Decentralized PI-D Controller Applied to an Aerothermic Process

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

doi:10.21307/ijssis-2017-520

Cited by 2 publications

(13 citation statements)

References 21 publications

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“…Hence, when the air temperature and the air flow inside the process are assumed to be uniform, a linear system model can be obtained. This kind of aerothermic process is being used by many researchers to check their new control strategies [1][2][3][4]6,14].…”

Section: Aerothermic Process Descriptionmentioning

confidence: 99%

“…The identified multivariable transfer functions of the aerothermic process are given by the following system equation [1][2]: (2) This equation can be shown as a schematic diagram in figure 5 The negative gain in the interactive transfer function, H 12 (s), implies that the air temperature behaves in the opposite way. In fact, the interaction effect tends to reduce the air temperature when the air flow increases.…”

Section: Figure 4: Data Set For Direct Continuous-time Identificationmentioning

confidence: 99%

“…In the aerothermic process case, the partial decoupling is considered in order to eliminate the interactions caused by the second loop on the first one. This partial decoupling is represented by the To eliminate the interaction between U 2 and Y 1 , after some simple mathematical manipulations, the expression for the ideal decoupler is given by the following expression [1][2]:…”

Section: Iv-2 Decentralized Pi-d Controllermentioning

confidence: 99%

“…In this paper, the Decentralized PI-D controller (DPI-D) [1] is considered and compared to both a Centralized Discrete State Space Model Predictive Control (CDSSMPC) [4] and a conventional PID controller to ensure the desired level of temperature and air flow of the aerothermic process. To fulfil the requirement for integral action in the CDSSMPC controller, it is embedded with integrators to achieve this objective and ensure outputs steady-state error free [16].…”

Section: Introductionmentioning

confidence: 99%

“…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%

See 4 more Smart Citations

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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Section: Aerothermic Process Descriptionmentioning

confidence: 99%

Section: Figure 4: Data Set For Direct Continuous-time Identificationmentioning

confidence: 99%

Section: Iv-2 Decentralized Pi-d Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Implementation of a Fuzzy Logic Controller for an Aerothermic Process

Khouili*,

Ramzi

2020

IJITEE

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The objective of any controller design is to maintain the set point value despite its variation, with a good rejection of various disturbances that can infect the system to be controlled and to minimize the energy consumption. To achieve these objectives, several control methods are proposed in the literature. Nevertheless, due to the non-linear behaviour of most industrial systems, fuzzy logic control remains the most appropriate method to control this type of system. This article compares two fuzzy logic control techniques having two inputs and one output. These methods are applied to an aerothermic process in our laboratory. The obtained experimental results allowed us to achieve the main control objectives, such asset-point trackingand regulation. These results encouraged us to exploit the advantage of each technique, in order to make the controller design simpler and to minimize the time required to calculate the command applied to the process.

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Decentralized PI-D Controller Applied to an Aerothermic Process

Cited by 2 publications

References 21 publications

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

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

Implementation of a Fuzzy Logic Controller for an Aerothermic Process

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