A Novel Rank-order-centroid Based Reduction of Self-balanced-bicycle-robot Controller Using Grey-wolf Optimizer

Yadav, Umesh Kumar; Meena, V. P.; Singh, V. P.

doi:10.1007/s10846-022-01758-2

Cited by 12 publications

(6 citation statements)

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“…ROC method offers a straightforward approach to determining weights 13 . It involves ranking multiple solutions (objectives) based on their significance and assigning weights accordingly.…”

Section: Roc Methodsmentioning

confidence: 99%

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Investigation of rank order centroid method for optimal generation control

Varshney,

Waghmare,

Singh

et al. 2024

Sci Rep

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Multi-criteria decision-making (MCDM) presents a significant challenge in decision-making processes, aiming to ascertain optimal choice by considering multiple criteria. This paper proposes rank order centroid (ROC) method, MCDM technique, to determine weights for sub-objective functions, specifically, addressing issue of automatic generation control (AGC) within two area interconnected power system (TAIPS). The sub-objective functions include integral time absolute errors (ITAE) for frequency deviations and control errors in both areas, along with ITAE of fluctuation in tie-line power. These are integrated into an overall objective function, with ROC method systematically assigning weights to each sub-objective. Subsequently, a PID controller is designed based on this objective function. To further optimize objective function, Jaya optimization algorithm (JOA) is implemented, alongside other optimization algorithms such as teacher–learner based optimization algorithm (TLBOA), Luus–Jaakola algorithm (LJA), Nelder–Mead simplex algorithm (NMSA), elephant herding optimization algorithm (EHOA), and differential evolution algorithm (DEA). Six distinct case analyses are conducted to evaluate controller’s performance under various load conditions, plotting data to illustrate responses to frequency and tie-line exchange fluctuations. Additionally, statistical analysis is performed to provide further insights into efficacy of JOA-based PID controller. Furthermore, to prove the efficacy of JOA-based proposed controller through non-parametric test, Friedman rank test is utilized.

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“…ROC method offers a straightforward approach to determining weights 13 . It involves ranking multiple solutions (objectives) based on their significance and assigning weights accordingly.…”

Section: Roc Methodsmentioning

confidence: 99%

“…Ribeiro et al 12 utilized the ROC method to select appropriate current transducers for a smart plug project development. Yadav et al proposed a novel approach involving the use of a grey-wolf optimizer and ROC-based technique to reduce the order of robotic-cycle controller 13 .…”

mentioning

confidence: 99%

Investigation of rank order centroid method for optimal generation control

Varshney,

Waghmare,

Singh

et al. 2024

Sci Rep

Self Cite

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“…The use of the Grey-wolf optimizer algorithm (Yadav et al 2022) and Cuckoo search (Sikander & Prasad 2019) to obtain the kth reduced-order controller requires optimizing "2k + 1" decision variables. It in uences the computational burden and convergence speed of the algorithm.…”

Section: Limitations Of Existing Techniquesmentioning

confidence: 99%

“…Controlling the balance of a two-wheeled mobile robot is a well-known robotics task. In every discipline of robotic design, system accuracy, and precision are crucial and basic needs (Sikander and Prasad, 2019;Yadav et al, 2022). Bicycle balancing is a challenging control problem because it is an unstable system with numerous sources of uncertainty due to unmodeled dynamics, parameter uctuations, and external disturbances (Bui & Parnichkun, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Particle swarm optimization-based reduced-order controller for balancing control issues with a two-wheeled mobile robot

Duddeti¹

2023

Preprint

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Existing controllers for a two-wheel mobile robot (TWMR) are based on higher-order controller (HOC) structures, which are complex and challenging to analyze, synthesize, and implement on hardware. To address this issue, the authors propose a reduced-order controller (ROC) for the pre-specified HOC that can effectively handle unpredictable dynamics. The proposed approach involves two phases. In phase 1, the ROC is made using dominant poles and moment matching. In phase 2, the particle swarm optimization (PSO) method improves the ROC numerator coefficients by minimizing the root mean square error and integral square error between the step responses and Bode magnitude plots of the HOC and ROC. One benefit of this technique is that the PSO algorithm’s search space bounds are not entirely arbitrary. They are instead picked based on the numerator coefficients calculated in Phase 1. It overcomes issues with evolutionary algorithms, such as random search space selection, optimization of additional decision variables, and longer simulation duration. Comparisons are made between the performance of the proposed controller and controllers built using other approaches. The determinations reveal that the proposed ROC outperforms these current techniques, resulting in a more straightforward and effective solution for regulating the unstable TWMR system. The efficacy of higher- and lower-order controllers is evaluated using a perturbed two-wheeled mobile robot. This evaluation focuses on analyzing various time response parameters and performance indices, including integral time absolute error (ITAE), integral square error (ISE), and integral absolute error (IAE). The MATLAB environment is employed as the preferred tool to carry out these simulations.

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A schematic study of dimension reduction techniques for complex high-order integer, fractional and interval systems

Goel,

Manocha

2024

Electr Eng

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A Novel Rank-order-centroid Based Reduction of Self-balanced-bicycle-robot Controller Using Grey-wolf Optimizer

Cited by 12 publications

References 50 publications

Investigation of rank order centroid method for optimal generation control

Investigation of rank order centroid method for optimal generation control

Particle swarm optimization-based reduced-order controller for balancing control issues with a two-wheeled mobile robot

A schematic study of dimension reduction techniques for complex high-order integer, fractional and interval systems

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