Optimal dynamic design of planar mechanisms using teaching–learning-based optimization algorithm

Chaudhary, Kailash; Chaudhary, Himanshu

doi:10.1177/0954406215612831

Cited by 6 publications

(4 citation statements)

References 37 publications

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“…Therefore, extensive research has been conducted on swarm intelligence algorithms in recent years. Inspired by the laws underlying the development of natural things, some examples of these algorithms are the teaching and learning optimization algorithm (TLBO) [1], the positive chord algorithm (SCA) [2,3], the particle swarm optimization (PSO) [4][5][6], and the genetic algorithm (GA) [7,8]. They can also be inspired by the collective or social intelligence of natural biology, as in the case of the Harris hawks algorithm (HHO) [9,10], the artificial fish swarm algorithm (FSA) [11], the sparrow search algorithm (CSA) [12][13][14], and the gray wolf optimization algorithm (GWO) [15].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Strategy Improved Arithmetic Optimization Algorithm

Liu

Pang

et al. 2022

Symmetry

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To improve the performance of the arithmetic optimization algorithm (AOA) and solve problems in the AOA, a novel improved AOA using a multi-strategy approach is proposed. Firstly, circle chaotic mapping is used to increase the diversity of the population. Secondly, a math optimizer accelerated (MOA) function optimized by means of a composite cycloid is proposed to improve the convergence speed of the algorithm. Meanwhile, the symmetry of the composite cycloid is used to balance the global search ability in the early and late iterations. Thirdly, an optimal mutation strategy combining the sparrow elite mutation approach and Cauchy disturbances is used to increase the ability of individuals to jump out of the local optimal. The Rastrigin function is selected as the reference test function to analyze the effectiveness of the improved strategy. Twenty benchmark test functions, algorithm time complexity, the Wilcoxon rank-sum test, and the CEC2019 test set are selected to test the overall performance of the improved algorithm, and the results are then compared with those of other algorithms. The test results show that the improved algorithm has obvious advantages in terms of both its global search ability and convergence speed. Finally, the improved algorithm is applied to an engineering example to further verify its practicability.

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Section: Introductionmentioning

confidence: 99%

A Multi-Strategy Improved Arithmetic Optimization Algorithm

Liu

Pang

et al. 2022

Symmetry

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“…Practical ways to move the CoM of the system and to make it stationary have been to add a set of counterweights [2][3][4][5], to add auxiliary structures [6][7][8][9], or to modify the form of the links from the early design phase [10][11][12]. A comprehensive review of the methods with illustrative examples can be seen in Reference [13].…”

Section: Introductionmentioning

confidence: 99%

An Alternative Method for Shaking Force Balancing of the 3RRR PPM through Acceleration Control of the Center of Mass

et al. 2020

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The problem of shaking force balancing of robotic manipulators, which allows the elimination or substantial reduction of the variable force transmitted to the fixed frame, has been traditionally solved by optimal mass redistribution of the moving links. The resulting configurations have been achieved by adding counterweights, by adding auxiliary structures or, by modifying the form of the links from the early design phase. This leads to an increase in the mass of the elements of the mechanism, which in turn leads to an increment of the torque transmitted to the base (the shaking moment) and of the driving torque. Thus, a balancing method that avoids the increment in mass is very desirable. In this article, the reduction of the shaking force of robotic manipulators is proposed by the optimal trajectory planning of the common center of mass of the system, which is carried out by "bang-bang" profile. This allows a considerable reduction in shaking forces without requiring counterweights, additional structures, or changes in form. The method, already presented in the literature, is resumed in this case using a direct and easy to automate modeling technique based on fully Cartesian coordinates. This permits to express the common center of mass, the shaking force, and the shaking moment of the manipulator as simple analytic expressions. The suggested modeling procedure and balancing technique are illustrated through the balancing of the 3RRR planar parallel manipulator (PPM). Results from computer simulations are reported.

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“…*One of the fundamental problems in the theory of mechanisms is the determination of the acceleration of individual members of the mechanism, as well as unknown forces in the joints, depending on the forces acting on the mechanism. To solve such problems, the general laws of body dynamics are generally applied (Beer and Johnston, 1997;Goldstein, 1991;Pytel and Kiusalaas, 1996;Chaudhary and Chaudhary, 2016).…”

Section: Introductionmentioning

confidence: 99%

Determination of angular acceleration of the rotating driving member of planar mechanisms by the method of guessing

Voloder¹,

Herzegovina²

2020

Int. j. adv. appl. sci.

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In this paper, we present a novel method for finding an unknown angular acceleration of the driving member of the planar hinged-arm mechanism, which is based on the reduction of the mechanism and quite arbitrary guessing the value of angular acceleration. Using this method, it is possible to directly determine the angular acceleration of the driving rotary member of the mechanism without the need to calculate the kinematic characteristics of the other members. The presented method can be applied to all planar mechanisms. The procedure used in this method is much shorter than in the case of the general laws of system dynamics. The solution obtained by this method is independent of the assumed initial solution, with the exception that the assumed solution cannot be zero.

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Optimal dynamic design of planar mechanisms using teaching–learning-based optimization algorithm

Cited by 6 publications

References 37 publications

A Multi-Strategy Improved Arithmetic Optimization Algorithm

A Multi-Strategy Improved Arithmetic Optimization Algorithm

An Alternative Method for Shaking Force Balancing of the 3RRR PPM through Acceleration Control of the Center of Mass

Determination of angular acceleration of the rotating driving member of planar mechanisms by the method of guessing

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