A novel modified opposition‐based hunger games search algorithm to design fractional order proportional‐integral‐derivative controller for magnetic ball suspension system

Abstract

This study focuses on construction of novel enhanced metaheuristic algorithm using a modified opposition‐based learning technique and the hunger games search algorithm. The proposed modified opposition‐based hunger games search (mOBL‐HGS) algorithm is aimed to be used as an efficient tool to tune a fractional order proportional‐integral‐derivative (FOPID) controller in order to control a magnetic ball suspension system with greater flexibility. The challenging benchmark functions from CEC2017 test suite are used to confirm the greater performance of the proposed mOBL‐HGS algorithm. The proposed mOBL‐HGS algorithm is also utilized to reach the optimum values of a FOPID controller employed in a magnetic ball suspension system in order to demonstrate its capability in terms of a complex real‐world engineering problem. The latter case is confirmed through comparative evaluations of statistical analysis, convergence profile, transient response, frequency response, disturbance rejection, and robustness. The demonstrated results confirm the greater ability of the proposed approach to control a magnetic ball suspension system.

An adaptive neuro-fuzzy based on a fractional-order proportional integral derivative design for a two-legged robot with an improved swarm algorithm

“…Such that, the authors in [24] provide a nonlinear PID controller to reduce the error to minimum value to increase the system stability for different models. Thus, we proposed the same technique for the neural-fuzzy network input with the FOPID controller [25,26] to present the best results for the TLR and increase the system stability.…”

A new MOPSO algorithm for solving mathematical test functions and control engineering problems

“…Nowadays, presenting an effective solution to improve the performance of conventional controllers by determining the adjustable parameters is a controversial research topic. One of the most successful schemes developed to find the proper values of the control parameters is applying the evolutionary intelligence-based algorithms (Izci, 2021; İzci and Ekinci, 2021; İzci et al, 2022a, 2022b).…”

“…Nowadays, presenting an effective solution to improve the performance of conventional controllers by determining the adjustable parameters is a controversial research topic. One of the most successful schemes developed to find the proper values of the control parameters is applying the evolutionary intelligence-based algorithms (Izci, 2021;_ Izci and Ekinci, 2021;_ Izci et al, 2022a_ Izci et al, , 2022b). Among numerous evolutionary strategies, the particle swarm optimization (PSO) scheme has been successfully used by many researchers to reach optimum solutions for many scientific problems.…”

Frequency control of nuclear-renewable hybrid energy systems using optimal PID and FOPID controllers

“…A newly created unique algorithm named modified opposition-based hunger games search (mOBL-HGS) has been introduced here to tune the FOPID controller effectively. Extensive analyses using the CEC2017 test suite have demonstrated that the mOBL-HGS algorithm performed better than the GWO, HHO, AO, and original form of the HGS algorithm [ 14 ].…”