A Novel H∞ Robust Control Strategy for Voltage Source Inverter in Microgrid

The voltage control performance of the voltage source inverter (VSI) in a microgrid may change under different load conditions. However, in the case of traditional control strategies, the robustness of VSI is insufficient. In response to the above problems, a novel robust control scheme for VSI in the microgrid based on H∞ hybrid sensitivity is proposed in this study. The grid-side interference during the VSI operation is taken as the variable, and the sensitivity function is designed to build a H∞ robust voltage controller for VSI. In addition, an adaptive virtual impedance group is designed to further improve the voltage control robustness under a variety of operation conditions. Finally, comparative simulation experiments are carried out to verify the anti-interference ability of the proposed control strategy under different working conditions.

An Improved Finite-Control-Set Model Predictive Current Control for IPMSM under Model Parameter Mismatches

The control performance of the finite control set model predictive current control (FCS-MPCC) for the interior permanent magnet synchronous machine (IPMSM) depends on the accuracy of the mathematical model. A novel robust model predictive current control method based on error compensation is proposed in order to reduce the parameter sensitivity and improve the current control robustness. In this method, the equivalent parameters are obtained from the known voltage and current information at the past time and the error between the predicted current and the actual current at the present time, which is utilized in the two-step prediction process to compensate the parameter mismatch error. Finally, the optimal voltage vector is selected by the cost function. The proposed method is compared with the traditional model predictive current control method through experiments. The experimental results show the effectiveness of the proposed method.

REVIEW ON DEEP LEARNING APPROACHES TO THE CONTROL OF PROSTHETIC HANDS WITH ELECTROMYOGRAPHY SIGNALS

Natural control methods based on surface electromyography (sEMG) and pattern recognition are promising for hand prosthetics. Several efforts have been carried out to enhance dexterous hand prosthesis control by impaired individuals. However, the control robustness offered by scientic research is still not sufcient for many real life applications, and commercial prostheses are capable of offering natural control for only a few movements. This paper reviews various papers on deep learning approaches to the control of prosthetic hands with EMG signals and made a comparison on their accuracy.

Reinforcement Learning and Control of a Lower Extremity Exoskeleton for Squat Assistance

A significant challenge for the control of a robotic lower extremity rehabilitation exoskeleton is to ensure stability and robustness during programmed tasks or motions, which is crucial for the safety of the mobility-impaired user. Due to various levels of the user’s disability, the human-exoskeleton interaction forces and external perturbations are unpredictable and could vary substantially and cause conventional motion controllers to behave unreliably or the robot to fall down. In this work, we propose a new, reinforcement learning-based, motion controller for a lower extremity rehabilitation exoskeleton, aiming to perform collaborative squatting exercises with efficiency, stability, and strong robustness. Unlike most existing rehabilitation exoskeletons, our exoskeleton has ankle actuation on both sagittal and front planes and is equipped with multiple foot force sensors to estimate center of pressure (CoP), an important indicator of system balance. This proposed motion controller takes advantage of the CoP information by incorporating it in the state input of the control policy network and adding it to the reward during the learning to maintain a well balanced system state during motions. In addition, we use dynamics randomization and adversary force perturbations including large human interaction forces during the training to further improve control robustness. To evaluate the effectiveness of the learning controller, we conduct numerical experiments with different settings to demonstrate its remarkable ability on controlling the exoskeleton to repetitively perform well balanced and robust squatting motions under strong perturbations and realistic human interaction forces.

Brain Long Noncoding RNAs: Multitask Regulators of Neuronal Differentiation and Function

The extraordinary cellular diversity and the complex connections established within different cells types render the nervous system of vertebrates one of the most sophisticated tissues found in living organisms. Such complexity is ensured by numerous regulatory mechanisms that provide tight spatiotemporal control, robustness and reliability. While the unusual abundance of long noncoding RNAs (lncRNAs) in nervous tissues was traditionally puzzling, it is becoming clear that these molecules have genuine regulatory functions in the brain and they are essential for neuronal physiology. The canonical view of RNA as predominantly a ‘coding molecule’ has been largely surpassed, together with the conception that lncRNAs only represent ‘waste material’ produced by cells as a side effect of pervasive transcription. Here we review a growing body of evidence showing that lncRNAs play key roles in several regulatory mechanisms of neurons and other brain cells. In particular, neuronal lncRNAs are crucial for orchestrating neurogenesis, for tuning neuronal differentiation and for the exact calibration of neuronal excitability. Moreover, their diversity and the association to neurodegenerative diseases render them particularly interesting as putative biomarkers for brain disease. Overall, we foresee that in the future a more systematic scrutiny of lncRNA functions will be instrumental for an exhaustive understanding of neuronal pathophysiology.

Die Attach Assembly Process Tool Advancement

Die attach film (DAF) voids detection is one of the challenges during the introduction of non-conductive adhesives for integrated circuit products affecting production control robustness and detection. In this paper, a specialized tool capable to distinguish and quantify the amount of DAF voids is presented wherein the implementation of semi-auto grid lines generates more precise measurement and correct defect call-out. The tool is proposed as an alternative option for x-ray inspection that is found to be incapable in proper detection and accurate measurement of gaps and un-occupied area within the adhesive thickness that produces over estimation of production rejects.