Implementation of reduced induction machine fuzzy logic control based on dSPACE-1104 R&amp;D controller board

Moutchou, Mohamed; Jbari, Atman; Abouelmahjoub, Younes

doi:10.11591/ijpeds.v12.i2.pp1015-1023

Cited by 2 publications

(3 citation statements)

References 11 publications

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“…Figures 8 to 11 show the results of comparative modeling of the system (1) with the control laws ( 5), ( 6), for two cases: i) when a variable appears in the laws 𝑥 3 and ii) when it is replaced by an estimate obtained based on the calculation of the function (14). The simulation was carried out for a load of 80% of the nominal and the same parameters of the steam generator and regulator as in the previous case.…”

Section: Resultsmentioning

confidence: 99%

“…As can be seen from the figures, the largest discrepancy is the graph of the level change. At the same time, the steadystate deviation of the level is different from zero, which is explained by inaccuracies in the approximation of the function (14). Figures 12 and 13 show graphs of changes in steam pressure at the outlet of the steam generator and the water level in its drum with varying load: I -𝐷 𝑏 = 0.7𝐷 𝑏 0 , II -𝐷 𝑏 = 0.8𝐷 𝑏 0 , III -𝐷 𝑏 = 0.95𝐷 𝑏 0 .…”

Section: Resultsmentioning

confidence: 99%

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Integrated application of synergetic approach for enhancing intelligent steam generator control systems

Isamiddin,

Dilnoza

2024

IJECE

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This article focuses on the integrated application of the synergetic approach to enhance the quality of intelligent steam generator control systems.By combining various techniques such as model-based control, adaptive control, and artificial intelligence, an efficient and flexible control system can be developed. Model-based control utilizes mathematical models of steam generators to formulate control algorithms and predict system behavior. Adaptive control enables the system to adapt to changing conditions by adjusting control parameters based on real-time measurements. Artificial intelligence techniques, including neural networks and genetic algorithms, facilitate learning, optimization, and data-driven decision-making processes. The objectives of this research are to investigate the benefits of the synergetic approach in steam generator control, including improved steam generation efficiency, optimized energy consumption, enhanced system stability and reliability, and adaptability to varying operating conditions and disturbances. The findings and conclusions of this study are expected to provide valuable insights for engineers, researchers, and professionals involved in the design and implementation of intelligent steam generator control systems. By integrating the synergetic approach, substantial enhancements in control quality can be achieved, leading to optimal operation and maximum efficiency of power plants.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Integrated application of synergetic approach for enhancing intelligent steam generator control systems

Isamiddin,

Dilnoza

2024

IJECE

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“…Otherwise, we get ∆ 𝑛 = 0. The objective of calculating survival rates is to select the chromosome of the population with the highest survival rate according to the available suitability, and the survival rate can be calculated as (5),…”

Section: Methodsmentioning

confidence: 99%

Synthesis of the neuro-fuzzy regulator with genetic algorithm

Siddikov,

Porubay,

Rakhimov

2024

IJECE

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Real-acting objects are characterized by the presence of various types of random perturbations, which significantly reduce the quality of the control process, which determines the use of modern methods of intellectual technology to solve the problem of synthesis of control systems of structurally complex dynamic objects, allowing to compensate the influence of external factors with the properties of randomness and partial uncertainty. The article considers issues of synthesis of the automatic control system of dynamic objects by applying the theory of intelligent control. In this case, a neural network based on radial-basis functions is used at each discrete interval for neuro-fuzzy approximation of the control system, allowing real-time adjustment of the regulator parameters. The radial basis function is designed to approximate functions defined in the implicit form of pattern sets. The neuro-fuzzy regulator's parameter configuration is accomplished using a genetic algorithm, enabling more efficient computation to determine the regulator's set parameters. The regulator's parameters are represented as a vector, facilitating their application to multidimensional objects. To determine the optimal tuning parameters of the neuro-fuzzy regulator, characterized by high convergence and the possibility of determining global extrema, a genetic algorithm was used. The effectiveness of the neuro-fuzzy regulator is explained by the possibility of providing quality control of the dynamic object under random perturbations and uncertainty of input data.

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Implementation of reduced induction machine fuzzy logic control based on dSPACE-1104 R&D controller board

Cited by 2 publications

References 11 publications

Integrated application of synergetic approach for enhancing intelligent steam generator control systems

Integrated application of synergetic approach for enhancing intelligent steam generator control systems

Synthesis of the neuro-fuzzy regulator with genetic algorithm

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