Temat: histereza - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Nonsmooth optimization control based on a sandwich model with hysteresis for piezo-positioning systems
Autorzy:: Yang, Sen
Tan, Yonghong
Dong, Ruili
Tan, Qingyuan
Powiązania:: https://bibliotekanauki.pl/articles/24200686.pdf
Data publikacji:: 2023
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: optimization control
hysteresis
piezoelectric actuator
sandwich system
histereza
siłownik piezoelektryczny
układ warstwowy
Opis:: A nonsmooth optimization control (NOC) based on a sandwich model with hysteresis is proposed to control a micropositioning system (MPS) with a piezoelectric actuator (PEA). In this control scheme, the hysteresis phenomenon inherent in the PEA is described by a Duhem submodel embedded between two linear dynamic submodels that describe the behavior of the drive amplifier and the flexible hinge with load, respectively, thus constituting a sandwich model with hysteresis. Based on this model, a nonsmooth predictor for sandwich systems with hysteresis is constructed. To avoid the complicated online search for the optimal value of the generalized gradient at a nonsmooth point, the method of the so-called weighted estimation of generalized gradient is proposed. In order to compensate for the model error caused by model uncertainty, a model error compensator (MEC) is integrated into the online optimization control strategy. Afterwards, the stability of the control system is analyzed based on Lyapunov’s theory. Finally, the proposed NOC-MEC method is verified on an MPS with a PEA, and the corresponding experimental results are presented.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2023, 33, 3; 449--461
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Neural network based identification of hysteresis in human meridian systems
Autorzy:: Tan, Y.
Dong, R.
Chen, H.
He, H.
Powiązania:: https://bibliotekanauki.pl/articles/330958.pdf
Data publikacji:: 2012
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: punkt akupunktury
histereza
sieć neuronowa
tradycyjna medycyna chińska
acupuncture point
hysteresis
identification
neural network
human meridian
traditional Chinese medicine
Opis:: Developing a model based digital human meridian system is one of the interesting ways of understanding and improving acupuncture treatment, safety analysis for acupuncture operation, doctor training, or treatment scheme evaluation. In accomplishing this task, how to construct a proper model to describe the behavior of human meridian systems is one of the very important issues. From experiments, it has been found that the hysteresis phenomenon occurs in the relations between stimulation input and the corresponding response of meridian systems. Therefore, the modeling of hysteresis in a human meridian system is an unavoidable task for the construction of model based digital human meridian systems. As hysteresis is a nonsmooth, nonlinear and dynamic system with a multi-valued mapping, the conventional identification method is difficult to be employed to model its behavior directly. In this paper, a neural network based identification method of hysteresis occurring in human meridian systems is presented. In this modeling scheme, an expanded input space is constructed to transform the multi-valued mapping of hysteresis into a one-to-one mapping. For this purpose, a modified hysteretic operator is proposed to handle the extremum-missing problem. Then, based on the constructed expanded input space with the modified hysteretic operator, the so-called Extreme Learning Machine (ELM) neural network is utilized to model hysteresis inherent in human meridian systems. As hysteresis in meridian system is a dynamic system, a dynamic ELM neural network is developed. In the proposed dynamic ELMneural network, the output state of each hidden neuron is fed back to its own input to describe the dynamic behavior of hysteresis. The training of the recurrent ELM neural network is based on the least-squares algorithm with QR decomposition.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2012, 22, 3; 685-694
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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