Suitability analysis of using industrial robots in manufacturing

Kangru, Tavo; Riives, Jüri; Mahmood, Kashif; Otto, Tauno

doi:10.3176/proc.2019.4.06

Cited by 11 publications

(9 citation statements)

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“…It can be derived from the expression f(x), which contains all the model information in the formula (1). e goal of the control is to approximate it using the radial basis (RBF) neural network control.…”

Section: Manipulator Joint Tracking Control Strategymentioning

confidence: 99%

“…Since the middle of the twentieth century, industrial manipulators have developed rapidly. So far, it has become indispensable and essential equipment in the fields of machinery manufacturing [1], aerospace [2], and transportation [3]. With more and more comprehensive application range of the manipulator, the control precision, speed, and stability of the manipulator system are demanding.…”

Section: Introductionmentioning

confidence: 99%

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Fractional-Order Iterative Sliding Mode Control Based on the Neural Network for Manipulator

Zhang

Wen-bo

2021

Mathematical Problems in Engineering

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This study aimed to improve the position tracking accuracy of the single joint of the manipulator when the manipulator model information is uncertain. The study is based on the theory of fractional calculus, radial basis function (RBF) neural network control, and iterative sliding mode control, and the RBF neural network fractional-order iterative sliding mode control strategy is proposed. First, the stability analysis of the proposed control strategy is carried out through the Lyapunov function. Second, taking the two-joint manipulator as an example, simulation comparison and analysis are carried out with iterative sliding mode control strategy, fractional-order iterative sliding mode reaching law control strategy, and fractional-order iterative sliding mode surface control strategy. Finally, through simulation experiments, the results show that the RBF neural network fractional-order iterative sliding mode control strategy can effectively improve the joints’ tracking and control accuracy, reduce the position tracking error, and effectively suppress the chattering caused by the sliding mode control. It is proved that the proposed control strategy can ensure high-precision position tracking when the information of the manipulator model is uncertain.

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Section: Manipulator Joint Tracking Control Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fractional-Order Iterative Sliding Mode Control Based on the Neural Network for Manipulator

Zhang

Wen-bo

2021

Mathematical Problems in Engineering

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“…We have used AHP based suitability analysis method [19], which uses product, technology and objective based parameters to evaluate suitability index. Expert group knowledge has been used for application-based criteria's evaluation.…”

Section: Fig 5 Company Feasibility Analysismentioning

confidence: 99%

“…For this study, IR welding application model has been used. Calculated indexes are compared to main suitability decision categories [19] for final assessment.…”

Section: Fig 5 Company Feasibility Analysismentioning

confidence: 99%

“…Their performance will improve future by increasing the number of data. After every data iteration through ANN, back-propagation process is called and synapsis weights are adjusted using Gradient Descent, maximizing the correlation between the output and the residual error of the model [19]. The DL networks are built using Artificial Intelligence Techniques Inc., Neural Designer software.…”

Section: The Efficiency Analysismentioning

confidence: 99%

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Optimisation of decision-making process in industrial robot selection

Kangru¹,

Riives²,

Otto³

et al. 2020

Journal of Machine Engineering

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The successful selection process of industrial robots (IRs) for today's Cyber-Physical Systems is an important topic and there are different possibilities to solve the task. The primary task is to estimate the existing IR selection systems according to the suitability analysis and to highlight the main positive features and problematic areas. The objective of the reverse task is to carry out the sensitivity analysis of the existing robot-based manufacturing systems. The matching of these two approaches helps decision makers to develop the main principles of IR selection in today`s multidimensional and fast-changing economic world.

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