Miracle-dz/UniFAR-FTUs huggingface dataset:
Analytic Behaviors applied to backlash compensation

This work presents an analytic behaviors-based methodology for the construction of feasible right inverses to compensate for the nonlinear backlash nonlinearity. A GP search is implemented to minimize the compensation error generated by the prospective inverses. The selected found solutions are then tested for regulation and tracking in the model of a modified printer driven by a DC motor subject to saturation and backlash in the input. It should be noted that, in contrast with existing approaches, the found solutions are independent of the nonlinearity’s parameter. Then, the solutions remain valid for variations in the size of backlash.
A Robust Torque Controller Based on Link-side Damping Assignment for Flexible Joint Robots

Disturbance-observer-based controller has been one of the most widely used robust control methods in force control of flexible joint robots, due to its robustness and simple implementation. However, flexible joint robots will suffer unexpected instability in robust torque control based on disturbance observer (DOB) because of the intrinsic uncertainty of the nominal model. This paper proposes a DOB-based robust torque control method based on link-side damping assignment, which can improve the robust stability of the inner DOB loop without affecting the torque tracking performance. Experiments are implemented on a collaborative robot with flexible joints. The results show that the proposed method can improve robust stability without sacrificing torque tracking performance.
Adaptive Scheme for Improving the Transient Stability of Grid-Connected inverter under Grid Voltage Sag based on Instability Prediction

Under severe grid faults, the grid-connected inverter system faces the problem of synchronization instability. If the fault is not monitored and removed in time, the grid may even collapse. Therefore, the research on transient instability mechanism and stability improvement method of grid-connected inverter system is of great significance. To the end, this paper analyzes the effects of PLL (phase-locked loop) parameters and injection current mode on the transient stability of the system by using equal area method, and shows that freezing the integral controller of PLL and injecting reactive current are both beneficial to improve the grid-connected inverter transient stability. Based on this conclusion, this paper proposes a transient stability improvement scheme for grid-connected inverters based on instability prediction and adaptive parameters adjustment. By monitoring the d-axis voltage and $\Delta \omega_{\mathrm{pll}}$ obtained in PLL, system instability can be predicted in time and parameters can be adjusted quickly in order to improve the transient stability of the system. Finally, the effectiveness of the improvement scheme is verified by simulation.
Modelling and Control of Wind Turbine and Battery Energy Storage System for Grid Integration

The benefits of renewable energy sources (RES) are undeniable, despite the fact that controlling their output power is complicated due to their intermittent nature. In this paper, a new set of analytical formulations has been proposed for simultaneous integration and control of wind turbine (WT) and battery energy storage system (BESS) considering the time-varying load models, and resources uncertainty. The objective functions of this method include smoothing the output power of the WT unit, balancing demand and generation, increasing WT shares as well as decreasing the automatic generation control (AGC) reserve capacity which is essential in the gird. In addition, the modification of BESS reference current is considered to prolong the BESS effective lifetime and guarantee the prevention of BESS from over-charge and discharge. The results show that simultaneous integration of WT and BESS in the grid will smooth WT output power, balance load and WT generation, thereby reducing AGC required capacity and increasing the hosting capacity of grids effectively.
A hybrid Protection Scheme based on Deep Reinforcement Learning

The growth of distributed renewable energy re-sources pushes classic protection schemes to their limits. An automated workflow is necessary to handle the volatility of future power systems characterized by constantly changing network states. Therefore a hybrid, deep reinforcement learning based protection scheme is proposed in this paper, which combines a single agent training for central protection with a multi agent training for a decentralized fallback.For this purpose, a training framework using a combination of PowerFactory and Python-Scripts connected via a Shared Memory is developed. The framework is used to train agents using Deep Recurrent Q-Learning. First a central protection is trained, which can then be imitated by decentral agents using only local measurements. Tests of the setup show a fast and selective response, which proves that deep reinforcement learning can indeed automatically generate a protection scheme fulfilling high requirements of dependability and security.
Common-Mode Voltage Reduction Scheme for MMC with Consideration of Dead Zone

Modular multilevel converter (MMC) has been widely used because of its superior performance. However, the rapid switching of power electronics devices will bring serious electromagnetic interference (EMI). In MMC system with the nearest level modulation (NLM), it is deduced that there is a common-mode voltage (CMV) whose amplitude is one third of the voltage of the capacitor of the sub-module (SM). In this paper, the vector modulation which is nearest to the zero-CMV vector (ZCMVV) in theory can greatly reduce the amplitude of the CMV. However, the dead zone in PWM can bring spikes in CMV and increase the CM EMI. By analyzing the switching state, the introduction of dead zone compensation (DZC) removes most of the peaking of the CMV. Experimental results have been provided to validate the effect of CM EMI mitigation in MMC after deadtime compensation.
Oscillation Suppression Method based on Power Control Optimization of the Wind Farms

Oscillation issues in the grid-connected wind farm system can severely deteriorate the power system security and lead to serious damage. Based on the impedance analysis method, many oscillation suppression methods have been proposed for reshaping the impedance characteristics of system to avoid the risk of oscillations. However, modifying converters’ controllers to reshape the wind turbines (WTs) impedance and usage of additional damping devices for reshaping the grid impedance both are time-consuming and costly. Therefore, based on the sequence impedance model of the grid-connected wind farm system, this paper proposes a novel oscillation suppression method by optimizing the power control of wind farm. The system stability margin can be improved by modifying the number of online WTs and the output power of each WT to reshape the impedance of wind farm, under the precondition that the output power demand of wind farm is satisfied. Then, the intelligent particle swarm optimization (PSO) algorithm is used to solve the optimization approach to obtain the number of online WTs with optimal stability margin. Finally, the simulation confirms the effectiveness of the proposed oscillation suppression method.
Immersion and Invariance-based Adaptive Control for Quadrotor Transportation Systems Using Deep Reinforcement Learning

Quadrotor unmanned aerial vehicles (UAVs) are playing important roles in cargo transportation, especially in complex and unknown environments. However, the underactuated nature of the quadrotor UAV transportation systems makes it difficult for control schemes design. Considering system parameter uncertainties, a novel nonlinear adaptive control law is designed via Immersion and Invariance (I&I) method. Besides, in order to pursue faster convergence and stronger robustness, a novel control structure based on deep reinforcement learning is presented. The RL agent, which is designed through twin delayed deep deterministic policy gradient (TD3) algorithm, generates optimal control gains for the I&I adaptive controller in real-time. The stability of the closed-loop system are guaranteed through Lyapunov technique and LaSalle’s invariance theorem. Finally, by comparing with the classical control scheme, the superior performance and stronger robustness of the proposed controller are validated through numerous simulations.
Gap Waveguide Technology: An Overview of Millimeter-Wave Circuits Based on Gap Waveguide Technology Using Different Fabrication Technologies

With the fast development of next-generation wireless communication and radar sensing, the millimeter-wave frequency band is becoming more and more important due to its rich spectrum, wide bandwidth, and ability to support the miniaturization of antennas and circuits <xref ref-type="bibr" rid="ref1">[1]</xref>, <xref ref-type="bibr" rid="ref2">[2]</xref>, <xref ref-type="bibr" rid="ref3">[3]</xref>, <xref ref-type="bibr" rid="ref4">[4]</xref>. High-performance, low-loss, and low-cost millimeter-wave circuits and modules are the key elements for wireless front-end systems. For planar transmission lines, such as microstrip lines and coplanar waveguides, they are very easy to integrate with other circuits for low-cost and low-profile physical designs. However, the high insertion loss and radiation loss from planar transmission lines limit their applications at the millimeter-waveband. As for rectangular and cylindrical waveguides, their main application is in the areas of low loss and high power handling, but their 3D structure is difficult to integrate with other planar passive and active circuits. The substrate-integrated waveguide (SIW) is a better way to merge the advantages of planar transmission lines and rectangular waveguides, but it still suffers from dielectric losses at the millimeter-wave frequency range.
Discrete Adaptive second order sliding mode control for Uncertain Hammerstein Nonlinear systems

This paper deals with the problem of synthesizing a robust controller for Input-Output uncertain nonlinear Hammerstein system, which is composed of two blocks: A nonlinear block in series with a linear system. In this context, we propose an adaptive second-order sliding mode control. First, a discrete sliding mode control was used, but the traditional sliding mode control suffer from the drawback of the high frequency chattering which can affect the system stability. Higher order discrete SMC is an efficient solution to overcome the chattering phenomena. In this paper, a new adaptive second order discrete sliding mode control (DSMC) development is presented. In fact, the adaptation mechanism is based on adjusting the switching gain in order to improve the tracking performance and to insure a strong robustness against external disturbances and parameters variations. Simulation example is given to verify the efficiency of the proposed method.
An Automated Visual Defect Segmentation for Flat Steel Surface Using Deep Neural Networks

Defect detection on the metal sheets is an importance for the steel production industry. This paper proposes an automated visual defect segmentation for flat steel surface using two different deep neural network methods, namely U-Net and fully convolutional network-8 (FCN-8). Different models like ResNet34, MobilNetV2, EfficientNetB0, InceptionV3, and VGG16 have been implemented as the U-Net’s encoder. In addition, FCN8 model has been investigated as a different architecture for the SEVERESTAL dataset. Due to the imbalanced dataset, various techniques such as dice coefficient as loss function and data augmentation have been used. Moreover, no-defect steel surfaces have been considered as a separate class. As a result, a promising dice score is achieved on the validation set for the described four-class approach.
A Reinforcement-Learning Style Algorithm for Black Box Automata

The analysis of hardware and software systems is often applied to a model of a system rather than to the system itself. Obtaining a faithful model for a system may sometimes be a complex task. For learning the regular (finite automata) structure of a black box system, Angluin's $L^{*}$ algorithm and its successors employ membership and equivalence queries. The regular positive-negative inference (RPNI) family of algorithms use a less powerful capability of collecting observations for learning, with no control on selecting the inputs. We suggest and study here an alternative approach for learning, which is based on calculating utility values, obtained as a discounted sum of rewards, in the style of reinforcement learning. The utility values are used to classify the observed input prefixes into different states, and then to construct the learned automaton structure. We show cases where this classification is not enough to separate the prefixes, and subsequently remedy the situation by exploring deeper than the current prefix: checking the consistency between descendants of the current prefix that are reached with the same sequence of inputs. We show the connection of this algorithm with the RPNI algorithm and compare between these two approaches experimentally.
A Quasi-Direct Drive Robot Hand for Reactive and Contact-rich Manipulations

Proprioceptive actuation, which includes direct-drive (DD) and quasi-direct-drive (QDD), has achieved great success in the legged robots community recently. It also shows great potential in reactive and contact-rich manipulations. However, it suffers from critical problems such as motor overheating and unacceptable low firm grasping force, which prevents its further applications. To solve these problems, this paper presents a novel QDD hand design with an innovative hybrid drive mechanism that can take full advantage of grasping mode characteristics, thereby significantly increasing the grasp force without overheating the motor. We present detailed design, formulate the kinematics and dynamics modeling, investigate current-based force sensing (proprioceptive perception) and associated control strategies. We develop an integrated robot arm-hand system and perform experiments to validate the effectiveness of our proposed approach. Experimental results indicate that our proposed design holds great promise in leading a fundamental paradigm shift in robot hand design.
Modeling and Robust Computed Torque Control for Lower Limb Exoskeleton Contacting with Ground

Rehabilitation devices perform nowadays suitable and excellent treatment for stroke patients. A Lower limb exoskeleton as a type of Rehabilitation devices has become the most known solution for lower limb rehabilitation of stroke patients. The essential part on lower limb exoskeleton work is to model and control it using a suitable modeling method and an efficient controller. For controlling a three degrees of freedom lower limb exoskeleton in case of contacting with ground ‘ a comparative study between a conventional computed torque controller (CCTC) and robust computed torque controller (RCTC) has been established. Simulation results has performed by comparing both controllers in the same conditions. Stability analysis of the control system has been performed by using a Lyapunov stability theorem. The results of simulation indicate that robust computed torque controller has a good tracking performance and an excellent robustness against system uncertainties and external disturbances as compared with conventional computed torque controller.
Global sliding mode control for teleportation systems with fixed-time delay

The work presented in this article focuses on a new bilateral and multilateral teleoperation framework that is proposed to perform tasks in a hazardous environment with an interconnection delay. First, a bilateral teleoperation system allows a master manipulator to remotely control a slave manipulator. A global sliding mode control GSMC on bilateral teleoperation system is used for this system based on the external disturbances. Despite the presence of the external force and fixed-time delay, the controller is robust and presents a finite-time convergence. Second, a multilateral teleoperation system allows a master manipulator to remotely control a manipulator that serves as the leader of a group of followers. A global sliding mode control on multilateral teleoperation is applied to master-slave-follower manipulators to avoid external disturbances and fixed-time delay. This controller also presents good performance. The two controllers are evaluated in simulation.
Comparing Symbolic and Connectionist Algorithms for Correlating the Age of Healthy Children with Sigma-Lognormal Neuromuscular Parameters

It is important to accurately evaluate the motor control maturity to help physicians diagnose delayed or abnormal motor development in children. Traditionally, it has been challenging to design assessment methods that are practical and accurate at the same time. This study aims to develop an effective algorithm to predict motor control maturity based on the Kinematic Theory of rapid human movements. We used handwritten pen strokes made on an electronic tablet by 513 children (5.5 to 13 years of age). We considered two types of movements: a single stroke and a triangle drawing test. For the analysis, Sigma-Lognormal parameters were extracted from recordings and used in predictive models. We compared multiple models, including linear regression, deep learning, K-nearest neighbors regression, random forest, gradient boosting regression, and support vector regression. These models performed well considering the within-children variability in handwritten strokes and the between-children variability in motor control maturity. The best score was obtained using the neural network model: coefficient of determination (R2): 0.548; mean absolute error (MAE): 0.937. We found simple stroke parameters alone to be sub-optimal; the results were better when using parameters from the triangle test. In conclusion, our study demonstrates that the Sigma-Lognormal model offers new possibilities for estimating the motor control maturity. Our method is fast and comfortable for the children, as it only requires performing handwriting strokes on an electronic tablet. This simple and user-friendly test is expected to be more convenient for doctors in a clinical context.
On the Performance of SISO, SIMO and MISO-NOMA Systems under Perfect and Imperfect SIC

This paper improves a two-user downlink non-orthogonal multiple access (NOMA) system by using multi-antennas at both the transmitter and the receiver. Three NOMA scenarios: a single-input single-output (SISO), a single-input multi-output (SIMO) and a multi-input single-output (MISO) are considered and compared. Assuming that the two users were over Rayleigh fading channels, the expressions of achievable capacity and outage probability (OP) on closed-form are derived and compared. To gain further insights into these scenarios, perfect and imperfect successive interference cancellation (SIC) are considered. The results of these three scenarios are presented and discussed, and it found that the improvement of the diversity order at the transmitter or the receiver enhances the performance of NOMA systems.
Integral-type Half-Tangent Phase-Locked Loop for Power Systems with Harmonic Disturbances

In this paper, an integral-type half-tangent phase locked loop (IHtan-PLL) is presented for power systems with harmonic disturbances. Compared with the traditional Synchronous Reference Frame Phase-Locked Loop (SRF-PLL), the proposed IHtan-PLL has the following advantages: (1) when the frequency occurs a large variation, the SRF-PLL has plenty of oscillations, and a considerable prolonged transient response. Whereas, the IHtan-PLL shows the faster trends, and on oscillation emerges; (2) the IHtan-PLL has a better filtering effect in terms of steady-state when the power system contains harmonic disturbances. Simulation tests are presented to demonstrate superior performance of IHtan-PLL.
Supervision and Speed Vector Control of Photovoltaic/Single-Phase Induction Machine System

This work explores the vector control of a single-phase induction motor drive powered by photovoltaic system. The static power converter side is implemented using a boost Direct Current (DC) converter associated with an H-Bridge IGBT inverter. The supervision of the boost converter is ensured by the so called “perturb & observe” algorithm coupled with a DC-bus control. The speed vector control is based in the field orientation idea adapted for this type of machine. Simulation results are extracted to clarify the operation of the global system.
Interpretation of spontaneous polarization anomalies caused by spheroidal ore bodies

The theoretical case of a polarized hidden sphere is discussed briefly. To determine location, depth and dip of an orebody displaying natural currents a method of practical value is explained and a master diagram is given. A distinction is made between anomalies which can be treated assuming a polarized sphere and those which can not be interpreted this way. A generalized method of topographical and regional correction is given. The procedure of interpretation and topographical correction is shown with two field examples. The method is not empirical and the results are precise when applied to the cases in which the assumption of a sphere is justified. It is shown that topographical corrections can not be overlooked.
DOA Estimation Using Beamspace-Based Deep Neural Network

Direction-of-arrival (DOA) estimation is to analyze the received sensor array data to determine the direction of the signal, so as to better perform beamforming or determine the target position. In the field of DOA estimation, traditional methods are parametric, while machine learning methods rely heavily on the consistency of test and training samples, and when there is array imperfection, their performance is extremely degraded. Therefore, this paper uses deep learning to solve the DOA estimation problem, and makes DOA estimation in the virtual array beam space to adapt to the array imperfections, and a training method robust to various array imperfections is proposed, that is, a spherical model is used to simulate the distribution of different array imperfections, and training sets are generated in the distribution. Simulation results show that the proposed method has good adaptability to array imperfections.
Comparison of reverberation measurements using Schroeder's impulse method and a decay-curve averaging method

Utilizing a digital acquisition system and minicomputer, two promising techniques for accurate determination of reverberation times have been studied in detail from the viewpoint of standard reverberation room tests. The first is Schroeder’s ’’integrated impulse method,’’ and special attention was given to the question of repeatability and the influence of signal‐to‐noise ratio on the successful application of the method. The second technique involves taking an ensemble average of a large number of logarithmic decay curves. It was found that, even for nonuniform decays, the average decay curves obtained by the second method compared well with those determined by the Integrated Impulse Method.