Valarmathi Ramasamy
Valarmathi Ramasamy has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
A. Kannabiran
SASTRA Deemed To Be University
1 shared publication- Guruprasath Muralidharan
Smarta Opti Solutions Pvt Ltd., Chennai 600073, India
1 shared publication - Kannan Ramkumar
SASTRA Deemed University
1 shared publication - Rakesh Kumar Sidharthan
School of Electrical & Electronics Engineering, SASTRA Deemed University, Thanjavur 613 401, India
1 shared publication V. Gomathi
Madras Institute of Technology Campus, Anna University
1 shared publicationV. Sudha
SASTRA Deemed To Be University
1 shared publication
Latest Publications
- 1 October 2025
- Published by Springer Nature
- p. 416-428
Fault Diagnosis in Three-Phase Voltage Source Inverter Using Machine Learning Techniques
- 1 October 2025
- Published by Springer Nature
- p. 416-428
Fault Diagnosis in Three-Phase Voltage Source Inverter Using Machine Learning Techniques
- Open Access
Optimal Tuning of Model Predictive Controller Weights Using Genetic Algorithm with Interactive Decision Tree for Industrial Cement Kiln Process
Energy intense nature of cement kiln demands optimal operation to minimize the energy requirement. Optimal control of cement kiln is achieved by proper tuning of the model predictive controller (MPC), which is addressed in this work. Genetic algorithm (GA) is used to determine the MPC weights that minimize the overall energy utilization with reduced tracking error. Single objective function has been formulated using importance weighted performance metrics like energy utilization and integral absolute error in tracking the desired response. Importance weights are determined in specific to the control scenarios using an interactive decision tree (IDT). It interacts with the operator to detect the weaker metrics and raises the importance level for further improvement. The algorithm terminates after attending all the metrics with the consent from the operator. Five control scenarios that predominantly occur in industrial cement kiln have been considered in this study. It includes tracking, measured, and unmeasured disturbance rejection of pulse and Gaussian type noises. The results illustrate the minimized energy operation with the use of the proposed single objective function as compared with the multi-objective function-based GA tuning procedure.
Open AccessOptimal Tuning of Model Predictive Controller Weights Using Genetic Algorithm with Interactive Decision Tree for Industrial Cement Kiln Process
Energy intense nature of cement kiln demands optimal operation to minimize the energy requirement. Optimal control of cement kiln is achieved by proper tuning of the model predictive controller (MPC), which is addressed in this work. Genetic algorithm (GA) is used to determine the MPC weights that minimize the overall energy utilization with reduced tracking error. Single objective function has been formulated using importance weighted performance metrics like energy utilization and integral absolute error in tracking the desired response. Importance weights are determined in specific to the control scenarios using an interactive decision tree (IDT). It interacts with the operator to detect the weaker metrics and raises the importance level for further improvement. The algorithm terminates after attending all the metrics with the consent from the operator. Five control scenarios that predominantly occur in industrial cement kiln have been considered in this study. It includes tracking, measured, and unmeasured disturbance rejection of pulse and Gaussian type noises. The results illustrate the minimized energy operation with the use of the proposed single objective function as compared with the multi-objective function-based GA tuning procedure.