Feedforward control to suppress the leading edge bulge in photopolymerization-based additive manufacturing

Purpose This study aims to examine the feasibility of feedforward actuation of the recoater blade position to alleviate the resin surface non-uniformity while moving over deep-to-shallow transitions of submerged (already cured) geometric features. Design/methodology/approach A two-dimensional computational fluid dynamics (CFD) model has been used to determine optimized blade actuation protocols to minimize the resin surface non-uniformity. An experimental setup has been designed to validate the feasibility of the proposed protocol in practice. Findings A developed protocol for the blade height actuation is applied to a rectangular stair-like configuration of the underlying part geometry. The evaluation of the actuation protocol revealed the importance of two physical length scales, the capillary length and the size of the flow recirculation cell below in the liquid resin layer below the blade. They determine, together with the length scales defining the topography (horizontal extent and depth), the optimal blade trajectory. This protocol has also shown its efficiency for application to more complicated shapes (and, potentially, for any arbitrary geometry). Practical implications This study shows that incorporation of a feedforward control scheme in the recoating system might significantly reduce (by up to 80%) the surface unevenness. Moreover, this improvement of performances does not require major modifications of the existing architecture. Originality/value The results presented in this work demonstrate the benefits of the integration of the feedforward control to minimize the leading edge bulges over underlying part geometries in stereolithography.

Stochastic Bayesian optimization for predicting borderline knock

Engine knock is an undesirable combustion that could damage the engine mechanically. On the other hand, it is often desired to operate the engine close to its borderline knock limit to optimize combustion efficiency. Traditionally, borderline knock limit is detected by sweeping tests of related control parameters for the worst knock, which is expensive and time consuming, and also, the detected borderline knock limit is often used as a feedforward control without considering its stochastic characteristics without compensating current engine operational condition and type of fuel used. In this paper, stochastic Bayesian optimization method is used to obtain a tradeoff between stochastic knock intensity and fuel economy. The log-nominal distribution of knock intensity signal is converted to Gaussian one using a proposed map to satisfy the assumption for Kriging model development. Both deterministic and stochastic Kriging surrogate models are developed based on test data using the Bayesian iterative optimization process. This study focuses on optimizing two competing objectives, knock intensity and indicated specific fuel consumption using two control parameters: spark and intake valve timings. Test results at two different operation conditions show that the proposed learning algorithm not only reduces required time and cost for predicting knock borderline but also provides control parameters, based on trained surrogate models and the corresponding Pareto front, with the best fuel economy possible.

Control Strategy Research of Based on Resonance Feedforward and Current Estimate of the Photovoltaic Grid Inverter

Due to poor quality of grid-connected current and large impedance value of grid are caused in weak grid environment. Therefore, a control strategy combining multiple resonant feedforward and current estimation method is adopted in this paper. Firstly, in order to make the positive feedback channel have the feedback effect only at the main background harmonics, the low-order harmonics of the grid are extracted by using the method of multi-resonance feedforward control. At the same time, under the premise that the suppression effect of LCL natural resonant frequency remains unchanged, the current estimation method is added into the control strategy, so as to reduce the system cost. Finally, the simulation results show that thus verifying the correctness and effectiveness of the control strategy.

Reorganization of functional and directed corticomuscular connectivity during precision grip from childhood to adulthood

How does the neural control of fine movements develop from childhood to adulthood? Here, we investigated developmental differences in functional corticomuscular connectivity using coherence analyses in 111 individuals from four different age groups covering the age range 8–30 y. EEG and EMG were recorded while participants performed a uni-manual force-tracing task requiring fine control of force in a precision grip with both the dominant and non-dominant hand. Using beamforming methods, we located and reconstructed source activity from EEG data displaying peak coherence with the EMG activity of an intrinsic hand muscle during the task. Coherent cortical sources were found anterior and posterior to the central sulcus in the contralateral hemisphere. Undirected and directed corticomuscular coherence was quantified and compared between age groups. Our results revealed that coherence was greater in adults (20–30 yo) than in children (8–10 yo) and that this difference was driven by greater magnitudes of descending (cortex-to-muscle), rather than ascending (muscle-to-cortex), coherence. We speculate that the age-related differences reflect maturation of corticomuscular networks leading to increased functional connectivity with age. We interpret the greater magnitude of descending oscillatory coupling as reflecting a greater degree of feedforward control in adults compared to children. The findings provide a detailed characterization of differences in functional sensorimotor connectivity for individuals at different stages of typical ontogenetic development that may be related to the maturational refinement of dexterous motor control.

The influence of stimulus and behavioral histories on predictive control of smooth pursuit eye movements

The smooth pursuit system has the ability to perform predictive feedforward control of eye movements. This study attempted to examine how stimulus and behavioral histories of past trials affect the control of predictive pursuit of target motion with randomized velocities. We used sequential ramp stimuli where the rightward velocity was fixed at 16 deg/s while the leftward velocity was either fixed (predictable) at one of seven velocities (4, 8, 12, 16, 20, 24, or 28 deg/s) or randomized (unpredictable). As a result, predictive pursuit responses were observed not only in the predictable condition but also in the unpredictable condition. Linear mixed-effects (LME) models showed that both stimulus and behavioral histories of the previous two or three trials influenced the predictive pursuit responses in the unpredictable condition. Intriguingly, the goodness of fit of the LME model was improved when both historical effects were fitted simultaneously rather than when each type of historical data was fitted alone. Our results suggest that predictive pursuit systems allow us to track randomized target motion using weighted averaging of the information of target velocity (stimulus) and motor output (behavior) in past time sequences.

Time-Delayed Acceleration Feedback Control of a Single-Link Flexible Manipulator Using Kalman Filter

The design problem of a discrete controller with time delay and acceleration feedback for a single-link flexible manipulator system is addressed in this paper. The dynamical model of a single-link flexible manipulator system is presented by the adoption of the finite element method and Lagrange’s equation. Based on the random-walk process and the discrete reduction method, an augmented discretized delay-free state derivate space equation containing the random noise is established. An acceleration-based Kalman filtering method is developed in order to estimate the system state and external excitation necessary for the controller design. In light of the estimated augmented states, a hybrid controller that combines a feedback control algorithm and a feedforward control algorithm is designed according to optimal control theory and Moore–Penrose theory. Numerical simulation results show that the proposed controller can damp out the vibration response of the flexible manipulator system effectively upon external excitations. Moreover, it is further revealed that the control performance of the presented method can be improved by adding the time delay appropriately.

Model-based feedforward control for suppressing torque oscillation of electric power steering system

Oscillation suppression is essential for the stability design of electric power steering (EPS) systems. The stability controller module in EPS controller is the key to solve the stability control problem of EPS system. This paper proposes a new method of stability analysis and stability controller module design for EPS systems. Furthermore, the dynamic characteristics of the EPS system are analyzed, and two critical factors on the resulting EPS stability, that is, large assist and variable assist gain are investigated experimentally. The transfer function from steering torque to sensor torque is redefined. A new transfer function is proposed for measuring the effect of variable assist gain on system performance. Based on the above factors and transfer functions, constraints on the stability controller design are proposed. Then the optimal parameters in the controller are obtained by maximizing an objective function including phase margin, gain margin, and crossover frequency. It is concluded from simulations and bench tests that the proposed stability controller can significantly reduce the torque oscillation of the EPS system.