Hardware Experiments Research Articles

An Adaptive-Iterative Nonlinear Interference Cancellation in Time-Varying Full-Duplex Channels

In this paper, first, the sensitivity of traditional Full-Duplex (FD) communication systems to time variation in the self-interference (SI) channel is demonstrated via performance analysis. It is seen that conventional schemes are not capable of providing efficient operation regarding practical concerns such as spectral efficiency, SI channel aging and learning accuracy, and the contamination of signal-of-interest (SoI). Then, in regard to the aforementioned concerns, a practical FD operation together with a novel iterative SoI-contamination-aware digital SI cancellation (DSIC) algorithm that relieves the sensitivity against time variation considerably is proposed. The proposed scheme, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Turbo</i> DSIC, is practical in terms of training and computational complexity thanks to its two-stage structure such that complex nonlinear parameter learning (NPL) is conducted very rarely in the first stage, and linear processing is performed adaptively at symbol-rate in the second stage, where SoI is iteratively estimated at each data block and eliminated from the received signal to improve SI channel estimation accuracy. Finally, the performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Turbo</i> DSIC, under hardware impairments and time-varying (TV) propagation, is evaluated via both computer simulation environment and actual experimental hardware setup in terms of bit error rate (BER) and achievable information rate (AIR) metrics.

Configuration Information System for online processing and data monitoring in the NICA experiments

The collection, storage and processing of experimental data are an integral part of modern high-energy physics experiments. The Configuration Information System (CIS) based on the Configuration Database (ConfigDb) is an essential element of a complex of information systems developed for the NICA experiments. The CIS is used to store and provide data on the configuration of the experiment hardware and software systems when collecting data from the detectors in online mode. The ConfigDb stores both a set of various configuration parameters and descriptions of a sequence of online software tasks to be started and run during experiment sessions. The system allows loading configuration information, activating hardware setups and launching all necessary software applications with the required parameters on the specified distributed nodes. The CIS architecture mainly contains the ConfigDb and Configuration Manager, which uses the Dynamic Deployment System API for managing tasks and providing intercommunication. The manager loads configuration data from the database, starts software tasks in a described sequence and supports task process management. The client-server architecture of the CIS is presented. The client has been implemented as a Web service to manage configuration parameters by users.

Master-Slave Synchronization of Robotic Arm using PID Controller

This paper analyzes the position control of a master-slave synchronization robotic arm driven by a D.C. motor using a PID (Proportional, Integral, and Derivative) controller with software and hardware design. This controller works to achieve the exact desired position simultaneously for the master and slave robot arm with minimal defects. The transfer function of the D.C. motor for the robotic arms used in this research is calculated with black box modelling. MATLAB Simulink block is used to test the software result. The MATLAB built-in auto-tuning method obtains Kp, Ki and Kd gain. These gains are adjusted with manual tuning to get precise angular positions for two robotic arms. This research uses Arduino Uno to act as a controller in the experiment. First, the position control of one robotic arm is tested with the same PID gain in MATLAB Simulink at different input degrees. Then, the hardware experiment of position control in one robotic arm is operated with only one PID gain at various reference degrees. Finally, the I2C communication protocol connects the master and slave robot arms. The main work that the PID controller hardware experiment with controls different level angular positions of two robotic arms.

Optimally Band-Limited Noise Filtering for Single-Qubit Gates

We introduce a quantum control protocol that produces smooth, experimentally implementable control sequences optimized to combat temporally correlated noise for single-qubit systems. The control ansatz is specifically chosen to be a functional expansion of discrete prolate spheroidal sequences, a discrete time basis known to be optimally concentrated in time and frequency, and quite attractive when faced with experimental control hardware constraints. We leverage the filter function formalism to transform the control problem into a filter design problem, and show that the frequency response of a quantum system can be carefully tailored to avoid the most relevant dynamical contributions of noise processes. Using gradient ascent, we obtain optimized filter functions and exploit them to elucidate important details about the relationship between filter function design, control bandwidth, and noise characteristics. In particular, we identify regimes of optimal noise suppression and, in turn, optimal control bandwidth directly proportional to the size of the frequency bands where the noise power is large. In addition to providing guiding principles for filter design, our approach enables the development of controls that simultaneously yield robust noise filtering and high-fidelity single-qubit logic operations in a wide variety of complex noise environments.

Design of Control System for Multistage Distillation Seawater Desalination Device Driven by Photovoltaic-Thermal

This research proposes a seawater desalination system driven by photovoltaic and solar thermal energy for remote regions such as islands and seaside villages where fresh water is not accessible. The performance of this system is demonstrated through experiments, and the main concerns are the output of the photovoltaic power generation system, power quantity, water yield, and the loads under different solar irradiance and temperature. In this system, a PLC is used as the controller to adjust the water pump by the collection and processing of sensor data. A load switching time system is designed to select different operating schemes under different environments in order to save energy. The control method of this system is developed to ensure that the photovoltaic power generation system does not undervoltage while maintaining the normal operation of the desalination system. An improved Perturbation and Observation (P&amp;O) algorithm is also proposed as a new Maximum Point Power Tracking (MPPT) method to solve the problem of misjudgment and oscillation after tracking the maximum power point (MPP) in the traditional P&amp;O algorithm. The simulation test in the MATLAB/Simulink environment shows that when external irradiance changes, the improved P&amp;O algorithm can track the MPP faster than the traditional P&amp;O algorithm, and the amplitude of oscillation on the MPP is smaller. The hardware experiments show that this system can operate stably and flexibly, and it is capable of producing 5.18 kWh of electric energy and 335.81 kg of freshwater per day. The maximum yield of the unit can reach 565.75 kg per day and the maximum daily power generation is 8.12 kWh.

Hu-bot: promoting the cooperation between humans and mobile robots

This paper investigates human–robot collaboration in a novel setup: a human helps a mobile robot that can move and navigate freely in an environment. Specifically, the human helps by remotely taking over control during the learning of a task. The task is to find and collect several items in a walled arena, and Reinforcement Learning is used to seek a suitable controller. If the human observes undesired robot behavior, they can directly issue commands for the wheels through a game joystick. Experiments in a simulator showed that human assistance improved robot behavior efficacy by 30% and efficiency by 12%. The best policies were also tested in real life, using physical robots. Hardware experiments showed no significant difference concerning the simulations, providing empirical validation of our approach in practice.

Manipulator trajectory tracking based on adaptive fuzzy sliding mode control

SummarySliding mode control is one of the common control methods for manipulators, but the discontinuity of sliding mode control can cause jitter and vibration of the manipulator system. This article takes the Dobot Magician manipulator as the research object and constructs a simplified model of the manipulator dynamics. And the adaptive fuzzy sliding mode control method is designed in combination with fuzzy control theory, which effectively solves the jitter problem of the control torque. In the MATLAB/Simulink simulation environment analysis show that the proposed adaptive fuzzy sliding mode control method solves the jitter problem in sliding mode control with good stability, robustness and tracking performance. By combining the adaptive fuzzy sliding mode control method with the manipulator and comparing the motion time of the same motion trajectory with the PID control method in Dobot Studio, the hardware experiment results effectively verify the feasibility and effectiveness of the designed control method.

Computational Optimal Impact Angle Control Guidance Laws Weighted by Arbitrary Functions

This paper presents a computational impact angle control guidance law based on the energy cost weighted by arbitrary functions in order to shape the acceleration command as desired. The optimal guidance problem is established in the impact angle frame and is solved by the Gauss orthogonal collocation method. The proposed guidance law is formulated from the generalized optimal control framework, thus a new guidance law that allows achieving a specific guidance goal can be easily obtained in the way of devising a proper weighting function (smooth, piecewise, nonsmooth, or even discontinuous). This property provides more degrees of freedom in the guidance law design to accomplish a specified guidance objective. A hardware experiment is conducted to evaluate the real-time computational capacity of the proposed computational guidance law. Illustrative examples with several types of weighting functions, including smooth, piecewise, nonsmooth, and discontinuous functions, are provided to demonstrate the advantages and capacity of the proposed guidance law.

Design analysis and experimental validation of a multi‐level inverter for vehicle to home application

AbstractRobust and economic inverting devices are needed with the increasing practice of direct current (DC) sources. This paper proposes a compact multi‐level inverter topology for Vehicle to Home (V2H) application under different load conditions. A reduced number of switches with a single DC source (EV battery) is used to generate the 11‐level of output voltage and current waveform. The proposed topology is quite simple in structure and ensures the increase of two levels with each additional switch. The complete power loss analysis is performed for the proposed inverter. The MATLAB/Simulink is used to perform the simulation, and the results are validated through the experimental hardware setup designed. The comparison with the existing multi‐level inverter (MLI) topologies and literature shows the significant acceptability and reliability of the proposed multi‐level inverter. The operation of the proposed MLI under various loading conditions has expressed robustness for V2H application. The simulated and experimental data confirm that both results have a good agreement in terms of THD and output power. The output voltage levels of the proposed inverter could be flexibly varied by employing different numbers of switching capacitors.

Resilient Unscented Kalman Filtering Fusion With Dynamic Event-Triggered Scheme: Applications to Multiple Unmanned Aerial Vehicles

In this article, the resilient unscented Kalman filtering fusion issue is investigated for a class of nonlinear systems under the dynamic event-triggered mechanism where each sensor node transmits the measurement information to its corresponding local filter in an intermittent way. Compared with its static counterpart, the dynamic event-triggered scheme is capable of scheduling the frequency of data transmission in a more efficient way, thereby better reducing communication burden and energy consumption. In addition, for each local filter, the variation of the filter gain is characterized by a multiplicative noise term. To cope with the intractable problem of computing the cross covariance between local filters, the sequential covariance intersection fusion strategy is introduced into the proposed distributed fusion framework. Finally, the proposed algorithm is applied to a maneuvering target tracking scenario with multiple unmanned aerial vehicles, and both numerical simulations and hardware experiments are provided to elucidate the effectiveness and practicality of the proposed filtering scheme.

Incipient Fault Diagnosis for DC–DC Converter Based on Multi-Dimensional Feature Fusion

To effectively recognize the incipient potential faults caused by degradation of multiple components in DC-DC converters, a fault diagnosis method that involves multi-dimensional feature fusion and sensitive feature extraction is proposed. Firstly, the time-domain statistical characteristics of fault and normal samples are extracted. The KL divergence and normalized kurtosis of intrinsic mode functions (IMFs) between them are calculated by empirical mode decomposition (EMD). In order to further improve the feature discrimination, a sensitive feature extraction method based on Mahalanobis distance (SFMD) is designed to screen out the key features. Finally, the sensitive features are used to construct the SA-LSSVM (Simulated annealing-Least squares support vector machine) model to realize the fault diagnosis. The accuracy of fault diagnosis in simulation and hardware experiment are 99.61% and 97.93% respectively. Compared with other fault diagnosis and feature selection methods, the proposed method still has higher accuracy and better engineering practicability.

Novel Zero-Voltage Zero-Current Transition Buck Converter With Minimal Impact of Active Auxiliary Cell on Overall Dynamics

This paper presents a novel zero-voltage zero-current transition DC/DC buck converter, which uses an active auxiliary resonant network to achieve soft switching operation of semiconductor switches and soft-recovery of power diodes over wide output power range and offers high efficiency. The auxiliary cell in the proposed converter does not cause any additional current stress on the semiconductor switches and has minimal impact on overall dynamics of the converter. Steady-state performance of the converter has been presented with detailed theoretical analysis of all operating modes. The design of auxiliary cell components and development of small signal model of the converter have been carried out from the mathematical equations depicting its dynamic behaviour. A closed loop voltage mode controller with a type III compensator has been developed for the converter to achieve the desired transient response under the influence of external disturbances. Power loss analysis and superiority of the proposed converter over other conventional configurations are also presented here. Finally, soft-switching behaviour and step-input transient response of the converter are verified by hardware experimentation on a 150W, 100 kHz prototype model. The experimental measurements have successfully validated the theoretically predicted behaviour of the converter.

Model-Based Robot Control with Gaussian Process Online Learning: An Experimental Demonstration

Autonomous robotic systems are increasingly deployed in operating environments with partially known and time-varying dynamics, which requires control algorithms to adapt their behavior accordingly. While it has been shown that this adaptivity can be safely realized by employing model-based control laws in combination with Gaussian process online model learning, the practical implementation usually requires approximations which do not admit theoretical guarantees needed for safe deployment. In this work, we address this discrepancy between theory and practice by demonstrating the practical applicability of Gaussian process-based online learning with theoretical guarantees in a real-world robotic experiment. For this purpose, we propose an online learning control architecture, which employs locally growing random trees of Gaussian processes with prediction error bounds. Moreover, we improve the practical applicability of this method by introducing an approach for online hyperparameter optimization. This allows us to demonstrate the effectiveness of Gaussian process online learning with prediction error bounds for control in hardware experiments.

入力外乱を考慮した最適化に基づくドローンネットワークの分散群れ制御

This paper presents a distributed flocking control methodology for a group of multirotor UAVs (drones) under input disturbances. The proposed method follows Reynolds's three flocking rules: cohesion; alignment; and separation. Each drone determines its velocity control input by solving a fully distributed optimization problem with the constraints for these three rules. Here, the conflict between cohesion and separation is handled by adding a relaxation term to the constraint for cohesion. Moreover, the effect of input disturbances, evaluated in preliminary experiments, is incorporated into the constraint for separation. The effectiveness of the proposed flocking control method is demonstrated via simulation and hardware experiments.

Dynamical Analysis of Hybrid-Scale Bifurcation in One-Cycle Controlled Single-Inductor Dual-Output Buck DC–DC Converters

In this paper, the hybrid-scale bifurcation evolutionary phenomena in one-cycle controlled (OCC) single-inductor dual-output (SIDO) Buck DC–DC converter are investigated thoroughly. First, the single-cycle and crossing-cycle iterative mapping models of the OCC SIDO converter in four operation modes are derived to describe the dynamical information under different topology switching sequences. Second, the evolutionary trend of hybrid-scale bifurcation behaviors is identified by numerical simulation. Third, bifurcation analysis based on the proposed models is performed by the trajectories of the eigenvalues, and significantly, the occurrence mechanism of hybrid-scale bifurcations in the evolutionary phenomena is explained in detail. And then, the participation factor is used to analyze the relationship between the variables and the eigenvalues. Especially, the relative parameter sensitivity of the iterative mapping model is proposed to evaluate the influence of such key parameters as load resistors on the eigenvalues so that plenty of design-oriented information will be provided to avoid the hybrid-scale bifurcation behaviors. Finally, PSpice circuit simulation and hardware experiments are carried out to verify the analysis results.

Phantom Car Attack Detection via Passive Opportunistic RF Localization

A novel opportunistic approach of passive RF localization is presented for detecting “phantom car” attacks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., vehicles intentionally reporting false position/velocity information to surrounding vehicles and communication networks. Current state-of-the-art approaches for vehicle localization mostly rely on either: (i) self-reported position/velocity updates obtained via navigation technologies such as GPS, or (ii) cooperative communication approaches involving multiple vehicles exchanging situational awareness information with each other. In both cases, these approaches assume that all vehicles involved truthfully share their actual location information, which might not be the case if malicious users are present within the traffic flow. The proposed approach does not make this assumption. Rather, it extracts location information of vehicles operating on the road without the need to cooperate with them. Multiple sensors surrounding these vehicles are opportunistically and passively measured for their RF emission characteristics ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., received signal strength, time difference of arrival) based on using on-board widely available wireless signals ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., 5G, Bluetooth, WiFi). These RF emissions are not intentionally designed to be used in localization applications. Their characteristics are processed via data fusion and bounded via vehicle dynamics behavioral models before being compared against reported positions within the vehicular communications network. This paper presents the phantom car attack model used in this work to compromise conventional vehicle localization techniques, as well as the framework and its respective sub-components implementing the proposed detection approach. To evaluate the feasibility of the proposed approach, a custom-built Python-based computer simulation platform is described that accurately models the vehicular environment and its associated RF emissions characteristics. Finally, a simple hardware field experiment evaluating the performance of the proposed approach incorporating RF localization, data fusion, and vehicle behavioral dynamics modules illustrates the viability of identifying phantom car attacks within a non-cooperative framework.

Comparison and discussion of intelligent optimization algorithms based on magnetic localization systems for improving the initial value defects of the L-M Algorithm

Magnetic localization techniques are used in in vivo therapy, drug delivery, and surgical localization widely due to their ability to locate in an unobstructed environment precisely. The Levenberg-Marquardt (L-M) algorithm is one of the most common magnetic localization algorithms due to its fast optimization speed and high precision, but it requires high initial values such that improper initial values can easily lead to local optimization. In this paper, the performance of ten commonly used intelligent optimization algorithms is compared based on the mathematical model of the permanent magnet. The final position of the magnet is obtained by a fusion algorithm that uses the result of the intelligent optimization algorithm as the initial value of L-M. The real-time localization accuracy of this fusion algorithm is verified on a hardware platform and experiments demonstrate that the integration of the TSA algorithm and L-M algorithm fusion yields higher performance of the magnetic positioning system.

Гетерогенные сети с ячеистой топологией: технологии, моделирование, прототипирование сетевых устройств

The article deals with the problem of choosing technologies, modeling tools, software and hardware for the implementation of heterogeneous, wireless, peer-to-peer computer networks for the infrastructure of the Internet of things. The features of experimental software and hardware development of network devices are analyzed. Recommendations are given regarding the choice of network technologies for organizing wireless networks of mesh topology, the use of modeling tools and the selection of a hardware base for the implementation of projects of network devices.

Sub-Nyquist Sampling and Measurement of FRI Signals With Additive Shot Noise

To mitigate the impact of shot noise on the parameter measurement process of Finite Rate of Innovation (FRI) signals, this paper proposes a sub-Nyquist modulation sampling system with two measurement channels. The noisy signal is sampled in the main channel after being filtered by a sinc kernel to obtain the aliasing Fourier coefficients, whereas the FRI signal is shifted out of the baseband with a cosine modulation function in the accessory channel to expose the shot noise information to the low frequency band. The burst time parameters of shot noise can be estimated using the annihilating filter method from the samples in the accessory channel, while the parameters of the FRI signal will be measured from the observations in the main channel after the shot noise information has been removed. If the Rate of Innovation of the FRI signal is ρ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>L</i></sub> and is affected by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> impulses of shot noise, the entire measurement process only requires ρ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>L</i></sub> ∙ τ + 2 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> points to be sampled per unit time τ. To enhance the robustness of the measurement process in actual circuit systems, a corrected annihilating filter method is proposed, and the parameter measurement error when employing this method is analyzed. Numerical and hardware experiments demonstrate that, compared to classical anti-noise algorithms, the proposed method not only measures the parameters of FRI signals contaminated by shot noise more accurately, but also significantly improves the robustness of the measurement process in non-ideal environments.

Coded Illumination for Improved Lensless Imaging

Mask-based lensless cameras can be flat, thin, and light-weight, which makes them suitable for novel designs of computational imaging systems with large surface areas and arbitrary shapes. Despite recent progress in lensless cameras, the quality of images recovered from the lensless cameras is often poor due to the ill-conditioning of the underlying measurement system. In this paper, we propose to use coded illumination to improve the quality of images reconstructed with lensless cameras. In our imaging model, the scene/object is illuminated by multiple coded illumination patterns as the lensless camera records sensor measurements. We designed and tested a number of illumination patterns and observed that shifting dots (and related orthogonal) patterns provide the best overall performance. We propose a fast and low-complexity recovery algorithm that exploits the separability and block-diagonal structure in our system. We present simulation results and hardware experiment results to demonstrate that our proposed method can significantly improve the reconstruction quality.