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An advanced pitch controller is proposed for the load mitigation of wind turbines. This study focuses on the nacelle acceleration feedback control and lidar-based feedforward control, and discusses how these controllers contribute to reduce the load on wind turbines. The nacelle acceleration feedback control increases the damping ratio of the first mode of wind turbines, but it also increases the fluctuation in the rotor speed and thrust force, which results in the optimum gain value. The lidar-based feedforward control reduces the fluctuation in the rotor speed and the thrust force by decreasing the fluctuating wind load on the rotor, which reduces the fluctuating load on the tower. The combination of the nacelle acceleration feedback control and the lidar-based feedforward control successfully reduces both the response of the tower first mode and the fluctuation in the rotor speed at the same time.

LCL-type grid-connected filter circuits are widely used for line network transmission because of their high ability to suppress current harmonics. However, due to the presence of resonance points in this third-order circuit, the system can generate severe oscillations. In order to improve power quality, a combined feed-forward factor control strategy is proposed in this paper. The control system introduces a combined feed-forward factor and incorporates the idea of dual PI control to ensure that the order of the system is reduced while improving its stability, followed by a detailed derivation of the constraint equations for each link parameter. The simulation verifies that the control scheme can regulate the output voltage of the line network and effectively reduce the harmonic content of the line network, providing a reference for reducing the harmonic content of grid-connected currents in engineering.

Variability in the environment defines the structure and dynamics of all living systems, from organisms to ecosystems. Species have evolved traits and strategies that allow them to detect, exploit and predict the changing environment. These traits allow organisms to maintain steady internal conditions required for physiological functioning through feedback mechanisms that allow internal conditions to remain at or near a set-point despite a fluctuating environment. In addition to feedback, many organisms have evolved feedforward processes, which allow them to adjust in anticipation of an expected future state of the environment. Here we provide a framework describing how feedback and feedforward mechanisms operating within organisms can generate effects across scales of organization, and how they allow living systems to persist in fluctuating environments. Daily, seasonal and multi-year cycles provide cues that organisms use to anticipate changes in physiologically relevant environmental conditions. Using feedforward mechanisms, organisms can exploit correlations in environmental variables to prepare for anticipated future changes. Strategies to obtain, store and act on information about the conditional nature of future events are advantageous and are evidenced in widespread phenotypes such as circadian clocks, social behaviour, diapause and migrations. Humans are altering the ways in which the environment fluctuates, causing correlations between environmental variables to become decoupled, decreasing the reliability of cues. Human-induced environmental change is also altering sensory environments and the ability of organisms to detect cues. Recognizing that living systems combine feedback and feedforward processes is essential to understanding their responses to current and future regimes of environmental fluctuations. This article is part of the theme issue ‘Integrative research perspectives on marine conservation’.

The dynamics modeling and trajectory optimization of a segmented linkage cable-driven hyper-redundant robot (SL-CDHRR) become more challenging, since there are multiple couplings between the active cables, passive cables, joints and end-effector. To deal with these problems, this paper proposes a dynamic modeling and trajectory tracking control methods for such type of CDHRR, i.e., SL-CDHRR. First, the multi-coupling kinematics equation (i.e., cable-joint-end) of the hyper-redundant robot is derived. Then, according to the transmission characteristics of the hybrid active/passive segmented linkage, the dynamic equation of series–parallel coupling is derived. It consists of parallel-active dynamics and series-passive dynamics. Furthermore, using the tension of active cables and the pose of the end-effector as optimization indicators, a trajectory tracking framework was constructed by the combination of dynamic feedforward control and PD control. The multi-objective particle swarm optimization method is used to achieve the simultaneous optimization of the energy indicator and control accuracy indicator during the trajectory tracking process. Finally, a MATLAB/SimMechanics co-simulation system is built, and the proposed methods are verified by the built co-simulation system.

The direct energy balance control strategy is analyzed and combined with the characteristics of supercritical units. The direct energy balance strategy is reconstructed. The reconstructed direct energy balance signal is applied to the boiler master control feedforward in the coordinated control, taking a power plant’s coordinated control system as an example, t. Thus, it is applied to the control of supercritical units, which improves the regulation quality of the control system in a steady and dynamic state, and effectively solves the problems of large fluctuation of mainstream pressure, locked increase, and decrease of load command, frequent withdrawal of coordinated control and unqualified AGC index.

Grid-connected inverter is widely used as interfaces between renewable energy sources and the power grid. However, under weak grid conditions, fluctuations in grid impedance can cause instability and current distortion when using conventional control strategies. To address this, a stability control strategy based on reference current cross-feedforward is proposed. By introducing the cross-feedforward term into the conventional dq-axis current references, the method maintains a simple structure and is easy to implement. Generalized Nyquist criterion analysis shows that it broadens the stability range and enables full-power operation under weak grid conditions. The efficacy of the proposed strategy is confirmed by simulation results.

Materials made from active, living, or robotic components can display emergent properties arising from local sensing and computation. Here, we realize a freestanding active metabeam with piezoelectric elements and electronic feed-forward control that gives rise to an odd micropolar elasticity absent in energy-conserving media. The non-reciprocal odd modulus enables bending and shearing cycles that convert electrical energy into mechanical work, and vice versa. The sign of this elastic modulus is linked to a non-Hermitian topological index that determines the localization of vibrational modes to sample boundaries. At finite frequency, we can also tune the phase angle of the active modulus to produce a direction-dependent bending modulus and control non-Hermitian vibrational properties. Our continuum approach, built on symmetries and conservation laws, could be exploited to design others systems such as synthetic biofilaments and membranes with feed-forward control loops. Mechanical metamaterials can be engineered with properties not possible in ordinary materials. Here the authors demonstrate and study an active metamaterial with self-sensing characteristics that enables odd elastic properties not observed in passive media.

Classification of plastic materials based on spectroscopic data is a very crucial task in a variety of applications, including automated recycling, environmental monitoring, quality control in manufacturing, quality control of products, and analysis of complex material properties. These applications demand high precision in identifying and separating plastic types to enhance sustainability and ensure regulatory compliance. In this work, we presented a novel technique Hybridized Attention mechanism-based Feedforward Neural Network (HAttFFNN) to detect three stage Polyethylene Terephthalate (PET) materials. Dataset used in this methodology is basically comprised of 295,327 samples, and contains the parameters like absorbance, wavelengths, references, samples. We collected the spectral data (900-1700 nm) using the Digital Light Processing (DLP) Near-Infrared (NIR) scan Nano Evaluation Module (EVM). We utilized various preprocessing techniques for better and improved detection result, such as Savitzky-Golay filter, interference, Standard Normal Variate (SNV) and Multiplicative Scatter Correction (MSC). The preprocessed and organized spectral data is provided to the proposed HAttFFNN model for the detection of three stage PET material. To validate the performance of the proposed model, we experimented various State-Of-The-Art (SOTA) models, Multi-Head Neural Network (MHNN), Virtual Geometry Group (VGG16), One-Dimensional Convolutional Neural Network (1D-CNN), Residual Network (ResNet), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The proposed model outperforms state of the art techniques across all metrics including accuracy, precision, recall, F1 score, and specificity with Stage 1 (PET Clear vs PET Hazard) achieving 99.33% accuracy, Stage 2 (PET vs Others) 99.32%, and Stage 3 (PET Coloured vs PET Transparent) 99.28%, along with consistently high precision, recall, and specificity values for each class. These results confirm that our proposed model, HAttFFNN, is able to achieve higher accuracy in spectroscopic classification domain, especially in complex cases such as differentiating between visually and spectrally similar materials (PET Clear vs PET Hazard, PET vs Others and PET Colored vs PET Transparent) where traditional models often fail. Furthermore, the Root Mean Square Error (RMSE) values 0.1408 for Stage 1, 0.1249 for Stage 2, and 0.1403 for Stage 3, further validate the model's low-error performance, reinforcing its effectiveness as a less error-prone approach for spectrometry-based plastic material classification.

Aiming at the problem of insufficient control accuracy of automatic disturbance rejection controller (ADRC) in valve-controlled hydraulic cylinder system, the ADRC is improved and applied to the electro-hydraulic servo system. To further reduce the influence of external disturbance and improve the anti-interference ability of the system, PD feedforward ADRC and error feedforward ADRC were designed based on the improved ADRC. The simulation and experimental results show that the anti-interference ability of error feedforward ADRC is stronger than PD feedforward ADRC and traditional ADRC, and can effectively improve the performance of the system, improve the response speed of the system, and greatly improve the anti-interference control accuracy of the system.

This study investigated the relationship between different disfluency types (i.e., repetitions, prolongations, and blocks) and resting state functional connectivity in the feedback (FB) and feedforward (FF) control networks in 20 adults who stutter. Frequency of each disfluency type was coded in speech samples derived from the Stuttering Severity Instrument, and functional connectivity between brain regions of interest was derived from resting state functional magnetic resonance imaging scans. We used LASSO regressions to identify the connections that most strongly predicted each disfluency type. Both repetitions and prolongations were significantly associated with increased connectivity in left ventral motor cortex - right ventral premotor cortex, which is hypothesized to be involved in FB control of speech. In contrast, blocks were significantly associated with reduced connectivity in right anterior cerebellum - left ventral lateral thalamic nucleus and increased connectivity in left presupplementary motor area - left posterior inferior frontal sulcus, both of which are hypothesized to be involved in FF control of speech. Our findings suggest that repetitions and prolongations may be associated with increased reliance on FB-based corrective mechanisms, whereas blocks may be associated with disrupted FF-based initiation mechanisms. These neural underpinnings may correspond to different challenges in terminating or initiating motor commands and underscore the nuanced neurobiological processes underlying speech disfluencies.