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Author: search.filters.author.Mohanta, Hare KrishnaSubject: search.filters.subject.EEE

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    Optimized adaptive neuro-fuzzy inference system for pH control
    (IEEE, 2013) Bhanot, Surekha; Mohanta, Hare Krishna
    pH control plays an important role in many modern industrial plants due to strict environment regulations. This paper presents fuzzy logic based pH control scheme for neutralization process in which genetic algorithm is used to optimize the various membership functions of fuzzy inference system. Further, using this optimized fuzzy inference system, adaptive neuro-fuzzy inference system for pH neutralization process is developed. Performances of both control schemes are compared for servo and regulatory operations. Results indicate that adaptive neuro-fuzzy inference system based control uses fewer rules as compared to optimized fuzzy logic based control.
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    Differential evolution based optimal fuzzy logic control of pH neutralization process
    (IEEE, 2014) Bhanot, Surekha; Mohanta, Hare Krishna
    Differential evolution (DE) is a member of evolutionary algorithm family which has gained popularity due to its conceptual simplicity and better convergence. This paper presents fuzzy logic based pH control scheme for neutralization process in which DE is used to optimize the input and output membership functions of fuzzy inference system (FIS). The fitness function for optimization is integral of squared errors (ISE). DE is able to converge and find optimal global solution over narrow as well as wide search spaces. Finally the controller performance has been evaluated for servo and regulatory operations.
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    Self-tuned fuzzy logic control of a pH neutralization process
    (IEEE, 2015) Bhanot, Surekha; Mohanta, Hare Krishna
    On-line implementation of self-tuning mechanism based adaptive fuzzy logic control of a pH neutralization process which takes care of steady state error and time taken to reach steady state under varying operating conditions has been presented in this paper. The pH neutralization system is Armfield pH Sensor Accessory (PCT42) in conjunction with Process Vessel Accessory (PCT41) and Multifunction Process Control Teaching System (PCT40). The proposed adaptive scheme updates the normalized universe of discourse of output fuzzy membership functions with varying scaling factors based on error and change of error values. The speed of response of the adaptive controller is taken care by use of coarse control technique whereas amount of deviation under steady state is accounted with the help of fine control technique. The performance of adaptive scheme is tested for pH control at equivalence point. LabVIEW software is used for online communication, control and display.
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    Particle Swarm Optimization based Fuzzy Logic Control of pH Neutralization Process
    (IJAER, 2015-06) Mohanta, Hare Krishna; Bhanot, Surekha
    pH control plays an important role in many modern industrial plants due to strict environment regulations. This paper presents fuzzy logic based pH control scheme for neutralization process in which particle swarm algorithm is used to optimize the input and output membership functions of fuzzy inference system. Performance of control scheme has been evaluated for servo and regulatory operations.
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    Genetic Optimization based Adaptive Fuzzy Logic Control of a pH Neutralization Process
    (Sersc, 2014) Mohanta, Hare Krishna; Bhanot, Surekha
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    Neural Control of Neutralization Process using Fuzzy Inference System based Lookup Table
    (IJCA, 2021-10-20) Mohanta, Hare Krishna; Bhanot, Surekha
    Over a number of years, pH control of neutralization process is recognized as a benchmark for modeling and control of nonlinear processes. This paper first describes dynamic modeling of pH neutralization process. Thereafter fuzzy logic based pH control scheme for neutralization process is developed. Further, a two-dimensional (2-D) lookup table is generated based on defuzzification mechanism of fuzzy inference system (FIS). Finally, using this lookup table, a neural network control for pH neutralization process is developed. Performances of fuzzy logic based control and lookup table based neural network control for servo and regulatory operations are compared based on integral square error (ISE) and integral absolute error (IAE) criterions. Results indicate that lookup table based neural network control performs better than fuzzy logic based control.