Optimal Control Parameters Research Articles

Use of the Simple Multicriteria Decision-Making (MCDM) Method for Optimization of the High-Alloy Steel Cutting Processby the Abrasive Water Jet

In the case of advanced manufacturing technologies, which include Abrasive Water Jet Machining, optimization of control parameters is necessary to achieve appropriate efficiency and quality. One of the optimization methods used in the presented research is SAW, from the Multi-Criteria Decision Making (MCDM) group. In multi-criteria decision-making (MCDM) situations, the criterion weights are crucial components that have a big impact on the outcomes. A novel technique called MEREC (MEthod based on the Removal Effects of Criteria) was presented to find the objective weights of the criteria. The research covered cutting high-alloy steel using AWJ, under the Design of Experiment (DoE) within the L9 orthogonal table. Abrasive flow rate, pressure, and feed were selected as control parameters. The cutting depth (beneficial) and the roughness of the cut surface Sa (non-beneficial) were taken as the output parameters. The result of the research is the determination of the impact of individual control parameters and the determination of a set of control parameters from the point of view of efficiency and quality.

Enhancing DC Motor Speed Control Performance Using Heuristic Optimization and Comparative Analysis of Control Methods

Direct Current (DC) motors are an important component that converts electrical energy into mechanical energy, used in a wide range of applications from industrial applications to home appliances. DC motor speed control has an important role in industrial processes to increase efficiency, realize precise movements and optimize energy consumption. In this study, various control methods and parameter optimization techniques for speed control of DC motors, which have a wide range of applications, have been systematically analyzed. The aim of the study is to develop an effective control strategy to ensure that DC motors reach the determined target speed by monitoring them in real time at different speeds and to minimize fluctuations caused by variable loads or external factors. In our study, Proportional-Integral-Derivative (PID), Proportional-Integral (PI), and Proportional-Derivative (PD) control methods were used. The parameters of these controllers were tuned using Matlab Tuned, The Cheetah Optimizer (CO) Algorithm, a new generation heuristic optimization method, and Particle Swarm Optimization (PSO), a widely accepted optimization method. The performances of the controllers were determined using criteria such as Integral of Absolute Error (IAE), Integral Squared Error (ISE), and Integral of Time multiplied by Absolute Error (ITAE). According to the results obtained, it was found that the PID, PI and PD control parameters determined using the CO Algorithm performed better than the controllers created using Matlab Tuned and PSO methods. New optimization methods, such as the CO Algorithm, have been found to have significant potential to improve the performance of control systems. Thanks to this study, it offers a practical approach for optimizing DC motor speed control in industrial processes. As a result, it has been found that the control parameters determined by the CO Algorithm have significant potential in improving the performance of DC motor speed control and control systems compared to other optimization methods.

Research on the Control and Modulation Scheme for a Novel Five-Switch Current Source Inverter

Different from the voltage source inverter (VSI), the current source inverter (CSI) can boost the voltage and eliminate the additional passive filter and dead time. However, the DC-side inductor current is not a real current source and is generated by a DC voltage supply and an inductor. Under different switching states, the DC-side inductor will be charged or discharged, which leads to the DC-side inductor current being discontinuous or increasing. To solve the control problem of the DC-side inductor current of the CSI, a novel single-phase CSI topology with five switching tubes for grid-connected applications is proposed. Firstly, the reference calculation method and the hysteresis loop control scheme for the DC-side inductor current are proposed, and the adjustable and constant DC-side inductor current are obtained. Since the PWM signals cannot be directly implemented to the switching tubes, the modulation strategy for the single-phase CSI is proposed in this paper. Then, an active damping method based on the feedback capacitor voltage is presented to suppress the resonance peak caused by the LC filter on the grid side. Finally, the math model of the AC-side structure is established, and the optimal proportional-resonant controller parameters’ design method is explored by the amplitude–frequency characteristic curves. The simulation and experiment are implemented for the proposed CSI topology. The results show that a high-quality power with a good control performance can be obtained with the proposed CSI topology.

Optimal Controller Identification for multivariable non-minimum phase systems

This work deals with data-driven control for non-minimum phase (NMP) systems, where the goal is to design a controller for a plant whose model is unknown by using a batch of input–output data collected from it. The approach is based on the Model Reference paradigm, where a transfer function matrix – the reference model – is used to specify the desired closed-loop performance. The NMP systems issue in Model Reference approaches is a well-known problem in control literature and it is no different in data-driven methods. This work explains how to adapt the formulation of the Optimal Controller Identification (OCI) method to cope with this class of systems. Considering a convenient parametrization of the reference model and a flexible performance criterion, it is possible to identify the NMP transmission zeros of the plant along with the optimal controller parameters, as it will be shown. Both diagonal and block-triangular reference model structures are treated in detail. Simulation examples show the effectiveness of the proposed approach.

Optimization of the different controller parameters via OBL approaches based artificial ecosystem optimization involving fitness distance balance guiding mechanism for efficient motor speed regulation of DC motor

This study proposes a new optimization approach, which is called as artificial ecosystem optimization algorithm with fitness-distance balance guiding mechanism by using opposite based learning methods (FDBAEO_OBLs) for the speed regulation of direct current (DC) motor. The performance of the proposed FDBAEO_OBL algorithm is tested in two different experimental studies. In the first experimental study, the proposed approach is tested in the CEC2020 benchmark test functions and the FDBAEO algorithm, which included the best OBL approach, is determined using non-parametric Wilcoxon and Friedman statistical analysis methods. Second, the parameters of proportional integral derivative (PID), tilt integral derivative (TID), proportional integral derivative with filter (PIDF), tilt integral derivative with filter (TIDF), fractional-order proportional integral derivative (FOPID), fractional-order proportional integral derivative with filter (FOPIDF), proportional integral derivative with fractional-order filter (PIDFF) and fractional-order proportional integral derivative with fractional-order filter (FOPIDFF) controller structures to be used in DC motor closed loop speed control are determined with FDBAEO_OBL, and the performances of the controllers are investigated. Integral absolute error (IAE), integral time absolute error (ITAE), integral time squared error (ITSE) and integral squared error (ISE) performance indices are used as the objective function of the operation process in which the control parameters are determined. According to the comparative step response results of the controller structures, the four best controller structures for DC motor speed regulation are determined. The performances of these controllers are examined under different simulation conditions and according to the results obtained, it is seen that the best controller structure is FOPIDFF. The FDBAEO_OBL algorithm, which is used in both benchmark test functions and DC motor speed regulation, shows an effective, durable and superior performance in finding the optimal solution values during the optimization.

Tuning control parameters of underwater vehicle to minimize the influence of internal solitary waves

ISWs (Internal solitary waves) are waves that occur in stratified oceans, generating strong shear near the pycnocline. Encountering ISWs can cause a vehicle to experience the phenomenon of “falling deep”. Therefore, it is quite necessary to exert control on the vehicle in order to effectively avoid this phenomenon. This study establishes a numerical wave tank for ISWs based on the mKdV (modified Korteweg de Vries) theory. Additionally, a PD (Proportional Derivative) control method is developed for vehicles encountering ISWs in stratified fluids. The PD control parameters are designed and tuned, and the vehicle's control effectiveness, motion response and hydrodynamic characteristics, are analysed. The results suggest that this control method can effectively reduce the heave and pitch motions of the vehicle in different submergence depths and wave amplitudes. The pitch angle can be controlled within 0.2 rad for different control parameters. Better control can suppress changes within a range of 0.1 rad. To better evaluate the control effect, a new evaluation parameter σ is proposed, which takes into account both heave and pitch motions. After implementing control, the value of σmax significantly decreases. Optimal control parameters can reduce the value of σmax by about 90% compared to an uncontrolled vehicle.

Bending analysis of textile reinforced concrete sandwich panels: Experimental and numerical evaluation

Textile reinforced concrete (TRC) is a combination of a cementitious matrix and a textile reinforcement that has good mechanical properties. This makes a highly promising composite material for a wide range of construction practices. One notable application of this innovative composite deals with the production of thin sandwich panels. Hence it is noteworthy to examine the control parameters (depth & number of layers) that affect the mechanical properties of TRC sandwich panels with chosen fibre meshes and core material.Initially, 24 different TRC panels were cast and modelled with varying depths (45 mm, 55 mm and 65 mm) and layers (3, 4, 5 and 6), incorporating two different fibre mesh reinforcements. The flexural behavior of these panels was studied both experimentally and numerically using ABAQUS software. The optimized control parameters was obtained from the results was TRC panel of 5 layers of Aramid fibre mesh having 55 mm depth.Further exploration focused on the effect of overall depth variation on the flexural behavior of TRC sandwich panels. A panel with Aramid fibre reinforcement and a calcium silicate board core, featuring a 55 mm overall depth, exhibited a remarkable 69.49 % increase in bending stress compared to a panel with a depth of 118 mm. These findings underscore the significance of optimized control parameters, showcasing the potential for enhancing the mechanical performance of TRC sandwich panels in construction practices.

Asymmetrical Four-Phase 8/6 Switched Reluctance Motor for a Wide Constant Power Region

In this paper, the methodology for designing an asymmetrical four-phase 8/6 switched reluctance motor (SRM) that achieves approximately constant output power over a wide speed range is described. In an asymmetrical 8/6 SRM, orthogonal phase pairs are different in terms of the pole width and number of turns. The main comparison criterion between the asymmetrical and symmetrical 8/6 SRM is the power-speed characteristic, obtained for a given rated RMS phase current of the symmetrical drive. The obtained results demonstrate that the asymmetrical 8/6 SRM allows the shape of the power-speed characteristic to be modified, thereby extending the constant power region well beyond that of the symmetrical configuration with the same rated power level. To make a fair comparison between the asymmetrical and symmetrical 8/6 SRM drives, the converter volt-ampere rating, machine volume, slot fill factor, and ohmic losses per phase are kept constant in all analyzed cases. For determination of the optimal control parameters and maximal drive performance for both designs, the appropriate SRM mathematical model and differential evolution algorithm are used. The applied model includes all substantial non-linearities and mutual coupling between phases. The simulation results are verified using a Finite Element Method (FEM)-based model in the Ansys Electronics 2020 R2 software package.

Modeling of mechanical behavior of Agave sisalana fiber's yarn using the design of experiments method

The aim of this study is to propose a model for predicting the mechanical properties of natural sisal yarn based on mechanical strength of used fibers, unit twist of yarn and its density. To achieve this, a complete factorial design modelling method was used, based on a design of experiments approach, which is recommended today because of the ease it offers in taking into account the most complex phenomena. It was developed on the basis of laboratory tests. The model obtained predicts mechanical strength and elasticity modulus of sisal yarn, based on control parameters such as fiber strength, unit twist and yarn mass. This model is currently one of the most accurate tools for predicting and optimising the mechanical properties of sisal yarns. The iso-surface curves represented by this model, can be used to obtain the strength and elasticity modulus of sisal yarn during manufacture, by varying the optimum control parameters. This model is an original tool that can be recommended to the spinning and sisal fiber composite manufacturing industries and others, as it saves time and materials and ensures precision in the manufacture of yarns according to the field of application, thereby contributing to safety in use.

Analysis of the PID Controller Parameters for A Surface Mobility Platform Mobile Robot

Surface mobility control is a fundamental challenge in robotics, and its applications range from autonomous vehicles to industrial automation. The main aim of the paper is to analyze the PID controller parameters for the speed control of a surface mobility platform mobile robot. MATLAB simulation toolbox and mathematical model of the DC motor are used to find the optimal PID controller parameters to complete the DC motor speed control system. For the SMP motion control system, the mobile robot kinematic model is supported as a differential drive system. In this paper, the PID controller parameters for the DC motor control system are analyzed with simulation results using various KP, KI, KD gains. The experimental results of the SMP mobile robot with optimal PID controller parameters are analyzed by driving on the rough, slope and even terrains. According to simulation results and experimental tests, the proposed system effectively controls the speed of the SMP mobile robot, demonstrating satisfactory performance in settling time, rise time, and system overshoot. Among PI and PID controllers, the designed PID controller can quickly control to reach the desired DC motor speed. Using the optimal PID coefficient parameters, all motors reached their desired speed in 0.12 seconds during simulations. In the experimental tests on various terrain (even, slope and rough), all motors achieved their desired speed simultaneously within 25 seconds.Keywords: Differential drive, PID controller, motion trajectory, real-time speed control. skid steering, SMP mobile robot, surface mobility control

LOAD FREQUENCY CONTROL STRATEGY FOR MICROGRID SYSTEMS USING COMPUTATIONAL INTELLIGENCE OPTIMIZED PID CONTROLLER

This paper presents Load Frequency Control (LFC) strategy for Microgrid (MG) systems, proposing a strategy based on a PID optimized controller using computational intelligence (CI) technique. The MG system, relying solely on Renewable Energy Sources(RES) is modeled in SIMULINK. A novel multi-objective function comprising of Weighted Integral Square Error, WISE and Weighted Integral Absolute Square Error WIASE (WISE+WIASE) is proposed to enhance system performance. Using Particle Swarm Optimization (PSO) and Accelerated Particle Swarm Optimization (APSO), the Proportional, Integral and Derivative controller parameters are tuned. Simulation results revealed the superiority of the PSO-PID with the least steady state error of 4.1560e-07 and overshoot 0.0611 respectively over the APSO-PID with steady state error value of 4.8144e-07 and overshoot of 0.1334 respectively. PSO-PID also outperformed APSO-PID across other indices (ITAE, IAE, ITSE and ISE) making the PSO-PID to excel over the APSO-PID in terms of steady state error and overshoot. The controllers proved to be robust in various conditions with the PSO-PID controller exhibiting more robustness and stability than the APSO-PID controller. Comparing PSO and APSO algorithms, PSO outperformed APSO in terms of robustness because for five number of runs in both algorithms, the PSO algorithm presents a more quality and consistent results and also display a better convergence than the APSO algorithm . Conversely, APSO surpasses PSO in fast computational time and convergence speed. These findings underscore the effectiveness of the proposed LFC strategy for RES based Microgrid systems, providing valuable insights into comparative performance of PSO and APSO algorithms.

Multi-objective optimization of office egg shadings using NSGA-II to save energy consumption and enhance thermal and visual comfort

The construction industry's excessive energy usage necessitates practical solutions to address this pressing issue. Windows are crucial architectural elements as they allow the majority of natural light to enter a structure. The poorly designed window and related components cause the space to overheat, consuming more energy. However, it negatively impacts the building's thermal and visual comfort. The paper introduces a novel approach for optimizing control parameters for egg shadings through multi-objective methods. The recommended approach significantly reduces the building's energy consumption and enhances its thermal and visual comfort. EnergyPlus is a software utilized for conducting energy-related simulations. The JEPLUS software has therefore considered seven design variables, such as differences in the vertical and horizontal angles of the shade, length and horizontal dimensions, depth and vertical dimensions, and window positioning. These year-round simulations are performed in four different orientations for four different Iranian towns with various climates. The data is improved by using the program JEPLUS + EA. To find the best locations on the Pareto front, the data is optimized using the NSGA-II approach. The optimization results indicate that a lower angle improves visual comfort but takes more electricity for lighting. The findings of multi-objective optimization of controlled blind characteristics improved thermal and visual comfort with ranges of 5–45% and 55–100%, respectively, and lowered overall building energy consumption by 1–19% yearly, depending on the building's geographic orientation.

A High Andean Hydrocolloid Extracted by Microatomization: Preliminary Optimization in Aqueous Stability.

Aqueous suspensions rely on electrostatic interactions among suspended solids, posing a significant challenge to maintaining stability during storage, particularly in the food and pharmaceutical industries, where synthetic stabilizers are commonly employed. However, there is a growing interest in exploring new materials derived from natural and environmentally friendly sources. This study aimed to optimize the stability parameters of a novel Altoandino Nostoc Sphaericum hydrocolloid (NSH) extracted via micro atomization. Suspensions were prepared by varying the pH, gelatinization temperature and NSH dosage using a 23 factorial arrangement, resulting in eight treatments stored under non-controlled conditions for 20 days. Stability was assessed through turbidity, sedimentation (as sediment transmittance), ζ potential, particle size, color and UV-Vis scanning. Optimization of parameters was conducted using empirical equations, with evaluation based on the correlation coefficient (R2), average relative error (ARE) and X2. The suspensions exhibited high stability throughout the storage period, with optimized control parameters identified at a pH of 4.5, gelatinization temperature of 84.55 °C and NSH dosage of 0.08 g/L. Simulated values included turbidity (99.00%), sedimentation (72.34%), ζ potential (-25.64 mV), particle size (300.00 nm) and color index (-2.00), with simulated results aligning with practical application. These findings suggest the potential use of NSH as a substitute for commercial hydrocolloids, albeit with consideration for color limitations that require further investigation.

A novel adaptive trajectory tracking control for complex environments based on accelerated back-propagation neural network

This work addresses the issue of trajectory tracking accuracy degradation for vehicle dynamics parameters and road conditions changing, an adaptive trajectory tracking controller is designed for the intelligent vehicle. Based on the yaw dynamics model, a model predictive controller (MPC) is developed for the trajectory tracking process. Meanwhile, the impacts of different control parameters on trajectory tracking at various speeds are compared. And the change rule between optimal control parameter values and reference speeds is determined by cubic polynomial fitting to ensure minimal error during the trajectory following process. Then, an estimator for vehicle tire cornering stiffness and road adhesion coefficient is designed and tested based on the accelerated back-propagation neural network (ABPNN) model, with its estimation values being compared to those obtained through recursive least square method. Last, a co-simulation platform combining MATLAB/Simulink and CarSim software is established to conduct the trajectory tracking experiment under variable working conditions. Experimental results show that the proposed adaptive controller not only exhibits high accuracy in trajectory tracking but also demonstrates excellent stability and adaptability under medium to low speed conditions, particularly when encountering changing road conditions.

Research on low-energy consumption automatic real-time regulation of cascade gates and pumps in open-canal based on reinforcement learning

ABSTRACT Cascade gates and pumps are common hydraulic structures in the open-canal section of water transfer projects, characterized by high energy consumption and substantial costs, making it challenging to regulate. By implementing cascade gates regulation to control the hydraulic process, lift distribution of pump stations can be optimized, thus enhancing operational efficiency and reducing energy consumption. However, the selection of control models and parameter optimization is difficult because hydraulic processes are nonlinear, high-dimensional, large hysteresis, strong coupling, and time-varying. This study considers minimum energy consumption of pump stations as the regulation objective and employs the reinforcement learning (RL) algorithm for optimization regulation (OR) within a typical canal section of the Jiaodong Water Transfer Project. Our results demonstrate that after regulating, OR can precisely control the water level to achieve the high efficiency lift interval of pump station, enhancing efficiency by 4.12–6.02% compared to previous operation. Moreover, using optimized hyperparameters group, the RL model proves robust under different work conditions. The proposed method is suitable for complex hydraulic processes, highlighting its potential to support more effective decision-making in water resources regulation.

ÜÇ SӘRBӘSTLİK DӘRӘCӘLİ ROBOT MANİPULYATOR SİSTEMİ ÜÇÜN OPTİMAL PİD TӘNZİMLӘYİCİSİNİN MODELLӘŞDİRİLMӘSİ

. In this research work, the application and analysis of the optimization methodology based on the dynamic flying objects algorithm to design the robot manipulator based on the optimal PID controller is considered. Based on the dynamic analysis of robot manipulators, the interaction between the torques created by the actuators and the position and speed of the manipulator was investigated. The optimal PID control law obtained from the proposed algorithm is applied to the robot system. The proposed controller optimizes the trajectory of the robot's end-effector for input during variable time and hardens the robot against perturbing effects. To achieve a highly adaptive optimization process, the correct formulation of the utility function leads to optimal solutions. Three different objective functions were used in the process of optimization of control parameters in the robot system and their results were compared. In this work, a new debugging methodology for trajectory tracking in robotic manipulator systems is presented. Optimum benefit is obtained by using the proposed PID regulation law. The obtained results are satisfactory and competitive. It has been determined that the problems of the smooth control system in the places where the robot system is highly non-linear are regulated by classical methods.

Dynamic analysis and optimal control of knowledge diffusion model in regional innovation ecosystem under digitalization

Knowledge diffusion in regional innovation ecosystems is an important factor that influences the regional innovation efficiency. In regional innovation ecosystems under digital empowerment, the knowledge diffusion enables the optimal allocation of innovation resources and promotes the sustainable development and ecological evolution of regional innovation ecosystems. In this paper, a SEIR (Susceptible–Exposed–Infected–Recovered) model is proposed for knowledge diffusion in regional innovation ecosystems under digitization. The basic reproduction number of the proposed model is calculated and its stability is validated. Finally, the expressions for the optimal control system and the optimal control parameters are presented. According to the research conclusions of this paper, the knowledge-diffusion ability of innovation agents in an innovation ecosystem affects the knowledge diffusion in a system; the contact rate between innovation agents affects the efficiency of knowledge diffusion in the system and the structure of the system; the digital transmission ability of innovation agents affects the breadth of knowledge diffusion in the system; and the self-learning ability of innovation agents affects the efficiency of knowledge diffusion in the system.The digital technologies help heterogeneous innovation agents in regional innovation ecosystems to break down the knowledge silos. At the same time, the digital technologies enhance the ability of innovation agents to absorb and learn knowledge in regional innovation ecosystems under digitization, thereby increasing the infection rate of knowledge diffusion in such systems.These conclusions extend the theoretical boundaries of innovation ecosystems and knowledge diffusion and offer management implications for enterprises and governments in decision-making.

Control of a painting robot containing parallelogram linkages based on its distinctive dynamic characteristics

To achieve high-performance control, the dynamics and control should be closely integrated. This paper focuses on dynamic-based control design for a hybrid painting robot containing parallelogram linkages to improve its tracking accuracy and motion consistency. The study derives the dynamics of the parallelogram component to investigate its distinctive attributes, which include time invariance, joint independence, and linear approximability. Based on these unique characteristics, a novel theory-experiment mixed two-step method for control design is presented. It involves identifying and visualizing an optimized space of control parameters through theoretical deduction, followed by determining the optimal control parameters within this space through experimental trials. Experimental results underscore a substantial improvement in the tracking accuracy and motion consistency of the painting robot.

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This article addresses the bifurcation characteristics and vibration reduction of a 2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2$$\\end{document}-DOF dynamical system simulating the nonlinear oscillation of an asymmetric rotor model subjected to simultaneous multiparametric and external excitations. To suppress the system's vibrations, two 1/2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1/2$$\\end{document}-DOF active dampers are attached to the system in linear and cubic nonlinear forms via a magnetic coupling actuator. The closed-loop system model is derived as two differential equations with multi-control terms, including cubic, quantic, and septic, coupled nonlinearly to two first-order systems. Applying perturbation theory, the system model is solved, and the autonomous system describing the closed-loop slow-flow dynamics is obtained. Through numerical algorithms, the motion bifurcation is analyzed using various tools such as 2D and 3D bifurcation diagrams, two-parameter stability charts, basins of attraction, orbit plots, and time response profiles. The analytical investigations confirm that the uncontrolled model behaves like a hardening Duffing oscillator with multistability characteristics, displaying simultaneous mono-stable, bi-stable, tri-stable, or quadri-stable periodic oscillations depending on both the asymmetric nonlinearities and angular velocity. Subsequently, the influence of different control parameters is analyzed to determine the threshold between mono and multi-stability conditions. Finally, optimal control parameters are designed to eliminate multistability characteristics and achieve minimum and safe vibration levels.

Torque fault compensation in electric vehicle switched reluctance motor drives: A jellyfish search optimization method

AbstractIn this paper, an enhanced indirect instantaneous‐torque‐control is proposed based on the torque sharing function approach of switched reluctance motor drives for electric vehicles by employing the jelly fish search. The major goal is to attain vehicle desires that include minimal torque ripple, maximum torque per ampere (MTPA), and huge performance and extend speed limit. First, a simplest analytic design is developed a determine more proficient turn‐on angle for the torque product. Second, an altered torque sharing function (TSF) is used for compensating the faults of torque tracking. The proposed technique is calculated to represent an accurate switched reluctance motor and its magnetized features. They have worked to create the machine model and execute the necessary transmits. The torque fault is evaluated and compensated inside the torque sharing function. The adapting TSF is compensates for the torque fault with receiving the phase because it is the minimal flux rate connecting variation. Finally, the jellyfish search technique is accepted to determine the optimal control parameters. The proposed strategies are done in MATLAB and its performance is contrasted with different existing strategies. According to the simulation result, the proposed strategy‐based accuracy is 94.2% at 50 iteration and 80% at 100th iteration which is higher than the existing methods. From this analyses, it proved that the proposed technique gives superior performance to existing one.