Single-input And Single-output Research Articles

Event‐triggered model reference adaptive control system design for SISO plants using meta‐learning‐based physics‐informed neural networks without labeled data and transfer learning

SummaryThis paper examines the controllability of a novel Lyapunov‐based model reference adaptive control (MRAC) system designed with a meta‐learning‐based physics‐informed neural network (MLPINN) for linear and nonlinear single‐input and single‐output (SISO) plants without labeled data (MLPINN‐MRAC system). It is devised with the benefits of several techniques: the integration of the identification process in a training mode into an online control mode (straightforward design); no labeled data generation for online identification by a physics‐informed neural network; the prevention of degradation in tracking performance by meta‐learning, as the system triggers a meta‐learning process only when an error threshold detects the deterioration (high efficiency and the reduction of computation cost); quick adaptation to new inputs and an updated control input for each sub‐time span by transfer learning. It is worth noting that the frequency of the meta‐learning event detection significantly affects the step response stability of the nonlinear plant. To achieve a better quality of the stabilization, more frequent event detection is necessary for both beginning and end intervals in control. Sixteen triggering events are enough to shape the acceptable step response of the nonlinear plant, and 44 triggering events achieve the plant's desired step response with its minor modeling error. While, as for the linear plant, a single triggering event is sufficient to attain its tolerable step response and modeling error, implying that intensive event detection is not critical in the identification. It is obvious that the MLPINN‐MRAC system functions well and is more beneficial and efficient for the nonlinear plant.

Stability analysis for large-scale multi-agent molecular communication systems.

Molecular communication (MC) is recently featured as a novel communication tool to connect individual biological nanorobots. It is expected that a large number of nanorobots can form large multi-agent MC systems through MC to accomplish complex and large-scale tasks that cannot be achieved by a single nanorobot. However, most previous models for MC systems assume a unidirectional diffusion communication channel and cannot capture the feedback between each nanorobot, which is important for multi-agent MC systems. In this paper, we introduce a system theoretic model for large-scale multi-agent MC systems using transfer functions, and then propose a method to analyze the stability for multi-agent MC systems. The proposed method decomposes the multi-agent MC system into multiple single-input and single-output (SISO) systems, which facilitates the application of simple analysis technique for SISO systems to the large-scale multi-agent MC system. Finally, we demonstrate the proposed method by analyzing the stability of a specific large-scale multi-agent MC system and clarify a parameter region to synchronize the states of nanorobots, which is important to make cooperative behaviors at a population level.

Experimentation of Multi-Input Single-Output Z-Source Isolated DC–DC Converter-Fed Grid-Connected Inverter with Sliding Mode Controller

Converting devices are quickly becoming the most important part of renewable energy-producing systems that are linked to the grid. Applications that are linked to the grid are the most common place to find usage for two-port power converters that are built using single-input and single-output (SISO) ports. The incorporation of SISO power converters into the grid-connected hybrid system results in an increase in both its size and its cost. Multiple power sources may be connected to a single DC bus by means of hybrid power systems, which make use of multi-input power converters. To combine the hybrid wind and PV system with a common DC bus, this study suggests an isolated multi-input single-output (IMISO) Z-Source converter. It has been determined that the suggested system performs well in spite of dynamic load fluctuations and shifting input voltage circumstances. The sliding mode controller (SMC) has also been used to control a single-phase five-level (SPFL) inverter. The purpose of developing the laboratory prototype model was to verify the proposed IMISO Z-source converter-fed single-phase five-level (SPFL) inverter in the context of the circumstance that is being investigated.

A SISO FMCW radar based on inherently frequency scanning antennas for 2-D indoor tracking of multiple subjects

The contextual non-invasive monitoring and tracking of multiple human targets for health and surveillance purposes is an increasingly investigated application. Radars are good candidates, since they are able to remotely monitor people without raising privacy concerns. However, radar systems are typically based on complex architectures involving multiple channels and antennas, such as multiple-input and multiple-output (MIMO) or electronic beam scanning, resulting also in a high power consumption. In contrast with existing technologies, this paper proposes a single-input and single-output (SISO) frequency-modulated continuous wave (FMCW) radar in combination with frequency scanning antennas for tracking multiple subjects in indoor environments. A data processing method is also presented for angular separation and clutter removal. The system was successfully tested in five realistic indoor scenarios involving paired subjects, which were either static or moving along predefined paths varying their range and angular position. In all scenarios, the radar was able to track the targets, reporting a maximum mean absolute error (MAE) of 20 cm and 5.64^circ in range and angle, respectively. Practical applications arise for ambient assisted living, telemedicine, smart building applications and surveillance.

Performance Analysis of Spatially Multiplexed MIMO System

Abstract: In SISO (single input and single output) system, refers to have only one input and one output. Here only one antenna is used at receiver and transmitter side. SISO are relatively simple to design and implement. They are of low cost and used in wide range of applications. However, SISO systems are poor in performance and deliver low data rate. So we’ll prefer MIMO (multiple input and multiple output) over SISO as it gives better spectral efficiency, improved reliability and good coverage. In MIMO we use multiple antennas at transmitter and receiver side which is known as Spatial Multiplexing(SM) which enhances the data rate. In the paper the performance of spatial combining MIMO system is analysed. Matlab used for simulation.

An improved whale optimization algorithm for the model order reduction of large-scale systems

An improved whale optimization algorithm (IWOA) is developed for the model order reduction (MOR) of large-scale systems (LSS) in this paper. An equivalent reduced order model (ROM) for the higher-order system (HOS) is derived by considering integral square error (ISE) as the objective function using IWOA. Many practical systems of single-input and single-output (SISO) and multi-input and multi-output (MIMO) systems are considered to examine the worth of the proposed technique. The powerfulness and robustness of the proposed design technique are tested on various typical examples. Several simulation results have been reported to demonstrate the efficacy of IWOA. To prove the potentiality of the suggested technique, the results have been compared with the familiar classical MOR techniques as well as other heuristic algorithms available in the literature.

Immersion and invariance based adaptive command filtered backstepping control design for a class of SISO uncertain nonlinear systems in strict-feedback form

In this article, an immersion and invariance based adaptive command filtered backstepping control method (I&I ACFBC) is proposed for a class of single-input and single-output (SISO) uncertain nonlinear systems in strict-feedback form. The problem of the explosion of complexity inherent to the traditional adaptive backstepping control design is solved by applying the proposed adaptive control technique, and the compensating signals are constructed to reduce the effect of the known filtering errors produced by the command filters. The adaptive laws are achieved by an immersion and invariance method. The Lyapunov stability theory is utilized to ensure the boundedness of all the signals in the closed-loop system. The feasibility and validity of the proposed adaptive control scheme are demonstrated by the simulation and experimental results on a DC-DC buck converter system.

Generation of Whole-Body FDG Parametric Ki Images from Static PET Images Using Deep Learning.

FDG parametric Ki images show great advantage over static SUV images, due to the higher contrast and better accuracy in tracer uptake rate estimation. In this study, we explored the feasibility of generating synthetic Ki images from static SUV ratio (SUVR) images using three configurations of U-Nets with different sets of input and output image patches, which were the U-Nets with single input and single output (SISO), multiple inputs and single output (MISO), and single input and multiple outputs (SIMO). SUVR images were generated by averaging three 5-min dynamic SUV frames starting at 60 minutes post-injection, and then normalized by the mean SUV values in the blood pool. The corresponding ground truth Ki images were derived using Patlak graphical analysis with input functions from measurement of arterial blood samples. Even though the synthetic Ki values were not quantitatively accurate compared with ground truth, the linear regression analysis of joint histograms in the voxels of body regions showed that the mean R2 values were higher between U-Net prediction and ground truth (0.596, 0.580, 0.576 in SISO, MISO and SIMO), than that between SUVR and ground truth Ki (0.571). In terms of similarity metrics, the synthetic Ki images were closer to the ground truth Ki images (mean SSIM = 0.729, 0.704, 0.704 in SISO, MISO and MISO) than the input SUVR images (mean SSIM = 0.691). Therefore, it is feasible to use deep learning networks to estimate surrogate map of parametric Ki images from static SUVR images.

Mitigation of Inter-Cell Interference by using MIMO System in Visible Light Communication System

In this paper, the multiple input and multiple output system (MIMO) is investigated to mitigate the inter-cell interference (ICI) in visible light communication (VLC). The interference due to light from neighbor cell deteriorates the channel capacity of the system. Therefore, a MIMO-based VLC system is employed to optimize the capacity especially in ICI areas of the room. In this paper, the capacity of [Formula: see text], [Formula: see text] and [Formula: see text] MIMO systems is investigated and compared with the single input and single output (SISO) channel capacity without interference. The channel capacity improves by increasing the order of MIMO array and it deteriorates due to ICI. However, the effects of ICI are more severe at higher signal-to-noise ratio (SNR) especially at high interference areas. Therefore, the order of MIMO array is investigated at different SNRs in interference regions to achieve the interference-free capacity system. Simulation and theoretical results show that ICI is considerably mitigated by using MIMO system. The high SNR 42, 47 and 51.7 dB is achieved in ICI for [Formula: see text], [Formula: see text] and [Formula: see text] MIMO system, respectively.

Conversion and SISO Equivalence of Impedance Model of Single-Phase Converter in Electric Multiple Units

The direct-quadrature (DQ) model, mirror-frequency impedance (MFI) model, and harmonic state-space (HSS) model are three main modeling methods for single-phase power electronic converter systems. These models are established under three different frameworks: the dq-frame, the mirror-frequency frame, and the extended harmonic domain frame. The purpose of this article is to demonstrate how to create a unified modeling framework and to provide an unambiguous relationship between the impedances in these three domains. The unified model framework facilitates flexible and scalable modeling by making different models to be easily interconnected with other systems. In addition, to simplify the stability analysis and impedance measurement process, the three modeling methods are reduced to first-order single-input and single-output (SISO) equivalent models, respectively, based on the model reduction technique. Finally, the impedance measurement and stability analysis results of electric multiple units (EMUs) accessed vehicle-grid system demonstrate the accuracy of the conversion and SISO equivalence technique.

Area-Based CFAR Target Detection for Automotive Millimeter-Wave Radar

Millimeter-wave (mmWave) radar is critical to the emerging automatous driving. As one important step to estimate the range / velocity of unknown targets, constant false-alarm rate (CFAR) techniques should be firstly applied. Most CFAR methods focus on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">point</i> -based target model (i.e. each target is represented as one point), which may be inadequate for the highly accurate detection scenarios. Owing to the largely improved temporal/spatial resolutions of mmWave radars, each target is now dispersed to many reflection points, covered by a certain area on Range Doppler Map (RDM). In this work, we fully utilize such new information provided by mmWave radars, and develop an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">area</i> -based CFAR framework by fully exploiting the potential diversity gain, with which the detection signal-to-noise ratio (SNR) is substantially improved. Theoretical analysis suggests the achieved SNR gain of our method over traditional algorithms grows as the area size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$S$</tex-math></inline-formula> of each target on RDM, i.e. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ {\mathcal {O}}(S)$</tex-math></inline-formula> . As demonstrated by numerical simulations and real experiments, our method dramatically improves the detection probability of both single-input and single-output (SISO) and multiple-input multiple-output (MIMO) radar systems, also greatly enriching the output point-cloud information for other sophisticated inference tasks. Our method has great potentials in the emerging automotive mmWave radars for highly accurate targets detection.

Enhancing NOMA’s Spectrum Efficiency in a 5G Network through Cooperative Spectrum Sharing

Non-orthogonal multiple access (NOMA) is one of the most effective techniques for meeting the spectrum efficiency (SE) requirements of 5G and beyond networks. This paper presents two novel methods for improving the SE of the downlink (DL) NOMA power domain (PD) integrated with a cooperative cognitive radio network (CCRN) in a 5G network using single-input and single-output (SISO), multiple-input and multiple-output (MIMO), and massive MIMO (M-MIMO) in the same network and in a single cell. In the first method, NOMA users compete for free channels in a competing channel (C-CH) on the CCRN. The second method provides NOMA users with a dedicated channel (D-CH) with high priority. The proposed methods are evaluated using the Matlab software program using the three scenarios with different distances, power location coefficients, and transmitting power. Four users are assumed to operate on 80 MHz bandwidths (BWs) and use the quadrature phase shift keying (QPSK) modulation technique in all three scenarios. Successive interference cancellation (SIC) and unstable channel conditions are also considered when evaluating the performance of the proposed system under the assumption of frequency selective Rayleigh fading. The best four-user SE performance obtained by user U4 was 3.9 bps/Hz/cell for SISO DL NOMA, 5.1 bps/Hz/cell for SISO DL NOMA with CCRN with C-CH, and 7.2 bps/Hz/cell for SISO DL NOMA with CCRN with D-CH at 40 dBm transmit power. While 64 × 64 MIMO DL NOMA improved SE performance of the best-use U4 by 51%, 64 × 64 MIMO DL NOMA with C-CH CCRN enhanced SE performance by 64%, and 64 × 64 MIMO DL NOMA with D-CH CCRN boosted performance by 65% SE compared to SISO DL NOMA at 40 dB transmit power. While 128 × 128 M-MIMO DL NOMA improved SE performance for the best U4 user by 79%, 128 × 128 M-MIMO DL NOMA with C-CH CCRN boosted SE performance by 85%, and 128 × 128 M-MIMO DL NOMA with D-CH CCRN enhanced SE performance by 86% when compared to SISO DL NOMA SE performance at 40 dB transmit power. We discovered that the second proposed method, when using D-CH with CCR-NOMA, produced the best SE performance for users. On the other hand, the spectral efficiency is significantly increased when applying MIMO and M-MIMO techniques.

Saturating PI Control of Stable Nonlinear Systems Using Singular Perturbations

This article presents an antiwindup propor- tional-integral (PI) controller, using a saturating integrator, for a single-input and single-output (SISO) stable nonlinear plant, whose steady-state input–output map is increasing. We prove that, under reasonable assumptions, there exists an upper bound on the controller gain such that for any constant reference input, the corresponding equilibrium point of the closed-loop system is exponentially stable, with a “large” region of attraction. When the state of the closed-loop system converges to this equilibrium point, then the tracking error tends to zero. The closed-loop stability analysis employs Lyapunov methods in the framework of the singular perturbations theory. Finally, we show that if the plant satisfies the asymptotic gain property, then the closed-loop system is globally asymptotically stable for any sufficiently small controller gain. The effectiveness of the proposed PI controller is proved by showing how it performs as part of the control algorithm of a synchronverter (a special type of dc to ac power converter).

Complex Circuit Modeling Methodology and Its Application to Depict Multifrequency Coupling Effect of Asymmetric Grid-Following Converter

Due to the multifrequency coupling effect (m-FCE), precise modeling and effective stability assessment of an asymmetric grid-following converter (GFC)-based ac system are complex to achieve. Traditional small-signal modeling methods and recently reported order-reduction methods could establish multi-input and multioutput (MIMO) and equivalent single-input and single-output (SISO) models to characterize m-FCE effectively. However, revealing the physical implications of coupling components and the generation mechanism of m-FCE are still challenging. Meanwhile, although the amplitude and phase dynamics have been considered in the existing modeling, a concrete conclusion illustrating these dynamics’ impact on m-FCE is still missing. To fill the gaps, this article thoroughly considers the impacts of amplitude and phase dynamics and finds that phase dynamics form additional positive feedback loops to enhance m-FCE. Based on this cognition, an accurate complex circuit modeling framework is proposed. It reveals that m-FCE is generated by the admittance mismatching between conjugated responses and “chain reaction” among coupling components. The proposed method has two merits: 1) it is an SISO modeling approach and helps reduce analysis complexity and 2) its graphical form helps provide a deep physical insight into m-FCE. Simulations and experiments verified the correctness of the proposed method.

Fractional order internal model control of non‐minimum phase and time‐delayed plants

AbstractSeveral applications on fractional order (FO) control have gained considerable significance in the recent years, which led to the evolution of novel tuning strategies of the generalized order FO controllers. Some of the methods in available literatures are based on constrained minimization optimization techniques or analytical method defined only for specific plants. They are valid only for some special model cases. On the contrary, in this technical note, a generalized non‐integer order internal model control (IMC) framework is realized for any order non‐minimum phase (NMP) plants with right half plane (RHP) zero as well as time delayed plants having any finite relative order. Its parameters are graphically interpreted satisfying the frequency domain design stipulations for single input and single output (SISO) higher order linear time invariant (LTI) plants. The performance of the same on a bioreactor fermentation process for its temperature control is found to have outperformed in contrast to its integer order (IO)‐IMC. It is therefore inferred here that this new approach pledges to impart unique solution of the controller parameters, formulating a highly efficient tool outperforming the existing paradigms. Simulation and real time experimentation are presented to validate the method put forward providing satisfactory performance in reference tracking, disturbance rejection, and robustness to various plant parameter perturbations.

Robust adaptive dynamic surface control scheme for a class of single‐input and single‐output uncertain nonlinear systems in strict‐feedback form

SummaryThis article presents a robust adaptive dynamic surface control using σ‐modification adaptation laws for a class of single‐input and single‐output (SISO) uncertain nonlinear systems in strict‐feedback form with parametric uncertainties and external disturbances. The proposed scheme is developed by employing dynamic surface control based robust adaptive control technique. The key features of the approach are that, first, the problem of explosion of complexity inherent in the conventional adaptive backstepping control method is avoided, second, the proposed approach of σ‐modification adaptation laws gives fast and accurate parameter estimation performance, and, third, the closed‐loop signals of the system are proven to be uniformly ultimately bounded (UUB) by using the Lyapunov stability theory. We show the effectiveness of our approach by simulating an electromechanical system.

Capacity Optimization of MISO System in Intercell Interference of Visible Light Communication System

The capacity of multiple input and single out (MISO) system is investigated in intercell interference environment of a visible light communication (VLC) system. The interference is dominating in cell edges and the center of the room due to overlapping of light signals from neighbor cell. The footprint of one cell extends at high SNR, and consequently, this increases the intercell interference regions. Therefore, the channel capacity is greatly deteriorated due to interference power. In this paper, the channel capacity can be improved using diversity gain of MISO-based VLC system. The light-emitting diode (LEDs) of MISO system in a cell can use the same data to improve the diversity gain of the system. The MISO array is derived as per SNR requirements in interference region of the room. The capacity of the MISO system is examined and compared with that of the single input and single output (SISO). A trade-off of the MISO array is obtained at different SNRs to achieve the interference free capacity system. Theoretical and simulation capacity response show that the interference is considerably minimized by using diversity gain of MISO system. The interference free capacity is achieved at 44 dB, 49.5 dB, and 51.7 dB SNR using 4X1, 9X1 and 16X MISO system, respectively.

Performance Analysis Antenna Diversity Technique with Wavelet Transform Using Array Gain for Millimeter Wave Communication System

Utilizing antenna diversity techniques has become a well-known approach to improve the performance of wireless communication systems. Multiple antenna arrays with half-length spacing, such as a uniform linear array (ULA), have been taken into consideration. Since 60 GHz is an unlicensed frequency band and ideal for local propagation, it is where the technology is being used. The transmitter and receiver both accomplish QAM modulation and demodulation. The performance in terms of bit error rate (BER) was tested in MATLAB simulation software for all antenna diversity scenarios: the single input and single output (SISO) DWT, multiple input and single output (MISO) DWT, single input and multiple output (SIMO) DWT, and multiple input and multiple output (MIMO) DWT. The MIMO DWT was shown to be the best of them. The performance of MIMO OFDM using various wavelets was also simulated, and the performance of the Haar wavelet transform was 2 dB better than that of the other wavelet transform. Compared to simulation results, the analytical results showed good agreement with little discrepancy.

An autonomous solar PV system using boost TPC for energy harvesting with mode-based power flow management control

Energy harvesting from environment provides a compelling alternative to battery-powered standalone systems. The rising use of green energy resources in energy sector has a substantial impact on the low voltage DC (LVDC) systems. Multiport converters make it easier to convert renewable energy for use in everyday situations. To ensure uninterruptible power supply, a non-isolated Three-Port Converter (TPC) acts as a gateway for renewable sources, energy storage, and loads. The paper describes a modified power flow management control for a standalone solar PV system using boost TPC with time sharing control and an added mode-based controller for voltage regulation. To operate the TPC effectively in Dual Output (DO), Dual Input (DI) and Single Input and Single Output (SISO) modes, localized controllers are used. The converter small signal model is developed for all operating modes and based on this, sampled data control theory is applied to design the digital controller for load regulation. When the TPC switches from one operating mode to another, the voltage regulation control sets the controller parameters for the appropriate mode. Also, to extend battery lifespan, the control algorithm includes a battery overcharge prevention mechanism. The proposed algorithm is realized in a System on a Programmable Chip (SoPC) using the Field Programmable Gate Array (FPGA) Cyclone-III and Nios-II as processor. The system is tested in real time with solar PV as the source of energy and a lead-acid battery as the storage.

Adaptive composite dynamic surface neural control for nonlinear fractional-order systems subject to delayed input

In the article, the adaptive composite dynamic surface neural controller design problem for nonlinear fractional-order systems (NFOSs) subject to delayed input is discussed. A fractional-order auxiliary system is first designed to solve the input-delay problem. By using the developed novel estimation models, the defined prediction errors and the states of error system can decide the weights of radial basis function neural networks (RBFNNs). During the dynamic surface controller design process, the developed fractional-order filters are designed to handle the complexity explosion problem when the classical backstepping control technique is utilized. It is shown that the designed adaptive composite neural controller ensures that all the system state variables are bounded and the tracking error of the considered system finally tends to a small neighborhood of zero. Finally, the results of the simulation explain the feasibility of the developed controller. In addition, the developed controller can also be applied to single input and single output(SISO) nonlinear systems subject to a unitary input function.