Direct Amplitude Control for Voice Coil Motor on High Frequency Reciprocating Rig

High-frequency reciprocating rig (HFRR) that utilizes direct drive voice coil motor (VCM) is designed to perform pin-on-flat tribotest such as assessing the lubricity of fuel. In this article, we propose direct amplitude control to maintain the amplitude of high-frequency reciprocating motion of VCM on HFRR subjects to frictional load. First, the mathematical model of HFRR is developed, which takes the effect of frictional load into consideration. Subsequently, direct amplitude control is proposed, which takes the error of amplitude rather than position error as performance index. It consists of an amplitude regulator and an offset compensator. Analysis and simulations of the proposed control method are presented as well. Finally, the proposed control method is deployed to a digital signal processor on an HFRR prototype experimental platform. The experiment results indicate a significant improvement of the performance of VCM-based HFRR in terms of amplitude accuracy in high-frequency band compared with that given by traditional PI control. The results also suggest the instability of PR control in this case

Speed sensorless nonlinear adaptive control of induction motor using combined speed and perturbation observer

High performance induction motors (IM) require a robust and reliable speed controller to maintain the speed tracking performance under various uncertainties and disturbances. This paper presents a sensorless speed controller for IM based on speed and perturbation estimation and compensation. By defining a lumped perturbation term to include all unmodeled nonlinear dynamics and external disturbances, two state and perturbation observers are designed with combining the model reference adaptive system (MRAS) based speed observer to estimate the flux and speed states and the flux- and speed-loop related lumped perturbation terms. The estimated flux, speed and perturbation terms are used to design an output feedback, speed sensorless nonlinear adaptive controller (SSNAC) for IM. The stability of the closed-loop system is addressed in Lyapunov theory. Effectiveness of the SSNAC is verified via simulation and experiment tests. Comparing with the standard vector control plus MRAS speed observer (VC-MRAS), the proposed SSNAC reduces the speed tracking error by 20% to 30% on average under model uncertainties and unknown load disturbance due to the estimation and compensation of perturbation terms. The combined observer can estimate the real rotor speed under speed varying and load changes and thus makes SSNAC achieve high performance robust speed drive without using speed sensors

Nonlinear adaptive speed control of a permanent magnet synchronous motor: A perturbation estimation approach

This paper presents a nonlinear adaptive control (NAC) scheme for the speed regulation of a permanent magnet synchronous motor (PMSM) based on perturbation estimation and feedback linearizing control. All PMSM system’s unknown nonlinearities, parameter uncertainties, and external disturbances including unknown time-varying load torque disturbance, are defined as lumped perturbation terms, which are estimated by designing perturbation observers. The estimates are used to adaptively compensate the real perturbations and achieve adaptive feedback linearizing control of the original nonlinear system. The proposed control scheme does not require accurate system model and full state feedback. Stability of the close-loop system with proposed NAC is investigated via Lyapunov theory, and the effectiveness of proposed NAC scheme is verified through both simulation and experimental studies. Both simulation and experimental results show that the proposed NAC scheme can provide less regulation error in speed tracking, better dynamic performance and robustness against parameter uncertainties and load torque disturbance, compared with conventional vector control and load torque estimated based control

Nutrient regulation of biological nitrogen fixation across the tropical western North Pacific

Nitrogen fixation is critical for the biological productivity of the ocean, but clear mechanistic controls on this process remain elusive. Here, we investigate the abundance, activity, and drivers of nitrogen-fixing diazotrophs across the tropical western North Pacific. We find a basin-scale coherence of diazotroph abundances and N 2 fixation rates with the supply ratio of iron:nitrogen to the upper ocean. Across a threshold of increasing supply ratios, the abundance of nifH genes and N 2 fixation rates increased, phosphate concentrations decreased, and bioassay experiments demonstrated evidence for N 2 fixation switching from iron to phosphate limitation. In the northern South China Sea, supply ratios were hypothesized to fall around this critical threshold and bioassay experiments suggested colimitation by both iron and phosphate. Our results provide evidence for iron:nitrogen supply ratios being the most important factor in regulating the distribution of N 2 fixation across the tropical ocean

Research on the Diagnosability of a Satellite Attitude Determination System on a Fault Information Manifold

In this paper, a new method for fault diagnosability research based on information geometry is proposed. The problem of the diagnosability evaluation of dynamic system faults is transformed into a distance calculation problem on a manifold. The Fisher information distance is used to realize a quantitative judgment of diagnosability, and a quantitative evaluation index of the fault diagnosability of a satellite attitude determination system is designed. This includes a fault detectability index and a fault isolability index. The validity and superiority of the new indexes are verified through a mathematical simulation. In addition, the fault information is visually presented by the geodesics of the fault manifold, and the properties and behavior of the fault are mined and analyzed on the fault information manifold, which lays a foundation for further exploration of fault information through geometric methods

Research on the Diagnosability of a Satellite Attitude Determination System on a Fault Information Manifold

In this paper, a new method for fault diagnosability research based on information geometry is proposed. The problem of the diagnosability evaluation of dynamic system faults is transformed into a distance calculation problem on a manifold. The Fisher information distance is used to realize a quantitative judgment of diagnosability, and a quantitative evaluation index of the fault diagnosability of a satellite attitude determination system is designed. This includes a fault detectability index and a fault isolability index. The validity and superiority of the new indexes are verified through a mathematical simulation. In addition, the fault information is visually presented by the geodesics of the fault manifold, and the properties and behavior of the fault are mined and analyzed on the fault information manifold, which lays a foundation for further exploration of fault information through geometric methods

Comparative Transcriptomics Analysis Reveals Rusty Grain Beetle’s Aggregation Pheromone Biosynthesis Mechanism in Response to Starvation

Pheromones are the basis of insect aggregation, mating, and other behaviors. Cucujoid grain beetles produce macrocyclic lactones as aggregation pheromones, yet research on their biosynthesis at the molecular level remains limited. The rusty grain beetle, C. ferrugineus, is an important economic species in China. Although two aggregation pheromone components have been identified, their suspected biosynthesis via the MVA pathway and the FAS pathway lacks molecular elucidation. Previous evidence supports that starvation affects the production of aggregation pheromones. Therefore, we constructed comparative transcriptome libraries of pheromone production sites in C. ferrugineus under starvation stress and identified genes related to pheromone biosynthesis and hormone regulation. A total of 2665 genes were significantly differentially expressed, of which 2029 genes were down-regulated in starved beetles. Putative C. ferrugineus genes directly involved in pheromone biosynthesis were identified, as well as some genes related to the juvenile hormone (JH) pathway and the insulin pathway, both of which were depressed in the starved beetles, suggesting possible functions in pheromone biosynthesis and regulation. The identification of genes involved in macrolide lactone biosynthesis in vivo holds great significance, aiding in the elucidation of the synthesis and regulatory mechanisms of cucujoid grain beetle pheromones

The Identification of ECG Signals Using Wavelet Transform and WOA-PNN

Electrocardiogram (ECG) signal identification technology is rapidly replacing traditional fingerprint, face, iris and other recognition technologies, avoiding the vulnerability of traditional recognition technologies. This paper proposes an ECG signal identification method based on the wavelet transform algorithm and the probabilistic neural network by whale optimization algorithm (WOA-PNN). Firstly, Q, R and S waves are detected by wavelet transform, and the P and T waves are detected by local windowed wavelet transform. The characteristic values are constructed by the detected time points, and the ECG data dimension is smaller than that of the non-reference detection. Secondly, combined with the probabilistic neural network, the mean impact value algorithm is used to screen the characteristic values, the characteristic values with low influence are eliminated, and the input and complexity of the model are simplified. Finally, a WOA-PNN combined classification method is proposed to intelligently optimize the hyper parameters in the probabilistic neural network algorithm to improve the model accuracy. According to the simulation verification on three databases, ECG-ID, MIT-BIH Normal Sinus Rhythm and MIT-BIH Arrhythmia, the identification accuracy of a single ECG cycle is 96.97%, and the identification accuracy of three ECG cycles is 99.43%

Neurofilament light chain as a mediator between LRRK2 mutation and dementia in Parkinson’s disease

Abstract Elevated neurofilament light chain (NfL) levels have been associated with dementia in idiopathic Parkinson’s disease (iPD). To examine the baseline and longitudinal changes in NfL levels in GBA-PD, SNCA-PD, and LRRK2-PD and further investigate the association between these genetic mutations, NfL, and dementia in PD. We analyzed data from the Parkinson’s Progression Markers Initiative (PPMI), including 184 healthy controls (HC) and 617 PD categorized as iPD (n = 381), LRRK2-PD (n = 142), GBA-PD (n = 76) and SNCA-PD (n = 18). Analysis of covariance (ANCOVA) or linear mixed-effect models were used to compare the baseline or dynamic NfL levels between groups. We then explored the relationship between genetic mutations, serum NfL levels, and conversion to dementia using mediation analysis. After adjusting for confounding factors, SNCA-PD exhibited higher baseline serum NfL levels than iPD. Regarding longitudinal changes, SNCA-PD showed the highest increase rate in estimated NfL levels (2.43 pg/mL per year), while LRRK2-PD experienced the slowest increase rate (0.52 pg/mL per year). Mediation analysis indicated that higher estimated NfL level changes were associated with faster cognitive decline (β = 0.591, p = 0.026). Specifically, the relationship between LRRK2 and dementia was mediated by the estimated NfL level change (β = −0.717, p < 0.05). Longitudinal changes in serum NfL levels may serve as a biomarker for cognitive decline in Parkinson’s disease. Moreover, compared to iPD, the slower progression of dementia in LRRK2-PD may be partially attributed to a slower increase in NfL levels