Through combining P-type iterative learning (IL) control, model-free adaptive (MFA)control and sliding mode (SM) control, a robust model-free adaptive iterative learning (MFA-IL)control approach is presented for the active vibration control of piezoelectric smart structures.Considering the uncertainty of the interaction among actuators in the learning control process,MFA control is adopted to adaptively adjust the learning gain of the P-type IL control in order toimprove the convergence speed of feedback gain. In order to enhance the robustness of the systemand achieve fast response for error tracking, the SM control is integrated with the MFA control todesign the appropriate learning gain. Real-time feedback gains which are extracted fromcontrollers construct the basic probability functions (BPFs). The evidence theory is adopted to thedesign and experimental investigations on a piezoelectric smart cantilever plate are performed tovalidate the proposed control algorithm. The results demonstrate that the robust MFA-IL controlpresents a faster learning speed, higher robustness and better control performance in vibrationsuppression when compared with the P-type IL control.
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http://dx.doi.org/10.3390/mi10030196 | DOI Listing |
NPP Digit Psychiatry Neurosci
January 2025
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY USA.
Sci Rep
January 2025
Multidisciplinary Center for Infrastructure Engineering, Shenyang University of Technology, Shenyang, 110870, China.
The current research introduces a model-free ultra-local model (MFULM) controller that utilizes the multi-agent on-policy reinforcement learning (MAOPRL) technique for remotely regulating blood pressure through precise drug dosing in a closed-loop system. Within the closed-loop system, there exists a MFULM controller, an observer, and an intelligent MAOPRL algorithm. Initially, a flexible MFULM controller is created to make adjustments to blood pressure and medication dosages.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Electrical Engineering, Chosun University, Gwangju, 61452, South Korea.
The study presents an intelligent, model-free current control strategy that eliminates the need for explicit plant models while efficiently reducing the effect of plant parameter perturbation. By employing a data-driven approach with fewer input features, the proposed scheme reduces the computational burden during training while maintaining high control performance. Unlike conventional model predictive current control (MPCC), which is computationally expensive because of solving optimization problems at each sample time, and requires precise plant models, the proposed method enhances system performance by addressing plant model discrepancies through data-driven techniques.
View Article and Find Full Text PDFJ Magn Reson
December 2024
Department of Low-Temperature Physics, Faculty of Mathematics and Physics, Charles University, V Holešovičkách 747/2, 180 00 Prague 8, Czech Republic.
PCA-based denoising usually implies either discarding a number of high-index principal components (PCs) of a data matrix or their attenuation according to a regularization model. This work introduces an alternative, model-free, approach to high-index PC attenuation that seeks to average values of PC vectors as if they were expected from noise perturbation of data. According to the perturbation theory, the average PCs are attenuated versions of the clean PCs of noiseless data - the higher the noise-related content in a PC vector, the lower is its average's norm.
View Article and Find Full Text PDFAutism Res
December 2024
Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Dresden, TU, Germany.
Existing literature has documented diminished norm-based adaptation (aftereffects) across several perceptual domains in autism. However, the exact underlying mechanisms, such as sensory dominance possibly caused by imprecise priors and/or increased sensory precision, remain elusive. The "Bayesian brain" framework offers refined methods to investigate these mechanisms.
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