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Articles by T.R. Rangaswamy
Total Records ( 2 ) for T.R. Rangaswamy
  N. Bharathi , J. Shanmugam and T.R. Rangaswamy
  In the present study, control of pH neutralization process using neural and fuzzy controller is proposed. Initially a conventional PI controller based on the Relay Feedback method is tried to control the pH at different linear regions. Control of pH by conventional PI controller based on the local linear model fails to provide satisfactory performance over the entire region. Hence to overcome this drawback a Neuro controller with inverse model anticipation and a fuzzy controller are used. In this study a novel fuzzy controller is used. Most fuzzy controllers use control error (e) and change in the control error ( e) as controller inputs and not able to differentiate the region in which process operates. This controller uses set point as third input to select the region in which the process is operating. An experimental study of the performance of the intelligent controllers designed is carried out on a pilot plant in the lab.
  S. Nagarajan , J. Shanmugam and T.R. Rangaswamy
  This study presents the design of Adaptive Neuro-Fuzzy Observer based sensor fault detection in a three-tank interacting level process. Three pairs of observers estimate the three system states. These Observers are designed with Multiple Adaptive Neuro-Fuzzy Inference System (MANFIS) that uses a neural network to fix optimal shape and parameters for the membership functions and effective rule base for the fuzzy system. Fault detection is performed by estimating the states of the level process and comparing them with measured values. A fault is signaled when the difference between the estimated and measured values crosses a threshold value. Decision functions are built from estimation errors to detect the fault. If any failure is identified, the control law is modified accordingly using the estimated value replacing the failed sensor output. In this research, MANFIS observer based fault detection is designed and simulated. Since, the threshold value can be different for different set point, a Neural Network (NN) based threshold generator is designed to give best threshold values for fault detection. The individual failures of three level sensors are considered for various set points and the results are discussed. The results show that the system is able to detect any sensor failure for any set point and to control the level in interacting tanks perfectly under failure situations.
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