm thick) and were electromechanically characterized. Since the sensor responses under load are strongly non-linear, a behavioral non-linear model (first order exponential) was proposed, adjusted to the experimental data, and used to objectively estimate the sensor performances in terms of sensitivity and measurement range. The main conclusions of this study are that the porosity of the PDMS foams can be adjusted through the sugar:PDMS volume ratio and the size of sugar crystals used to fabricate the foams. Additionally, the porosity of the foams significantly modified the sensor performances. Indeed, compared to bulk PDMS sensors of the same size, the sensitivity of porous PDMS sensors could be multiplied by a factor up to 100 (the sensitivity is 0.14 %.kPa−1 for a bulk PDMS sensor and up to 13.7 %.kPa−1 for a porous PDMS sensor of the same dimensions), while the measurement range was reduced from a factor of 2 to 3 (from 594 kPa for a bulk PDMS sensor down to between 255 and 177 kPa for a PDMS foam sensor of the same dimensions, according to the porosity). This study opens the way to the design and fabrication of wearable flexible pressure sensors with adjustable performances through the control of the porosity of the fabricated PDMS foams.

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