Formalization of the problems of synthesizing adaptive control systems with reference models

Igamberdiev, X Z; Botirov, T. V.; Latipov, Sh B

doi:10.1088/1742-6596/2388/1/012051

Cited by 7 publications

(10 citation statements)

References 6 publications

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“…To lead to the main result of the proposed article, attention is focused on an exceptional methodological approach [12][13][14][15][16][17][18][19][20] based on the analysis of derivatives of input and output signals that are available for measurement. This method represents a unique and innovative way that provides compelling evidence of the effectiveness and feasibility of our new control algorithm.…”

Section: Resultsmentioning

confidence: 99%

Algorithms for adaptive control with limited disturbance compensation

Botirov,

Latipov,

Baqoyev

et al. 2024

E3S Web of Conf.

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This paper proposes an adaptive control scheme for systems affected by parametric uncertainty and limited external disturbances, without requiring measurements of the controlled variable's derivatives. The control approach provides compensation for these disturbances, addressing control problems with a reference model. Adaptive control is a technique where a system's parameters are adjusted based on external conditions or internal changes. In the context of constrained disturbance compensation, control algorithms must account for limitations on available resources or system capacity to counteract disturbances. Such algorithms can be beneficial in situations where the system has limited resources or where disturbances cannot be fully eliminated. The proposed adaptive control scheme aims to effectively manage systems subject to parametric uncertainty and bounded external disturbances, without the need for derivative measurements of the controlled variable. The control approach compensates for these disturbances, making it suitable for applications where complete disturbance rejection is not feasible due to resource or capacity constraints.

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Section: Resultsmentioning

confidence: 99%

Algorithms for adaptive control with limited disturbance compensation

Botirov,

Latipov,

Baqoyev

et al. 2024

E3S Web of Conf.

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show abstract

“…But the Kotelnikov series is not very convenient, in comparison with other series, in software and hardware reconstruction, which most often determines the choice in favour of power series, Lagrange polynomials, etc. [10][11][12][13][14][15]. In the proof of the sampling theorem it is assumed that an infinite Kotelnikov series is used to recover the information.…”

Section: Discussionmentioning

confidence: 99%

“…Analysis of expressions (9) (10) allows us to draw an important conclusion: approximation can be used only for recovery of signals, spectra of which are finite and it is possible to specify the boundary frequency. But, as noted earlier, real signals have infinite spectra and the description of the sampling-recovery process only by means of approximating polynomials does not allow taking into account all components of the error.…”

Section: Methodsmentioning

confidence: 99%

Filtering errors in primary sensor signals

Jumaev,

Ismoilov

2023

E3S Web Conf.

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Sensors are used in the primary measurement of signals. When signals in the sensor are transmitted to the next device, there is an occurrence of errors due to various effects. Filters are used to eliminate the errors that have occurred. Depending on the frequency nature of the signals, there are high pass filters and low pass filters. We select the filters according to the frequency range of the signal. It is observed that the measurement result is different from the actual value if the signal is not filtered. This article analyzes the errors that occur when signals are processed. In time discretization, the state of the signal spectrum is considered. The cause of the appearance of errors has been studied.

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“…The problem of controlling the grinding process is to take into account the increased correlation between factors, large time delays, uncontrolled perturbations, changes in parameters in time, nonlinearity of the technological process, as well as the suboptimal design of the grinding plant [9][10][11]. This is achieved by introducing into the predictive model the description of uncertainties and cases of deviation from the technological process.…”

Section: Methodsmentioning

confidence: 99%

Simulation and control of ball mills under uncertainty conditions

Mukhitdinov,

Kadirov,

Boybutayev

et al. 2024

J. Phys.: Conf. Ser.

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The article considers a model for monitoring the grinding process in a ball mill, where the ground material itself is involved in grinding. The constructed model takes into account the uncertainties associated with the quality indicators of the processed ore at the beginning and in the process of grinding, the influence of the quality indicators of the ore in terms of particle size and their hardness on the kinetics of grinding. The non-linearity of the technological process and uncertainties are taken into account in the control system aimed at stabilizing the system. The model is presented in the form of four blocks describing the main links of the mill plant. At the output of the model, the participation of the mill, the volume of the sump are determined, and the particle size at the outlet of the hydrocyclone is estimated. The operation of the hydrocyclone control system is demonstrated, which controls the loading density and particle size depending on the change in ore quality.

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Formalization of the problems of synthesizing adaptive control systems with reference models

Cited by 7 publications

References 6 publications

Algorithms for adaptive control with limited disturbance compensation

Algorithms for adaptive control with limited disturbance compensation

Filtering errors in primary sensor signals

Simulation and control of ball mills under uncertainty conditions

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