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Hybrid synchronization and parameter estimation of a complex chaotic network of permanent magnet synchronous motors using adaptive integral sliding mode control

Siddique Nazam, Rehman Fazal U.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2021

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Vol. 69, nr 3

art. no. e137056

EN

The synchronisation of a complex chaotic network of permanent magnet synchronous motor systems has increasing practical importance in the field of electrical engineering. This article presents the control design method for the hybrid synchronization and parameter estimation of ring-connected complex chaotic network of permanent magnet synchronous motor systems. The design of the desired control law is a challenging task for control engineers due to parametric uncertainties and chaotic responses to some specific parameter values. Controllers are designed based on the adaptive integral sliding mode control to ensure hybrid synchronization and estimation of uncertain terms. To apply the adaptive ISMC, firstly the error system is converted to a unique system consisting of a nominal part along with the unknown terms which are computed adaptively. The stabilizing controller incorporating nominal control and compensator control is designed for the error system. The compensator controller, as well as the adopted laws, are designed to get the first derivative of the Lyapunov equation strictly negative. To give an illustration, the proposed technique is applied to 4-coupled motor systems yielding the convergence of error dynamics to zero, estimation of uncertain parameters, and hybrid synchronization of system states. The usefulness of the proposed method has also been tested through computer simulations and found to be valid.

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An improved framework for Parkinson’s disease prediction using Variational Mode Decomposition-Hilbert spectrum of speech signal

Karan Biswajit, Sekhar Sahu Sitanshu

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 2

717--732

EN

Parkinson’s disease (PD) is a neuro-degenerative disease due to loss of brain cells, which produces dopamine. It is most common after Alzheimer’s disease specially seen in old age people. In the earlier stage of disease, it has been noticed that most of the people suffering from speech disorder. From last two decades many studies have been conducted for the analysis of vocal tremors in PD. This study explores the combined approach of Variational Mode Decomposition (VMD) and Hilbert spectrum analysis (HSA) to investigate the voice tremor of patients with PD. A new set of features Hilbert cepstral coefficients (HCCs) are proposed in this study. Proposed features are assessed using vowels and words of PC-GITA database. The effectiveness of HCC features is utilized to perform classification, and regression analysis for PD detection. The highest average classification accuracy up to 91% and 96% is obtained with vowel /a/ and word /apto/ respectively. Further the classification accuracy up to 82% is obtained with independent dataset, when tested with the optimized model developed using PC-GITA database. In dysarthria level prediction highest correlation up to 0.82 is obtained using vowel /a/ and 0.8 with word /petaka/. The outcomes of this study indicate that the proposed articulatory features are suitable and accurate for PD assessment.

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Ocena zdolności sorpcyjnych kwasów huminowych jako parametru określającego możliwość ich zastosowania w celach nawozowych

Hoffmann K., Huculak-Mączka M.

Przemysł Chemiczny

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2011

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T. 90, nr 5

792-795

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Problem oznaczania pozostałości rozpuszczalników organicznych w wybranych produktach spożywczych i farmaceutycznych, cz. II

Michulec M., Wardencki W.

Analityka : nauka i praktyka

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2003

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nr 2

26--28