Using the Capabilities of Neural Networks to Adapt the Parameters of PID Regulation in Automatic Control Systems of Low-Power Boilers

Gordin, Sergey A.; Козлова, О. В.; Zaychenko, I.V.

doi:10.1109/fareastcon50210.2020.9271418

Cited by 3 publications

(1 citation statement)

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“…In order to be able to estimate the energy consumption of the robot associated with the trajectory of movement, it is necessary to have the function E f(arguments). It is proposed to restore (identify) a non-linear dependence using well-established tools of neural networks (Martínez and Velásquez, 2011;Gordin et al, 2020;Ivanov et al, 2021), fuzzy logic (Voskoglou, 2022) or hybrid (neuro-fuzzy systems) (Marakhimov et al, 2018) in the automatic synthesis mode based on the training sample. To obtain a training sample, it was decided to perform a limited but sufficient number of movement trajectories, representing the set W and the loading zone (Figure 2B), having carried out all the necessary measurements.…”

Section: Methodsmentioning

confidence: 99%

Automated method based on a neural network model for searching energy-efficient complex movement trajectories of industrial robot in a differentiated technological process

Gorkavyy,

Egorova

et al. 2023

Front. Energy Res.

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Purpose of the work: Researching possibility of creating a method for improving the energy efficiency of differentiated robotic technological process (RTP) in the food industry. The high rates of development of production processes robotization, including in the food industry, leading to increase the cost of electrical energy, determine the research tasks relevance in finding energy-efficient methods for controlling industrial robots.Methodology: The proposed approach is based on principles of object-oriented design and possibility of classifying robotic technological process: Applying specific model sets and methods to improve energy efficiency to individual classes. The existing possibility of energy consumption synthesizing models of robots inside differentiated robotic technological process, such as stacking loads, in reduced form, made it possible to use neural network methods to identify non-linear dependencies. At the same time, training sample for intelligent modules was formed on the basis of classical experiment planning algorithms. The synthesis of methods, models and procedures was implemented on the basis of high-level programming languages C++, MATLAB.Results: A mathematical model and automated algorithms for its synthesis are proposed, which make it possible to adjust robotic technological process simulation model taking into account its specifics and implement a method for finding the optimal parameters of its functioning. To confirm the effectiveness of proposed solution, the obtained neural network model and optimization method were tested on real robotic technological process, and the calculation of economic efficiency of proposed solution was also given.Conclusion/recommendations: The application of this approach will significantly reduce energy costs for robotic operations in the food industry.

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Section: Methodsmentioning

confidence: 99%

Automated method based on a neural network model for searching energy-efficient complex movement trajectories of industrial robot in a differentiated technological process

Gorkavyy,

Egorova

et al. 2023

Front. Energy Res.

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Hybrid model process design of joint operator-robot interaction within a synergistic system

Gorkavyy,

Ivanov,

Grabar

et al. 2023

International Scientific and Practical Conference “Innovative Technologies in Agriculture”

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Analysis and Development Potential of Predictive Models for Energy Flows of Autonomous Hybrid Energy Systems

Berdonosov

Vasilev

Zhivotova

2021

J. Phys.: Conf. Ser.

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The article is devoted to the analysis and development potential for predictive models of energy flows of autonomous hybrid energy systems. The article considers the results of the analysis in the form of a morphological table and TRIZ–evolutionary map. The research defined the most promising in predicting energy flows are hybrid models that include more than one architecture. For example, DCNN + LSTM or MLP + LS–SVR. The authors intend to continue research in the direction of creating predictive models of wind energy flows for autonomous hybrid energy systems.

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Using the Capabilities of Neural Networks to Adapt the Parameters of PID Regulation in Automatic Control Systems of Low-Power Boilers

Cited by 3 publications

References 1 publication

Automated method based on a neural network model for searching energy-efficient complex movement trajectories of industrial robot in a differentiated technological process

Automated method based on a neural network model for searching energy-efficient complex movement trajectories of industrial robot in a differentiated technological process

Hybrid model process design of joint operator-robot interaction within a synergistic system

Analysis and Development Potential of Predictive Models for Energy Flows of Autonomous Hybrid Energy Systems

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