Continuous Time Identification and Decentralized Pid Controller of an Aerothermic Process

Ramzi, Mustapha; Youlal, H.; Haloua, Mohamed

doi:10.21307/ijssis-2017-491

Cited by 5 publications

(17 citation statements)

References 19 publications

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“…This partial decoupling is represented by the Figure 7. To eliminate the interaction between U2 and Y1, after some simple mathematical manipulations, the expression for the ideal decoupler is given by the following expression [7]: …”

Section: Decentralized Pi-d Controllermentioning

confidence: 99%

“…It is argued that the temperature control is no more a challenging control problem in most applications. Nevertheless, some practical issues in many temperature control applications stimulate new developments and further investigations [7][8][9][10][11][12][13][14].…”

mentioning

confidence: 99%

“…Different prototypes of the considered process have been used to check new control strategies and many results were reported in the single variable control cases [7,[15][16][17][18]. Worth to mention herein that the basic factory control system delivered with the process is restricted to classical analogue PID control [19].…”

mentioning

confidence: 99%

“…In the synthesis of the decentralized PI-D controller, a multivariable transfer function is identified using the numeric Direct Continuous-Time Identification (DCTI) approach [7,[20][21]. This technique has attracted an increasing attention of several researchers in the last few years [22][23][24][25][26].…”

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confidence: 99%

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Decentralized PI-D Controller Applied to an Aerothermic Process

Ramzi¹,

Bennis

Haloua

et al. 2012

International Journal on Smart Sensing and Intelligent Systems

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Section: Decentralized Pi-d Controllermentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Decentralized PI-D Controller Applied to an Aerothermic Process

Ramzi¹,

Bennis

Haloua

et al. 2012

International Journal on Smart Sensing and Intelligent Systems

Self Cite

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“…The implementation of the discrete state space predictive control in real time is based on the result of the cost function minimization and only the first input of the optimal command sequence is used each time a new state is updated. In the synthesis of the CDSSMPC and the DPI-D controllers, a multivariable model is identified using a numeric direct continuous-time identification approach [1][2][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Centralized Discrete State Space Model Predictive Control And Decentralized Pi-D Controller Of An Aerothermic Process

Ramzi¹,

Youlal²

2014

International Journal on Smart Sensing and Intelligent Systems

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The aerothermic process is a pilot scale heating and ventilation system equipped with a heater grid and a centrifugal blower. The interaction between its main variables is considered as challenging for mono-variable controllers. A change in the ventilator speed affects the temperature behavior which represents a factor that must be managed for energy saving and the human welfare. This paper presents an experimental comparison between a Centralized Discrete State Space Model Predictive Control (CDSSMPC) and a Decentralized PI-D (DPI-D) controller. These both techniques are designed by using respectively the Laguerres functions and the static decoupler approach. To demonstrate the effectiveness of the two methods, an implementation on an aerothermic process is performed. This pilot scale is fully connected through the Humusoft MF624 data acquisition system for real time control. The results show satisfactory performance in closedloop of the DPI-D controller compared to the CDSSMPC and the conventional PID ones.Index terms: Centralized discrete state space model predictive control, Laguerre functions, Static decoupler, Decentralized PI-D controller, Multivariable systems, Aerothermic process. M. Ramzi and H. Youlal, CENTRALIZED DISCRETE STATE SPACE MODEL PREDICTIVE CONTROL AND DECENTRALIZED PI-D CONTROLLER OF AN AEROTHERMIC PROCESS 1831 I.

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Designing multivariable PI controller with multi-response optimization for a pilot plant binary distillation column: a robust design approach

Bhat

Thirunavukkarasu

Selvanathan

et al. 2023

JEDT

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Purpose The purpose of this paper is to propose and validate a robust industrial control system. The aim is to design a Multivariable Proportional Integral controller that accommodates multiple responses while considering the process's control and noise parameters. In addition, this paper intended to develop a multidisciplinary approach by combining computational science, control engineering and statistical methodologies to ensure a resilient process with the best use of available resources. Design/methodology/approach Taguchi's robust design methodology and multi-response optimisation approaches are adopted to meet the research aims. Two-Input-Two-Output transfer function model of the distillation column system is investigated. In designing the control system, the Steady State Gain Matrix and process factors such as time constant (t) and time delay (?) are also used. The unique methodology is implemented and validated using the pilot plant's distillation column. To determine the robustness of the proposed control system, a simulation study, statistical analysis and real-time experimentation are conducted. In addition, the outcomes are compared to different control algorithms. Findings Research indicates that integral control parameters (Ki) affect outputs substantially more than proportional control parameters (Kp). The results of this paper show that control and noise parameters must be considered to make the control system robust. In addition, Taguchi's approach, in conjunction with multi-response optimisation, ensures robust controller design with optimal use of resources. Eventually, this research shows that the best outcomes for all the performance indices are achieved when Kp11 = 1.6859, Kp12 = −2.061, Kp21 = 3.1846, Kp22 = −1.2176, Ki11 = 1.0628, Ki12 = −1.2989, Ki21 = 2.454 and Ki22 = −0.7676. Originality/value This paper provides a step-by-step strategy for designing and validating a multi-response control system that accommodates controllable and uncontrollable parameters (noise parameters). The methodology can be used in any industrial Multi-Input-Multi-Output system to ensure process robustness. In addition, this paper proposes a multidisciplinary approach to industrial controller design that academics and industry can refine and improve.

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Continuous Time Identification and Decentralized Pid Controller of an Aerothermic Process

Abstract: Abstract-The interactions between input/output in multivariable processes

Cited by 5 publications

References 19 publications

Decentralized PI-D Controller Applied to an Aerothermic Process

Decentralized PI-D Controller Applied to an Aerothermic Process

Centralized Discrete State Space Model Predictive Control And Decentralized Pi-D Controller Of An Aerothermic Process

Designing multivariable PI controller with multi-response optimization for a pilot plant binary distillation column: a robust design approach

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