A novel cascaded fractional fuzzy approach for control of fermentation process

Mohan, V.; Pachauri, Nikhil; Panjwani, Bharti; Kamath, Dattaguru V.

doi:10.1016/j.biortech.2022.127377

Cited by 11 publications

(2 citation statements)

References 39 publications

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“…NSGA-II performs all the computations in this work and finds the controller parameters for its optimum performance. The design steps for implementing NSGA-II are given in [38]. In subsequent sections, the performance of the NSGA-II optimised FOFPI ?…”

Section: Multiobjective Optimisation and Nsga-iimentioning

confidence: 99%

“…A different type of fractional order fuzzy PD plus conventional I controller is proposed by Jesus et al [32], in which a basic genetic algorithm is utilised to optimise its parameters. Due to efficient and robust control offered by fractional order fuzzy PID controllers, its implementation is found in diverse fields such as load frequency control of interconnected systems [33], smart seismic isolated structures [34], automatic generation control of power systems [35], hydrometallurgy [36], hybrid power system [37], bioreactor [38].…”

Section: Introductionmentioning

confidence: 99%

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Self-regulatory Fractional Fuzzy Control for Dynamic Systems: An Analytical Approach

Mohan

Panjwani

Chhabra

et al. 2022

Int. J. Fuzzy Syst.

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This paper presents an analytical design of a fractional order fuzzy proportional integral plus derivative (FOFPI + D) controller. Artificial intelligence is incorporated into the controller with the help of a formula-based fuzzy logic system. The designed scheme combines fractional order fuzzy PI (FOFPI) and fractional order fuzzy D (FOFD) controller, derived from fundamental FOPID control law. The proposed scheme enjoys the linear structure of the FOPID controllers with non-linear gains that provide self-tuning control capability. The sufficient condition for stability of the closed-loop system is also established using the graphical approach. Performance of the proposed FOFPI + D, its integer order variant (FPI + D), and conventional controllers is examined for control of a highly non-linear and uncertain two-link robotic manipulator system. The optimum parameters of controllers are found by minimising aggregated control variation and error objective through non-dominated sorting genetic algorithm-II (NSGA-II). The comparison for trajectory tracking shows that FOFPI + D has the minimum integral absolute error (IAE) compared to other controllers. Further, rigorous performance investigations are performed to verify the robustness of designed controllers against parametric uncertainties, the varying boundary conditions of reference trajectory and disturbance rejection. It is concluded from the results that the proposed FOFPI + D controller exhibits superior performance.

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Section: Multiobjective Optimisation and Nsga-iimentioning

confidence: 99%