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Model Predictive Control System Design for Boiler Turbine Process Sandeep Kumar Sunori; Pradeep Kumar Juneja; Anamika Bhatia Jain
International Journal of Electrical and Computer Engineering (IJECE) Vol 5, No 5: October 2015
Publisher : Institute of Advanced Engineering and Science

MPC is a computer based technique that requires the process model to anticipate the future outputs of that process. An optimal control action is taken by MPC based on this prediction. The MPC is so popular since its control performance has been reported to be best among other conventional techniques to control the multivariable dynamical plants with various inputs and outputs constraints. In the present work the control of boiler turbine process with three manipulated variables namely fuel flow valve position, steam control valve position and feed water flow valve position and three controlled variables namely drum pressure, output power and drum water level deviation [8] has been attempted using MPC technique. Boiler turbine process is very complex and nonlinear multivariable process. A linearized model obtained using Taylor series expansion around operating point has been used.

Multiloop and Prediction Based Controller Design for Sugarcane Crushing Mill Process Sandeep Kumar Sunori; Pradeep Kumar Juneja; Anamika Bhatia Jain
International Journal of Advances in Applied Sciences Vol 4, No 4: December 2015
Publisher : Institute of Advanced Engineering and Science

In the present work a sugarcane crushing mill is presented as a MIMO system with high multivariable interaction.A linear model of the plant is taken with flap position and turbine speed as manipulated variables and mill torque and buffer chute height as controlled variables.The multiloop PI controller has been designed for this plant by first investigating the RGA and the value of Niederlinski index of this plant.The decoupling of this system is done and the respective open loop and closed loop step responses are observed and compared with those of the composite MIMO system. Also the performance of multiloop controller is compared with controller designed using model predictive control system strategy for this plant.

Model Predictive Control System Analysis for Sugarcane Crushing Mill Process Sandeep Kumar Sunori; Pradeep Kumar Juneja; Anamika Bhatia Jain
Bulletin of Electrical Engineering and Informatics Vol 4, No 3: September 2015
Publisher : Institute of Advanced Engineering and Science

MPC is a computer based technique that requires the process model to anticipate the future outputs of that process. An optimal control action is taken by MPC based on this prediction. The MPC is so popular since its control performance has been reported to be best among other conventional techniques to control the multivariable dynamical plants with various inputs and outputs constraints. In this paper the performance of an MPC controller on a single stage of milling train of sugar mill is analyzed. A linear model of the plant is taken with flap position and turbine speed set point as manipulated variables and mill torque and buffer chute height as controlled variables. The set point tracking responses are compared for constrained and unconstrained cases. The effect of presence of unmeasured disturbance also is investigated.

Model Predictive Control System Analysis for Sugarcane Crushing Mill Process Sandeep Kumar Sunori; Pradeep Kumar Juneja; Anamika Bhatia Jain
Bulletin of Electrical Engineering and Informatics Vol 4, No 3: September 2015
Publisher : Institute of Advanced Engineering and Science

MPC is a computer based technique that requires the process model to anticipate the future outputs of that process. An optimal control action is taken by MPC based on this prediction. The MPC is so popular since its control performance has been reported to be best among other conventional techniques to control the multivariable dynamical plants with various inputs and outputs constraints. In this paper the performance of an MPC controller on a single stage of milling train of sugar mill is analyzed. A linear model of the plant is taken with flap position and turbine speed set point as manipulated variables and mill torque and buffer chute height as controlled variables. The set point tracking responses are compared for constrained and unconstrained cases. The effect of presence of unmeasured disturbance also is investigated.

Model Predictive Control System Analysis for Sugarcane Crushing Mill Process Sandeep Kumar Sunori; Pradeep Kumar Juneja; Anamika Bhatia Jain
Bulletin of Electrical Engineering and Informatics Vol 4, No 3: September 2015
Publisher : Institute of Advanced Engineering and Science

MPC is a computer based technique that requires the process model to anticipate the future outputs of that process. An optimal control action is taken by MPC based on this prediction. The MPC is so popular since its control performance has been reported to be best among other conventional techniques to control the multivariable dynamical plants with various inputs and outputs constraints. In this paper the performance of an MPC controller on a single stage of milling train of sugar mill is analyzed. A linear model of the plant is taken with flap position and turbine speed set point as manipulated variables and mill torque and buffer chute height as controlled variables. The set point tracking responses are compared for constrained and unconstrained cases. The effect of presence of unmeasured disturbance also is investigated.

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