Improved Takagi–Sugeno fuzzy model-based control of flexible joint robot via Hybrid-Taguchi genetic algorithm

Nikdel, Parisa; Hosseinpour, M.; Badamchizadeh, Mohammad Ali; Akbari, Mohammad

doi:10.1016/j.engappai.2014.03.009

Cited by 31 publications

(9 citation statements)

References 11 publications

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“…Recently, nature-inspired algorithms, such as GA, have found a special place in engineering optimization problems (Nikdel et al ., 2014). GP is basically a GA applied to a population of members or computer programs.…”

Section: Intelligent Optimization Proceduresmentioning

confidence: 99%

Intelligent model-based optimization of cutting parameters for high quality turning of hardened AISI D2

Pourmostaghimi

Zadshakoyan

Badamchizadeh

2020

AIEDAM

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This paper proposes an intelligent model-based optimization methodology for optimizing the production cost and material removal rate subjected to surface quality constraint in turning operation of hardened AISI D2. Unlike traditional approaches, this paper deals with finding optimum cutting parameters considering the real condition of the cutting tool. Tool flank wear is predicted by the model obtained using genetic programming. On the basis of the predicted flank wear value, the surface roughness of work piece is estimated by neural networks. Applying the particle swarm optimization algorithm, the optimum machining parameters are determined. The simulation and experimental results show that machining with proposed intelligent optimization methodology has higher efficiency than conventional techniques with constant optimized cutting parameters.

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Section: Intelligent Optimization Proceduresmentioning

confidence: 99%

Intelligent model-based optimization of cutting parameters for high quality turning of hardened AISI D2

Pourmostaghimi

Zadshakoyan

Badamchizadeh

2020

AIEDAM

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“…The flow chart of the algorithm is shown in Figure 5. Especially, the terminology of genetic algorithm has not been completely unified, so the concepts mentioned in different literatures also differ in expression [11].…”

Section: Overview Of Genetic Algorithmsmentioning

confidence: 99%

Hybrid Optimization Algorithm for Cable Design of Electromechanical Products Based on A* and Genetic Algorithm

2019

IJOMAM

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In order to solve the problem that traditional mechanical and electrical cable design relies heavily on experience and lacks optimization in structure design, aiming at the particularity of cable component design, after comparing several intelligent optimization algorithms synthetically, commonly used A* algorithm and genetic algorithm, which are suitable for cable design, are selected and improved in order to realize the intelligent optimization design of cable component. Firstly, the main technologies needed to be considered in actual cabling design are analysed; the cabling environment requirements of intelligent optimization algorithm are compared, and the automatic generation method of cabling environment is obtained based on three-dimensional software; A* algorithm considering cabling technology is designed and improved to plan the cabling path. The application of genetic algorithm in the design of cable branching structure is analysed, and the partial operation of genetic algorithm is improved to optimize the performance of the algorithm; and then A* algorithm is introduced into the fitness function of genetic algorithm to realize the global optimization design of branching structure and path in cable design. The research results confirm that the hybrid algorithm of A* algorithm and genetic algorithm has certain advantages in the design of cable for mechanical and electrical products. It improves the traditional design algorithm and greatly improves the efficiency of the design of cable for mechanical and electrical products.

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“…Heuristic algorithm can help in fixing drawback in Fuzzy Sugeno. Nikdel et al [26], Mariajayaprakash et al [27], Nallasamy and Ratnavelu has implemented optimization on Sugeno can provide 2243 the level of accuracy steeper, and optimized fuzzy controller gives better performance than a conventional fuzzy controller also regarding rising and settling time. Evolution Strategies (ES) is an evolutionary algorithm used in this study as an optimization technique.…”

Section: Related Workmentioning

confidence: 99%

Rule Optimization of Fuzzy Inference System Sugeno using Evolution Strategy for Electricity Consumption Forecasting

Santika¹,

Mahmudy²,

Naba³

2017

IJECE

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The need for accurate load forecasts will increase in the future because of the dramatic changes occurring in the electricity consumption. Sugeno fuzzy inference system (FIS) can be used for short-term load forecasting. However, challenges in the electrical load forecasting are the data used the data trend. Therefore, it is difficult to develop appropriate fuzzy rules for Sugeno FIS. This paper proposes Evolution Strategy method to determine appropriate rules for Sugeno FIS that have minimum forecasting error. Root Mean Square Error (RMSE) is used to evaluate the goodness of the forecasting result. The numerical experiments show the effectiveness of the proposed optimized Sugeno FIS for several test-case problems. The optimized Sugeno FIS produce lower RMSE comparable to those achieved by other well-known method in the literature.

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Improved Takagi–Sugeno fuzzy model-based control of flexible joint robot via Hybrid-Taguchi genetic algorithm

Cited by 31 publications

References 11 publications

Intelligent model-based optimization of cutting parameters for high quality turning of hardened AISI D2

Intelligent model-based optimization of cutting parameters for high quality turning of hardened AISI D2

Hybrid Optimization Algorithm for Cable Design of Electromechanical Products Based on A* and Genetic Algorithm

Rule Optimization of Fuzzy Inference System Sugeno using Evolution Strategy for Electricity Consumption Forecasting

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