Search Results (6)

Genome-wide association studies (GWAS) are essential for identifying genomic regions associated with agronomic traits, but Linear Mixed Model (LMM)-based GWAS face challenges in capturing complex gene interactions. This study explores the potential of machine learning (ML) methodologies to enhance marker identification and association modeling in plant breeding. Unlike LMM-based GWAS, ML approaches do not require prior assumptions about marker–phenotype relationships, enabling the detection of epistatic effects and non-linear interactions. The research sought to assess and contrast approaches utilizing ML (Decision Tree—DT; Bagging—BA; Random Forest—RF; Boosting—BO; and Multivariate Adaptive Regression Splines—MARS) and LMM-based GWAS. A simulated F₂ population comprising 1000 individuals was analyzed using 4010 SNP markers and ten traits modeled with epistatic interactions. The simulation included quantitative trait loci (QTL) counts varying between 8 and 240, with heritability levels set at 0.5 and 0.8. These characteristics simulate traits of candidate crops that represent a diverse range of agronomic species, including major cereal crops (e.g., maize and wheat) as well as leguminous crops (e.g., soybean), such as yield, with moderate heritability and a high number of QTLs, and plant height, with high heritability and an average number of QTLs, among others. To validate the simulation findings, the methodologies were further applied to a real Coffea arabica population (n = 195) to identify genomic regions associated with yield, a complex polygenic trait. Results demonstrated a fundamental trade-off between sensitivity and precision. Specifically, for the most complex trait evaluated (240 QTLs under epistatic control), Ensemble methods (Bagging and Random Forest) maintained a Detection Power (DP) exceeding 90%, significantly outperforming state-of-the-art GWAS methods (FarmCPU), which dropped to approximately 30%, and traditional Linear Mixed Models, which failed to detect signals (0%). However, this sensitivity resulted in lower precision for ensembles. In contrast, MARS (Degree 1) and BLINK achieved exceptional Specificity (>99%) and Precision (>90%), effectively minimizing false positives. The real data analysis corroborated these trends: while standard GWAS models failed to detect significant associations, the ML framework successfully prioritized consensus genomic regions harboring functional candidates, such as SWEET sugar transporters and NAC transcription factors. In conclusion, ML Ensembles are recommended for broad exploratory screening to recover missing heritability, while MARS and BLINK are the most effective methods for precise candidate gene validation. Full article

This paper focuses on the synchronous vibration suppression of an active magnetic bearing (AMB) system without a rotating speed sensor. One of the most intractable problems with AMB systems is the synchronous vibration caused by the mass imbalance of the rotor. Moreover, practically all existing unbalance control algorithms require the rotating speed sensor to determine rotation speed. However, in some unique applications, it is impossible to install and use the rotating speed sensor as intended. This study provided a nonlinear adaptive control (NAC) algorithm and a modified frequency estimator to address the above issues. The proposed approach can suppress current and displacement vibrations by regulating the control structure. The frequency estimator calculates the rotating speed based on the position of the rotor at different moments, which has a quick response time, high precision, and effective tracking. The NAC algorithm can achieve unbalanced control based on the period iteration strategy. Additionally, the Lyapunov method is used to demonstrate the stability of the NAC algorithm. Finally, the experimental and simulation results also confirm the effectiveness and reliability of the overall control scheme. The results from simulations and experiments indicate that the novel frequency estimator can track the speed accurately and that its error can be regulated to within $\pm$0.05 Hz. The overall control schema can reduce the displacement vibration’s amplitude by 72.2% and the current vibration’s amplitude by 65.6%. Full article

A hydraulic pressure servo system based on two high-speed on/off valves (HSV) is a discontinuous system due to the discrete flow of HSV when driven by pulse width modulation (PWM) signal. Pressure variation in the testing chamber is determined by the flow rate difference between the charging and discharging HSV. In this paper, a pressure controller consisting of a differential PWM (DPWM) scheme, asymmetric pressure difference compensation (APDC) and nonlinear adaptive control (NAC) is proposed to precisely control the pressure. The DPWM scheme is designed to improve the resolution of the net flow rate into the testing chamber. Furthermore, due to the strong asymmetry between the charging and the discharging process, the APDC method is proposed to design the two initial duty cycles of the DPWM signal which help to balance its charging and discharging ability under different working pressure points. Since the pressure system is a nonlinear, uncertain system due to oil compression and leakage, the NAC is designed to calculate the control duty cycle of the DPWM signal, which is used to overcome the unmodeled dynamic and parameter uncertainties. Comparative experiments indicate that the proposed controller can ensure good pressure tracking performance and enhance system robustness under different working pressure points and tracking frequencies. Full article

In this paper, a novel adaptive control system (NAC) is proposed for a restricted quarter-car electrohydraulic active suspension system. The main contribution of this NAC is its explicit tackling of the trade-off between passenger comfort/road holding and passenger comfort/suspension travel. Reducing suspension travel oscillations is another control target that was considered. Many researchers have developed control laws for constrained active suspension systems. However, most of the studies in the works of the latter have not directly examined the compromise between road holding, suspension travel, and passenger comfort. In this study, we proposed a novel adaptive control system to explicitly address the trade-off between passenger comfort and road holding, as well as the compromise between passenger comfort and suspension travel limits. The novelty of our control technique lies in its introduction of a modeling system for a dynamic landing tire system aimed at avoiding a dynamic tire liftoff. The proposed control consists of an adaptive neural networks’ backstepping control, coupled with a nonlinear control filter system aimed at tracking the output position of the nonlinear filter. The tracking control position is the difference between the sprung mass position and the output nonlinear filter signal. The results indicate that the novel adaptive control (NAC) can achieve the handling of car–road stability, ride comfort, and safe suspension travel compared to that of the other studies, demonstrating the controller’s effectiveness. Full article

This paper presents an adaptive pitch-angle control approach for a permanent magnet-synchronous generator-based wind turbine (PMSG-WT) connecting with a power grid to limit extracted power above the rated wind speed. In the proposed control approach, a designed perturbation observer is employed for estimating and compensating unknown parameter uncertainties, system nonlinearities, and unknown disturbances. The proposed control approach does not require full state measurements or the accurate system model. Simulation tests verify the effectiveness of the proposed control approach. The simulation results demonstrate that compared with the feedback linearizing controller, conventional vector controller with proportional-integral (PI) loops, and PI controller with gain scheduling, the proposed control approach can always maintain the extracted wind power around rated power, and has higher performance and robustness against disturbance and parameter uncertainties. Full article

This paper proposes a perturbation estimation-based nonlinear adaptive control (NAC) for a voltage-source converter-based high voltage direct current (VSC-HVDC) system which is applied to interconnect offshore large-scale wind farms to the onshore main grid in order to enhance the fault ride-through (FRT) capability of Type-4 wind energy conversion systems (WECS). The VSC-HVDC power transmission system is regraded as a favourable solution for interconnecting offshore wind farms. To improve the FRT capability of offshore power plants, a de-loading strategy is investigated with novel advanced control of the VSC-HVDC systems. The proposed NAC does not require an accurate and precise model and full state measurements since the combinatorial effects of nonlinearities, system parameter uncertainties, and external disturbances are aggregated into a perturbation term, which are estimated by a high-gain perturbation observer (HGPO) and fully compensated for. As the proposed NAC is adaptive to system model uncertainties (e.g., mismatched output impedance of the converters and the line impedance of transmission line), time-varying disturbance (e.g., AC grid voltage sags and line to ground faults), and unknown time-varying nonlinearities of the power-electronic system (e.g., unmodelled dynamics existed in valve and VSC phase-locked loop system), a significant robustness can be provided by the de-loading strategy to enhance the FRT capability. Simulation results illustrated that the proposed strategy can provide improved dynamic performance in the case of operation with a variety of reduced voltage levels and improved robustness against model uncertainties and mismatched system parameters comparing with conventional vector control. Full article