Controller design for underwater robotic vehicle based on improved whale optimization algorithm

Hasan, Mustafa Wassef; Abbas, Nizar Hadi

doi:10.11591/eei.v10i2.2288

Cited by 8 publications

(3 citation statements)

References 22 publications

(25 reference statements)

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“…Table 1 shows the results of the chosen benchmarking functions. According to the above, there is only one local minima value for unimodal test functions, but multimodal test functions have multiple local minima values as the number of task dimensions increases [24]. However, based on the above result, it is clear that unimodal test functions (F1, F2, F3, and F4), multimodal test functions (F5, F6, F7, and F8), and fixed multimodal test functions (F9, F10, F11, and F12) tend to reach towards more optimal minimum value than that HHO and SSA.…”

Section: Proposed Algorithm Evaluationmentioning

confidence: 96%

Adaptive Optimal Trajectory Tracking Control for Four Flexible Joint Robot Manipulator

2022

IJIES

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The Swarm Intelligence Optimization Methods were widely used in science. Animals' swarming behaviour mainly inspires these methods. Conventional optimization for Harris Hawks (HHO) is a recent technique based on the hunting strategy of harris hawks. The parallelism technique was used in this study to try to improve the original version of the algorithm. The improved algorithm sets the parameters of two controllers designed to improve the 4-link flexible joint manipulator's dynamic performance. The integral sliding mode controller controls the flexible joints, and the adaptive output feedback tracking controller controls the rigid links. When the improved algorithm is tested on benchmark functions and compared to other optimization strategies. the results show that the improved algorithm is faster and more precise. The simulation results indicate the improved algorithm's capabilities, with an enhancement percentage of 76.72 percent for the first link and 70.91 percent for the first joint compared to the standard approach.

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Section: Proposed Algorithm Evaluationmentioning

confidence: 96%

Adaptive Optimal Trajectory Tracking Control for Four Flexible Joint Robot Manipulator

2022

IJIES

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“…According to the Figure 2, there is only one local minima value for unimodal test functions, but multimodal test functions have multiple local minima values as the number of task dimensions increases [26]. However, based on the above result, it is clear that unimodal test functions (F1, F2, F3, and F4) multimodal test functions (F5, F6, F7, and F8) fixed multimodal test functions (F9, F10, F11, and F12) tend to reach towards more optimal minimum value than that SSA and GWO.…”

Section: Proposed Algorithm Evaluationmentioning

confidence: 99%

Optimal integral sliding mode controller controller design for 2-RLFJ manipulator based on hybrid optimization algorithm

Yahya

Abbas

2022

IJECE

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A newly hybrid nature-inspired algorithm called HSSGWOA is presented with the combination of the salp swarm algorithm (SSA) and grey wolf optimizer (GWO). The major idea is to combine the salp swarm algorithm's exploitation ability with the grey wolf optimizer's exploration ability to generate both variants' strength. The proposed algorithm uses to tune the parameters of the integral sliding mode controller (ISMC) that design to improve the dynamic performance of the two-link flexible joint manipulator. The efficiency and the capability of the proposed hybrid algorithm are evaluated based on the selected test functions. It is clear that when compared to other algorithms like SSA, GWO, differential evolution (DE), gravitational search algorithm (GSA), particles swarm optimization (PSO), and whale optimization algorithm (WOA). The ISMC parameters were tuned using the SSA, which was then compared to the HSSGWOA algorithm. The simulation results show the capabilities of the proposed algorithm, which gives an enhancement percentage of 57.46% compared to the standard algorithm for one of the links, and 55.86% for the other.

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“…WOA has high performance to solve multiple optimization tasks. However, it has some convergence problem [41]- [45]. For this reason, the BAT algorithm may be implied with WOA to enhance exploration capability of WOA [42].…”

Section: Introductionmentioning

confidence: 99%

Hybrid algorithm for cloud-fog system based load balancing in smart grids

Saoud

Recioui

2022

Bulletin EEI

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Energy management is among the key components of smart metering. Its role is to balance energy consumption and distribution. Smart devices integration results in a huge data exchange between different parts of the smart grid causing a delay in the response and processing time. To overcome this latency issue, the cloud computing has been proposed. However, cloud computing does not perform well when there are large distances from the cloud to the consumers. Fog computing solves this issue. In this paper, a cloud-fog computing system is presented to achieve an accurate load balancing. The hybridization of whale optimization algorithm with bat algorithm (WOA-BAT) is proposed for load balancing. The model performance is compared to state of art load balancing techniques as throttled, round robin, whale and particle swarm optimization algorithms in terms of processing and the response time. The results reveal that the proposed WOA-BAT has better results in terms of response time than the three algorithms with 4.3% improvement compared to RR and TH. It also outperforms all the algorithms in terms of processing time by at least 22.3%.

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Controller design for underwater robotic vehicle based on improved whale optimization algorithm

Cited by 8 publications

References 22 publications

Adaptive Optimal Trajectory Tracking Control for Four Flexible Joint Robot Manipulator

Adaptive Optimal Trajectory Tracking Control for Four Flexible Joint Robot Manipulator

Optimal integral sliding mode controller controller design for 2-RLFJ manipulator based on hybrid optimization algorithm

Hybrid algorithm for cloud-fog system based load balancing in smart grids

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