scholarly journals Grey Wolf and Weighted Whale Algorithm Optimized IT2 Fuzzy Sliding Mode Backstepping Control with Fractional-Order Command Filter for a Nonlinear Dynamic System

2021 ◽  
Vol 11 (2) ◽  
pp. 489
Author(s):  
Seongik Han
Keyword(s):  
Fuzzy Logic ◽  
Fractional Order ◽  
Optimization Algorithm ◽  
Sliding Mode ◽  
Fuzzy Logic System ◽  
Backstepping Control ◽  
Whale Optimization Algorithm ◽  
Grey Wolf ◽  
Whale Optimization ◽  
Logic System

In this study, a fractional-order sliding mode backstepping control method was proposed, which involved the use of a fractional-order command filter, an interval type-2 fuzzy logic system approximation method, and a grey wolf and weighted whale optimization algorithm for multi-input multi-output nonlinear dynamic systems. For designing the stabilizing controls of the backstepping control, a novel fractional-order sliding mode surface was suggested. Further, the transformed errors that occurred during the recursive design steps were easily compensated by the controllers constructed using a new fractional-order command filter. Thus, the differentiation issue of the virtual control in the conventional backstepping control design could be bypassed with a simpler controller structure. Subsequently, the unknown plant dynamics were approximated by an interval type-2 fuzzy logic system. The uncertainties, such as the approximation error and the external disturbance, were compensated by the fractional-order sliding mode control that was added in the backstepping controller. Furthermore, the controller parameters and the fuzzy logic system were optimized via a grey wolf and weighted whale optimization algorithm to obtain a faster tuning process and an improved control performance. Simulation results demonstrated that the fractional-order sliding mode backstepping control scheme provides enhanced control performance over the conventional backstepping control system. Thus, in this paper, a fractional-order sliding mode surface and fractional-order backstepping control are studied, which provide more rapid convergence and enhanced robustness. Furthermore, a hybrid grey wolf and weighted whale optimization algorithm are proposed to provide an improved learning performance than those of conventional grey wolf optimization and weighted whale optimization methods.

Dynamic characterization of a master–slave robotic manipulator using a hybrid grey wolf–whale optimization algorithm

2021 ◽  
pp. 107754632110034
Author(s):  
Ololade O Obadina ◽  
Mohamed A Thaha ◽  
Kaspar Althoefer ◽  
Mohammad H Shaheed
Keyword(s):  
Optimization Algorithm ◽  
Hybrid Algorithm ◽  
Robot Manipulator ◽  
Moving Average ◽  
Identification Problem ◽  
Whale Optimization Algorithm ◽  
Grey Wolf Optimizer ◽  
Parametric Modelling ◽  
Grey Wolf ◽  
Whale Optimization

This article presents a novel hybrid algorithm based on the grey-wolf optimizer and whale optimization algorithm, referred here as grey-wolf optimizer–whale optimization algorithm, for the dynamic parametric modelling of a four degree-of-freedom master–slave robot manipulator system. The first part of this work consists of testing the feasibility of the grey-wolf optimizer–whale optimization algorithm by comparing its performance with a grey-wolf optimizer, whale optimization algorithm and particle swarm optimization using 10 benchmark functions. The grey-wolf optimizer–whale optimization algorithm is then used for the model identification of an experimental master–slave robot manipulator system using the autoregressive moving average with exogenous inputs model structure. Obtained results demonstrate that the hybrid algorithm is effective and can be a suitable substitute to solve the parameter identification problem of robot models.

scholarly journals Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)

2019 ◽  
Vol 9 (18) ◽  
pp. 3755 ◽  
Author(s):  
Wei Chen ◽  
Haoyuan Hong ◽  
Mahdi Panahi ◽  
Himan Shahabi ◽  
Yi Wang ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Landslide Susceptibility ◽  
Heuristic Algorithms ◽  
Whale Optimization Algorithm ◽  
Economic Losses ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Prediction Ability ◽  
Inference System ◽  
Whale Optimization

The most dangerous landslide disasters always cause serious economic losses and human deaths. The contribution of this work is to present an integrated landslide modelling framework, in which an adaptive neuro-fuzzy inference system (ANFIS) is combined with the two optimization algorithms of whale optimization algorithm (WOA) and grey wolf optimizer (GWO) at Anyuan County, China. It means that WOA and GWO are used as two meta-heuristic algorithms to improve the prediction performance of the ANFIS-based methods. In addition, the step-wise weight assessment ratio analysis (SWARA) method is used to obtain the initial weight of each class of landslide influencing factors. To validate the effectiveness of the proposed framework, 315 landslide events in history were selected for our experiments and were randomly divided into the training and verification sets. To perform landslide susceptibility mapping, fifteen geological, hydrological, geomorphological, land cover, and other factors are considered for the modelling construction. The landslide susceptibility maps by SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-WOA, and SWARA-ANFIS-GWO models are assessed using the measures of the receiver operating characteristic (ROC) curve and root-mean-square error (RMSE). The experiments demonstrated that the obtained results of modelling process from the SWARA to the SAWRA-ANFIS-GWO model were more accurate and that the proposed methods have satisfactory prediction ability. Specifically, prediction accuracy by area under the curve (AUC) of SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-GWO, and SWARA-ANFIS-WOA models were 0.831, 0.831, 0.850, 0.856, and 0.869, respectively. Due to adaptability and usability, the proposed prediction methods can be applied to other areas for landslide management and mitigation as well as prevention throughout the world.

A parameter estimation method for fractional-order nonlinear systems based on improved whale optimization algorithm

2019 ◽  
Vol 33 (07) ◽  
pp. 1950075 ◽  
Author(s):  
Gong Ren ◽  
Renhuan Yang ◽  
Renyu Yang ◽  
Pei Zhang ◽  
Xiuzeng Yang ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Fractional Order ◽  
Optimization Algorithm ◽  
Fitness Function ◽  
Whale Optimization Algorithm ◽  
Engineering System ◽  
Grasshopper Optimization Algorithm ◽  
Whale Optimization ◽  
System Characteristics

Compared to the integer-order systems, the system characteristics of the fractional system are closer to the system characteristics of the real engineering system, the study found beyond that, strictly speaking, various physical phenomena in nature are nonlinear. The problem of parameter estimation problem of fractional-order nonlinear systems can be transformed into the problem of parameter optimization problem by constructing an appropriate fitness function. This paper proposes a hybrid improvement algorithm based on whale optimization algorithm (WOA) to solve this problem and verify it both in Lorenz system and Lu system. The simulation result shows that the hybrid improved algorithm is superior to genetic algorithm (GA), particle swarm optimization (PSO), grasshopper optimization algorithm (GOA) and WOA in convergence speed and accuracy.

Upgraded Whale Optimization Algorithm for fuzzy logic based vibration control of nonlinear steel structure

2019 ◽  
Vol 192 ◽  
pp. 53-70 ◽  
Author(s):  
Mahdi Azizi ◽  
Reza Goli Ejlali ◽  
Seyyed Arash Mousavi Ghasemi ◽  
Siamak Talatahari
Keyword(s):  
Fuzzy Logic ◽  
Vibration Control ◽  
Optimization Algorithm ◽  
Steel Structure ◽  
Whale Optimization Algorithm ◽  
Whale Optimization

scholarly journals An Adaptive Control of Fractional-Order Nonlinear Uncertain Systems with Input Saturation

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Changhui Wang ◽  
Mei Liang ◽  
Yongsheng Chai
Keyword(s):  
Fuzzy Logic ◽  
Fractional Order ◽  
Uncertain Systems ◽  
Input Saturation ◽  
Fuzzy Logic System ◽  
Distributed Model ◽  
Unknown Parameters ◽  
External Disturbances ◽  
Nonlinear Uncertain Systems ◽  
Logic System

This paper develops a fractional-order adaptive fuzzy backstepping control scheme for incommensurate fractional-order nonlinear uncertain systems with external disturbances and input saturation. Based on backstepping algorithm, the fuzzy logic system is used to approximate the unknown nonlinear uncertainties in each step of the backstepping, and the fractional-order parameters update laws for fuzzy logic system, unknown parameters, and the external disturbances are proposed. With the aids of the frequency distributed model of fractional integrator for the fractional-order systems in the procedure of controller design, the stability of the closed-loop system is established. To verify the effectiveness and robustness of the proposed controller, two simulation examples are demonstrated at last.

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