Discrete optimisation of a gear train using biogeography based optimisation technique

Savsani, Vimal; Rao, R. Venkata; Vakharia, D. P.

doi:10.1504/ijde.2009.028652

Cited by 14 publications

(8 citation statements)

References 18 publications

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“…3,4,5,6,7,8,15,16,17,18,19 and 20 for joint angles 9,10,11,12,13,14) obtained from the model are very close to the desired trajectory of the PUMA's arm.…”

Section: Simulink Simulation Resultssupporting

confidence: 51%

“…BBO has been applied to several real-world problems. In addition to experimental robot control tuning, as discussed in this paper and in [9], BBO has been applied to aircraft engine sensor selection [8], power system optimization [10,11], groundwater detection [12], mechanical gear train design [13], satellite image classification [14], and neuro-fuzzy system training for biomedical applications [15]. Recent research in the area of BBO has focused on putting it on a firm theoretical and mathematical foundation, including the derivation of Markov models [16,17] and dynamic system models [18] that describe its behavior.…”

Section: Introductionmentioning

confidence: 99%

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Multi-objective biogeography-based optimization technique for tuning PUMA 560’S controller

Chouraqui

Benzater

2014

Nonlinear Dyn

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Biogeography-based optimization (BBO) is one of the recently developed population-based algorithms which has shown impressive performance over evolutionary algorithms. BBO is based on the study of geographical distribution of biological organisms over space and time. In this paper, non-dominated sorting BBO (NSBBO) is proposed to tune proportionalderivative control system for the six degree arm manipulator PUMA 560. The BBO algorithm is based on mathematical models of biogeography, which describe the migration of species between habitats. To tune six PD controller of a PUMA 560 arm manipulator, we need to minimize simultaneously six position errors so there exists a multi-objective optimization problem. The NSBBO algorithm searches for the controller gains, so that integral absolute error in joint space is minimized. Results obtained show the effectiveness of the algorithm to optimize the controlling parameters and minimizing the error.

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“…3,4,5,6,7,8,15,16,17,18,19 and 20 for joint angles 9,10,11,12,13,14) obtained from the model are very close to the desired trajectory of the PUMA's arm.…”

Section: Simulink Simulation Resultssupporting

confidence: 51%

Section: Introductionmentioning

confidence: 99%

Multi-objective biogeography-based optimization technique for tuning PUMA 560’S controller

Chouraqui

Benzater

2014

Nonlinear Dyn

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show abstract

“…Biogeography-based optimization (BBO) was first presented in [45] and is an example of how a natural process can be generalized to solve optimization problems. Since its introduction, it has been applied to a variety of problems, including sensor selection [45], power system optimization [38,42], groundwater detection [24], mechanical gear train design [43], and satellite image classification [36].…”

Section: Introductionmentioning

confidence: 99%

Analytical and numerical comparisons of biogeography-based optimization and genetic algorithms

Simon

Rarick

Ergezer

et al. 2011

Information Sciences

110

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“…BBO has been applied to several real-world problems. In addition to experimental robot control tuning, as discussed in this paper and in [4], BBO has been applied to aircraft engine sensor selection [2], power system optimization [12,13], groundwater detection [14], mechanical gear train design [15], satellite image classification [16], and neuro-fuzzy system training for biomedical applications [17]. Recent research in the area of BBO has focused on putting it on a firm theoretical foundation, including the derivation of Markov models [18,19] and dynamic system models [20] that describe its behavior.…”

Section: Bbomentioning

confidence: 99%

Fuzzy Robot Controller Tuning with Biogeography-Based Optimization

Thomas

Lozovyy

Simon

2011

Lecture Notes in Computer Science

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Abstract. Biogeography-based optimization (BBO) is an evolutionary algorithm (EA) based upon the models of biogeography, which describe the relationship between habitat suitability and the migration of species across habitats. In this work, we apply BBO to the problem of tuning the fuzzy tracking controller of mobile robots. This is an extension of previous work, in which we used BBO to tune a proportional-derivative (PD) controller for these robots. We show that BBO can successfully tune the shape of membership functions for a fuzzy controller with both simulation and real world experimental results.

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Discrete optimisation of a gear train using biogeography based optimisation technique

Cited by 14 publications

References 18 publications

Multi-objective biogeography-based optimization technique for tuning PUMA 560’S controller

Multi-objective biogeography-based optimization technique for tuning PUMA 560’S controller

Analytical and numerical comparisons of biogeography-based optimization and genetic algorithms

Fuzzy Robot Controller Tuning with Biogeography-Based Optimization

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