Hardware Experiments Research Articles

Finite-Time Disturbance Observer of Active Power Filter With Dynamic Terminal Sliding Mode Controller

This article proposes an adaptive dynamic terminal sliding mode control (ADTSMC) based on a finite-time disturbance observer (FTDO) for a single-phase active power filter (APF). Aiming at the internal parameter fluctuations and external unknown disturbances in the APF system, an FTDO is designed to compensate for the unknown uncertainties. Then, the proposed dynamic terminal sliding mode control (DTSMC) method combines the dynamic sliding mode control and the terminal sliding mode control to achieve finite-time convergence and weaken system chattering. In order to ensure the organic combination of FTDO and DTSMC, a dual-hidden-layer recurrent neural network (DHLRNN) is used to estimate the gain of sliding mode switching term adaptively so that FTDO can achieve the maximum disturbance estimation. Hardware experiments verify that the designed ADTSMC method has satisfactory robustness and harmonic compensation property under different nonlinear loads.

Maximum power point tracking method based on impedance matching for a micro hydropower generator

An innovative maximum power point tracking method based on impedance matching is proposed and applied in a micro hydropower generator. The micro hydropower generator exploits the water head which is derived from the repeated diving of the marine animal to power the marine telemetry tag. The output voltage of the micro generator is intermittent and changes very fast. The present maximum power point tracking methods can’t deal with this type of intermittent and fast changing voltage of the generator. Hence, to track the optimum operation point and improve the energy efficiency, a new MPPT method which tracks the optimum operation point by directly matching the virtual load impedance of the generator to its internal resistance is proposed. The impedance matching law is developed based on the mathematical model. The experiment of the MPPT hardware is conducted, and the experiment result is used to validate the simulation model. Based on the validated model, the analysis results show that under the fluid pressure from 2 MPa to 8 MPa, the energy efficiency of the generator that uses the MPPT method increase to 1.8 to 2.7 times the one with the direct charging method. The results indicates the effectiveness and advantage of the MPPT method in tracking the optimum operation point and improving the energy efficiency in the context of intermittent and fast changing voltage.

A novel dual-attention optimization model for points classification of power quality disturbances

The rapid development of the power grid makes power quality disturbances (PQDs) more complex. Accurately classifying PQDs and measuring the duration of each disturbance element are both crucial for studying the causes of PQDs and subsequent countermeasures. To satisfy the requirements, a novel dual-attention optimization model (DAOM) is presented in this paper for points classification. First, the local feature attention mechanism (LFAM) based on Hilbert transform (HT) is proposed in the input layer to enhance the local features of PQDs. Subsequently, on the basis of convolutional neural network (CNN), a channel attention mechanism (CAM) based on squeeze-and-excitation network (SENet) is introduced to each convolutional layer to achieve the purpose of automatically learning the importance of each channel feature. Finally, each sampling point is classified in the form of multiclass-multioutput. The proposed model is built through the Keras framework, and a synthetic database containing 49 types of PQDs is established to test the model. The proposal achieves an average classification accuracy of 98.95% in a 30 dB white noise environment, which is more precise and robust than other deep learning-based models. At the same time, the proposed model consistently performs best when evaluated through imbalanced data classification metrics and nonparametric test results. Unlike traditional methods, the proposal can accurately identify the occurrence and end time of each element in complex PQDs. Moreover, through a hardware experiment based on the AC source, the proposed model achieves an average accuracy of 98.30%, which is ahead of other comparison models as well.

Meta-analysis of the space flight and microgravity response of the Arabidopsis plant transcriptome

Spaceflight presents a multifaceted environment for plants, combining the effects on growth of many stressors and factors including altered gravity, the influence of experiment hardware, and increased radiation exposure. To help understand the plant response to this complex suite of factors this study compared transcriptomic analysis of 15 Arabidopsis thaliana spaceflight experiments deposited in the National Aeronautics and Space Administration’s GeneLab data repository. These data were reanalyzed for genes showing significant differential expression in spaceflight versus ground controls using a single common computational pipeline for either the microarray or the RNA-seq datasets. Such a standardized approach to analysis should greatly increase the robustness of comparisons made between datasets. This analysis was coupled with extensive cross-referencing to a curated matrix of metadata associated with these experiments. Our study reveals that factors such as analysis type (i.e., microarray versus RNA-seq) or environmental and hardware conditions have important confounding effects on comparisons seeking to define plant reactions to spaceflight. The metadata matrix allows selection of studies with high similarity scores, i.e., that share multiple elements of experimental design, such as plant age or flight hardware. Comparisons between these studies then helps reduce the complexity in drawing conclusions arising from comparisons made between experiments with very different designs.

Embeddable parallel digital image of doubly-fed induction generator

Due to the complexity of the structure of doubly-fed induction generator (DFIG), it is difficult to conduct real-time simulation. For this reason, this paper designs an embeddable parallel digital image IP of DFIG based on field programmable logic array (FPGA) to realize efficient electromagnetic simulation of DFIG. First, this paper proposes a virtual capacitor equivalent method for induction machine equivalent “T” circuit decoupling; Secondly, based on the principle of data independence and parallelism within the time step, a parallel algorithm for the internal components of DFIG is proposed; Finally, the FPGA-DFIG digital image constructed in this paper is connected to the RT-LAB/power grid, and hardware experiments are carried out under three working conditions: steady state, voltage sag and DC side fault. The experimental results are compared with the simulation results of DFIG grid connected system built in MATLAB/Simulink environment to verify the effectiveness and rapidity of the methods and models proposed in this paper.

Multiple Alternatives of Offset Boosting in a Symmetric Hyperchaotic Map

The offset as the average value of a variable plays an important role in signal processing and system design. Offset boosting can be realized by a non-bifurcation parameter or an initial condition. In this work, symmetric coexisting attractors with opposite polarity and a 2D hyperchaotic map with multiple modes of offset boosting are proposed, where the offset can be controlled both by the initial condition and system parameter, and as a result, multiple alternatives of offset boosting and offset competition show up. Consequently, the final offset is determined eventually by the balance of two factors. The theoretical findings are verified through the hardware experiment based on the STM32. Finally, a pseudo-random number generator (PRNG) is constructed based on the newly proposed hyperchaotic map, demonstrating its high performance in engineering applications.

Human neural network activity reacts to gravity changes in vitro.

During spaceflight, humans experience a variety of physiological changes due to deviations from familiar earth conditions. Specifically, the lack of gravity is responsible for many effects observed in returning astronauts. These impairments can include structural as well as functional changes of the brain and a decline in cognitive performance. However, the underlying physiological mechanisms remain elusive. Alterations in neuronal activity play a central role in mental disorders and altered neuronal transmission may also lead to diminished human performance in space. Thus, understanding the influence of altered gravity at the cellular and network level is of high importance. Previous electrophysiological experiments using patch clamp techniques and calcium indicators have shown that neuronal activity is influenced by altered gravity. By using multi-electrode array (MEA) technology, we advanced the electrophysiological investigation covering single-cell to network level responses during exposure to decreased (micro-) or increased (hyper-) gravity conditions. We continuously recorded in real-time the spontaneous activity of human induced pluripotent stem cell (hiPSC)-derived neural networks in vitro. The MEA device was integrated into a custom-built environmental chamber to expose the system with neuronal cultures to up to 6 g of hypergravity on the Short-Arm Human Centrifuge at the DLR Cologne, Germany. The flexibility of the experimental hardware set-up facilitated additional MEA electrophysiology experiments under 4.7 s of high-quality microgravity (10-6 to 10-5 g) in the Bremen drop tower, Germany. Hypergravity led to significant changes in activity. During the microgravity phase, the mean action potential frequency across the neural networks was significantly enhanced, whereas different subgroups of neurons showed distinct behaviors, such as increased or decreased firing activity. Our data clearly demonstrate that gravity as an environmental stimulus triggers changes in neuronal activity. Neuronal networks especially reacted to acute changes in mechanical loading (hypergravity) or de-loading (microgravity). The current study clearly shows the gravity-dependent response of neuronal networks endorsing the importance of further investigations of neuronal activity and its adaptive responses to micro- and hypergravity. Our approach provided the basis for the identification of responsible mechanisms and the development of countermeasures with potential implications on manned space missions.

Realization of Intelligent Observer for Sensorless PMSM Drive Control

An observer is a crucial part of the sensorless control of a permanent magnet synchronous motor (PMSM). An observer, based on mathematical equations, depends on information regarding several parameters of the controlled motor. If the motor is replaced, then we need to know the motor parameter values and reset the observer’s parameters. This article discusses an intelligent observer that can be used for several motors with different parameters. The proposed intelligent observer was developed using machine learning methods. This observer’s core algorithm is a modified Jordan neural network. It processes Iα, Iβ, vα, and vβ to produce Sin θ and Cos θ values. It is combined with a phase-locked loop function to generate position and speed feedback information. The offline learning process is carried out using data acquired from the simulations of PMSM motors. This study used five PMSMs with different parameters, three as the learning reference sources and two as testing sources. The proposed intelligent observer was successfully used to control motors with different parameters in both simulation and experimental hardware. The average error in position estimated for the simulation was 0.0078 p.u and the error was 0.0100 p.u for the experimental realization.

Transformer‐less unified power flow controller in medium voltage distribution networks

AbstractA transformer‐less unified power flow controller (UPFC) is a power electronic device consisting of series and shunt voltage source converters (VSCs) that are not connected to a common DC bus. It can control power flow in medium voltage (MV) distribution networks without the need for interfacing transformers. Hence there is a significant reduction in size and cost compared to a conventional UPFC. This paper investigates the operating range of a transformer‐less UPFC in an MV distribution network and the required power ratings of the series and shunt converters. The results showed that a transformer‐less UPFC is able to provide active power control using partially rated converters. However, its ability to control reactive power is limited by the current ratings of the converters. The analysis was verified using software simulation and hardware experiment.

N-Dimensional Image Encoding on Quantum Computers

Quantum computing is currently one of the fastest emerging branches of information processing due to its theoretical computation powers exceeding conventional computers by far. However, currently well-known quantum-powered algorithms are of theoretical nature and its effect on practical problems has yet to be discovered. This work reviews current approaches in context of the applicability of quantum computing for the domain of image processing with respect to 3D computed tomography. It focuses on the encoding of n-dimensional images on a quantum computer and also the quantum measurement process, i.e., the image read-out. First results show that while the encoding is indeed highly efficient and is well-suited for representing 3D voxel data as obtained from computed tomography, the decoding to a classical representation is rather expensive. The latter part is also very sensitive to quantum hardware noise as was evidenced by both noisy simulator and real quantum hardware experiments.

Reinforcement Learning-Based Prescribed Performance Motion Control of Pneumatic Muscle Actuated Robotic Arms With Measurement Noises

Featured with high power density, excellent flexibility, shock absorption capacity, etc., pneumatic muscles (PMs) promote the development of exoskeleton robots and rehabilitation equipment. However, the complex nonlinearities of PMs limit efficiency optimization in closed-loop control, while the force-displacement coupling, soft materials, deficient workspace, etc., make it more difficult to simultaneously increase motion speeds and ensure the safety of multiple PM-actuated (PMA) robots. Although force sensors can currently be replaced by applying state estimation techniques, the amplification effects of measurement noises still compromise control accuracy and stability in practice. To this end, this article proposes a reinforcement learning-based robust motion control method with the prescribed performance, which achieves efficient and satisfactory tracking control for PMA robotic arms. In particular, by elaborately incorporating an integral term, a robust generalized proportional integral observer is used to eliminate measurement noises. Meanwhile, by using an actor–critic network to optimize control performance, an error-transformation-based continuous controller is designed to guarantee the uniformly ultimately boundedness of tracking errors. Compared with most existing methods, this article provides the first solution to restrict the entire transient and steady-state performance of PMA robotic arms, improve the noise suppression capability, and optimize the control efficiency simultaneously. Finally, complete stability analysis based on Lyapunov techniques is provided, and several groups of hardware experiments demonstrate the practicability and robustness of the proposed method.

Automated group motion control of magnetically actuated millirobots

Small-size robots offer access to spaces that are inaccessible to larger ones. This type of access is crucial in applications such as drug delivery, environmental detection, and collection of small samples. However, there are some tasks that are not possible to perform using only one robot including assembly and manufacturing at small scales, manipulation of micro- and nano- objects, and robot-based structuring of small-scale materials. In this article, we focus on tasks that can be achieved using a group of small-scale robots like pattern formation. These robots are typically externally actuated due to their size limitation. Yet, one faces the challenge of controlling a group of robots using a single global input. In this study, we propose a control algorithm to position individual members of a group in predefined positions. In our previous work, we presented a small-scaled magnetically actuated millirobot. An electromagnetic coil system applied external force and steered the millirobots in various modes of motion such as pivot walking and tumbling. In this paper, we propose two new designs of these millirobots. In the first design, the magnets are placed at the center of body to reduce the magnetic attraction force between the millirobots. In the second design, the millirobots are of identical length with two extra legs acting as the pivot points and varying pivot separation in design to take advantage of variable speed in pivot walking mode while keeping the speed constant in tumbling mode. This paper presents an algorithm for positional control of n millirobots with different lengths to move them from given initial positions to final desired ones. This method is based on choosing a leader that is fully controllable. Then, the motions of other millirobots are regulated by following the leader and determining their appropriate pivot separations in order to implement the intended group motion. Simulations and hardware experiments validate these results.

Bounded Positioning Control of Manipulators Subject to Base Oscillation and Payload Uncertainty

Mounting base oscillation poses a significant challenge to the control of these mechanical systems called oscillatory base manipulators (OBMs). The positioning accuracy of the system is severely affected by base oscillations. Many control strategies have been investigated, but most of them require measurement or prediction of base oscillations. This paper focuses on a unique type of OBMs—the vehicle-mounted manipulator that operates completely within the non-inertial task space (base-fixed space). It is a great challenge to obtain its base oscillation in practical applications. In addition, the inherent payload uncertainty and actuator saturation make its control design more challenging. To solve above problems, this study developed a novel control strategy. A nonlinear dynamics model is constructed firstly, where the base-oscillation term is considered as the external disturbance of the system. Then, a positioning control, which can be regarded as a proportional derivative (PD) control with continuous time-varying gains, is proposed based on the implicit Lyapunov method. Finally, the simulations and hardware experiments are carried out to certify the effectiveness of the proposed control. Results demonstrate that, even though without prediction or measurement of the base oscillations, the proposed control offers superior positioning performance in terms of robustness against the base oscillation and payload uncertainty as well as assurance of boundedness of the control input.

Analysis and design of high gain DC-DC converter for renewable energy applications

ABSTRACT The paper proposes an efficient DC-DC converter for renewable energy applications. The proposed high-gain converter is designed with switched inductor cell and voltage multiplier cell. In the proposed converter, the continuous source current is obtained by switched inductor cells, and high-voltage gain is achieved using voltage multiplier cells. The proposed converter provides a voltage gain of 10 when operated with a duty ratio of 27.3%, whereas a voltage gain of 39 is obtained for an 80% duty ratio. The efficiency of 96.54% is achieved in simulation for the rated condition of 24 V/240 V, 120W. The converter operation under steady-state, state-space modelling, and voltage and current stress of the power semiconductor components are analyzed. In addition to that, loss distribution and efficiency analysis are made. The performance of the converter is analyzed using the Matlab Simulink tool. Also, the experimental hardware prototype model is developed to validate the simulated and theoretical analysis.

Task-Oriented Grasping Position Selection in Deformable Object Manipulation

Grasping mechanisms in robotic deformable object manipulation (DOM) remain under-investigated due to the intrinsic complexity of DOM. For grasping in DOM, grasping position selection is crucial since external forces are applied to grasping locations on the deformable object (DO) to make it deform. The feasibility and efficiency of performing a specific DOM task thus highly depend on where grasps are placed. This letter investigates the grasping position selection problem in DOM. In particular, we tackle it in a task-oriented manner to facilitate robot deforming operations. For common deformation description methods (image feature and point cloud) and grasping scenarios (single and dual grasps), we develop grasping position selection methods that take into account the properties of both the task and DO. Specifically, metrics are first constructed to quantify grasping position quality when conducting the deforming process. Algorithms are then proposed based on metrics, which enable robots to select appropriate grasping positions in DOM. We evaluated our methods in both simulations and hardware experiments.

Routes toward chaos in a memristor-based Shinriki circuit.

In this paper, the complex routes to chaos in a memristor-based Shinriki circuit are discussed semi-analytically via the discrete implicit mapping method. The bifurcation trees of period-m (m = 1, 2, 4 and 3, 6) motions with varying system parameters are accurately presented through discrete nodes. The corresponding critical values of bifurcation points are obtained by period-double bifurcation, saddle-node bifurcation, and Neimark bifurcation, which can be determined by the global view of eigenvalues analysis. Unstable periodic orbits are compared with the stable ones obtained by numerical methods that can reveal the process of convergence. The basins of attractors are also employed to analyze the coexistence of asymmetric stable periodic motions. Furthermore, hardware experiments are designed via Field Programmable Gate Array to verify the analysis model. As expected, an evolution of periodic motions is observed in this memristor-based Shinrik's circuit and the experimental results are consistent with that of the calculations through the discrete mapping method.

Adaptive Preamble Embedding With MIMO to Support User-Defined Functionalities in WLANs

As the Wi-Fi technology transitions into its sixth generation (Wi-Fi 6), there is a growing consensus on the need to support security and coordination functions at the Physical (PHY) layer. In contrast to the costly approach of extending the PHY-layer header to support new functions (e.g., Spatial Reuse field in the Wi-Fi 6 frame), we propose to turn specific parts of the frame preamble into a reliable data field while maintaining its primary functions. Specifically, in this paper, we develop a scheme called <i>extensible preamble modulation (eP-Mod)</i> for 802.11n/ac/ax protocols that are built on multiple-input-multiple-output (MIMO) and orthogonal frequency-division multiplexing (OFDM). For each frame, <i>eP-Mod</i> can embed up to 144 user bits into the 802.11ac preamble of an <inline-formula><tex-math notation="LaTeX">$8\times 8$</tex-math></inline-formula> MIMO <inline-formula><tex-math notation="LaTeX">$40\;$</tex-math></inline-formula> MHz transmission. The proposed scheme is adaptive to channel conditions and enables several promising PHY-layer services, such as PHY-layer encryption and channel/device authentication, and PHY-layer signaling. At the same time, it allows legacy ( <i>eP-Mod</i> -unaware) devices to continue to process the received preamble as normal by guaranteeing that the proposed preamble waveforms satisfy the structural properties of a standardized preamble. Through numerical analysis, extensive simulations, and hardware experiments, we validate the practicality and reliability of <i>eP-Mod</i> .

Wind speed and direction measurement method based on intelligent optimization algorithm.

Accurate and rapid measurement of wind speed and direction is an important research topic. However, the current measurement algorithms based on ultrasonic arrays are constrained by the large computational effort caused by the spectrum peak search, which hinders the development and application of ultrasonic array wind parameter measurement technology. To overcome this problem, this study applies an intelligent optimization algorithm for measuring wind speed and direction based on a co-prime arc ultrasonic array, which avoids the problem of a large number of calculations in the spectrum peak search. First, the spatial-spectral function of the propagator method algorithm is employed as the fitness function of the particle swarm optimization algorithm. Then, the wind parameter estimation problem is formulated as a function optimization problem, which realizes the fast and accurate measurement of wind speed and direction. Then, the artificial bee colony algorithm is used to measure wind speed and direction, further reducing the calculation amount of the wind parameter measurement. The performance and speed of the proposed method are verified by the design simulation and comparison experiments, reducing the time complexity by up to 90%. In addition, the feasibility of the proposed method is validated in hardware experiments.

A Control Method for Converter-Interfaced Sources to Improve Operation of Directional Protection Elements

The traditional fault control strategy of converter-interfaced renewable energy sources (CIRESs) may bring about a lower sensitivity level or misoperation of fault component-based directional elements. To overcome this problem, a new control scheme is proposed to adjust sequence impedance angles of CIRESs by computing suitable current references of the CIRES controller. Meanwhile, these current references are maximized by an iterative algorithm to make full use of the short-circuit capacity of CIRESs. The proposed control scheme is applicable to various faulty conditions such as different fault types, power factors, weak grids, and larger fault resistances. Compared with the new directional elements that need to update protection algorithms, the proposed control strategies can make CIRESs compatible with the existing directional elements whilst the necessary fault ride-through (FRT) requirements can still be satisfied. Furthermore, all the controller parameters are not required to be revised based on the detected fault type, even with only local measured data collected. The associated PSCAD simulations, real-time digital simulator (RTDS) testing and the downscale hardware experiment verify the proposed method.

Unmanned Aerial Transportation System With Flexible Connection Between the Quadrotor and the Payload: Modeling, Controller Design, and Experimental Validation

Aerial delivery is becoming a reality due to the development of microelectronics and communication technology. Most existing methods for cable-suspended transportation systems utilize fixed-length cable to connect the unmanned aerial vehicle quadrotor and the payload. Such aerial transportation systems present underactuated property, which is caused by the indirectly controllable payload motion and the underactuation of the quadrotor itself. In practical applications, payload hoisting and lowering motion independent of the quadrotor altitude will further expand the application scope in such areas as limited space crossing and offshore sample collection. To realize the aforementioned objectives, a flexible connection between the quadrotor and the payload is realized by mounting an actuator beneath the fuselage. Suffering from strong nonlinearity and complex dynamic coupling, the control problem becomes extremely challenging and more cumbersome, as the system’s degree of freedom (DOF) increases. To deal with these problems, in this article, a nonlinear control approach is presented by energy-based analysis, which achieves simultaneous quadrotor positioning, payload swing elimination and hoisting <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">/</b> lowering. Lyapunov techniques and LaSalle’s Invariance theorem are utilized to prove the asymptotic convergence of the equilibrium point. Finally, a series of hardware experiments are conducted on a self-built aerial transportation platform. As far as we know, this article provides the first mechanism and control solution for payload hoisting/lowering independent of the quadrotor altitude.