Advanced Schemes Research Articles

Nonlinear Model Predictive Control with L1 Cost-Function Using Neural Networks for Multivariable Processes*

This work presents a computationally Efficient Model Predictive Control (MPC) algorithm for nonlinear Multiple Input Multiple Output (MIMO) processes in which the sum of absolute values of predicted control errors (the L1 norm) is minimized rather than the typically used sum of squared errors (the L2 norm). An approximator of the absolute value function combined with an advanced online trajectory linearization scheme is used to obtain a computationally uncomplicated algorithm that requires solving online quadratic optimization tasks. For a nonlinear MIMO neutralization process, we show that the described algorithm gives control quality comparable to that possible in MPC with nonlinear optimization. We examine the effectiveness of polynomial and neural approximation of the absolute value function. Moreover, we show that the described algorithm gives better control quality than the classical approach to MPC with the L2 cost-function.

A polar-switchable and controllable negative phototransistor for information encryption.

Anomalous negative phototransistors have emerged as a distinct research area, characterized by a decrease in channel current under light illumination. Recently, their potential applications have been expanded beyond photodetection. Despite the considerable attention given to negative phototransistors, negative photoconductance (NPC) in particular remains relatively unexplored, with limited research advancements as compared to well-established positive phototransistors. In this study, we designed ferroelectric field-effect transistors (FeFETs) based on the WSe2/CIPS van der Waals (vdW) vertical heterostructures with a buried-gated architecture. The transistor exhibits NPC and positive photoconductance (PPC), demonstrating the significant role of ferroelectric polarization in the distinctive photoresponse. The observed inverse photoconductance can be attributed to the dynamic switching of ferroelectric polarization and interfacial charge transfer processes, which have been investigated experimentally and theoretically using Density Functional Theory (DFT). The unique phenomena enable the coexistence of controllable and polarity-switchable PPC and NPC. The novel feature holds tremendous potential for applications in optical encryption, where the specific gate voltages and light can serve as universal keys to achieve modulation of conductivity. The ability to manipulate conductivity in response to optical stimuli opens up new avenues for developing secure communication systems and data storage technologies. Harnessing this feature enables the design of advanced encryption schemes that rely on the unique properties of our material system. The study not only advances the development of NPC but also paves the way for more robust and efficient methods of optical encryption, ensuring the confidentiality and integrity of critical information in various domains, including data transmission, and information security.

Limited Feedback Design Based on Kronecker Product Codebook for Massive MIMO Systems

To realize massive multiple-input multiple-output (MIMO) systems, the uniform planar array (UPA) structure has been adopted to deploy a large number of antennas in a limited area of a radio unit at a base station. Subsequently, the Kronecker product (KP) codebook, consisting of horizontal and vertical-domain codebooks, was introduced to enable efficient quantization and feedback for the channel state information (CSI) under the UPA structure. In this paper, we propose advanced limited feedback schemes based on the KP codebook for massive MIMO systems. First, we propose one feedback bit allocation scheme to maximize the beamforming gain. Eigenvalues of spatial correlation channels are used to determine the numbers of feedback bits for horizontal and vertical-domain CSI, and the numbers of feedback bits to maximize the beamforming gain are derived as a closed-form expression. Moreover, we propose a novel feedback and pilot transmission scheme to reduce both the feedback and pilot overhead. The proposed feedback and pilot transmission scheme utilizes the property that the small-scale fading information can be compressed only to the horizontal-domain CSI when the spatial correlation of the vertical-domain channel is high. Simulation results show that, with the use of the proposed feedback bit allocation, the beamforming gain increases by up to 65% compared with the fixed feedback bit allocation when the horizontal and vertical-domain spatial correlation coefficients are equal to 0.57, and the numbers of horizontal and vertical-domain antennas are 8 and 12, respectively. In addition, it is also shown that the proposed feedback and pilot transmission scheme improves the ergodic rate by up to 48% compared with conventional feedback schemes.

Learning Nonlocal Sparse and Low-Rank Models for Image Compressive Sensing: Nonlocal sparse and low-rank modeling

The compressive sensing (CS) scheme exploits many fewer measurements than suggested by the Nyquist–Shannon sampling theorem to accurately reconstruct images, which has attracted considerable attention in the computational imaging community. While classic image CS schemes employ sparsity using analytical transforms or bases, the learning-based approaches have become increasingly popular in recent years. Such methods can effectively model the structure of image patches by optimizing their sparse representations or learning deep neural networks while preserving the known or modeled sensing process. Beyond exploiting local image properties, advanced CS schemes adopt nonlocal image modeling by extracting similar or highly correlated patches at different locations of an image to form a group to process jointly. More recent learning-based CS schemes apply nonlocal structured sparsity priors using group sparse (and related) representation (GSR) and/or low-rank (LR) modeling, which have demonstrated promising performance in various computational imaging and image processing applications.

Sensor fault-tolerant control for wind turbines: an iterative learning method

The combined wind speed estimator and tip speed ratio (WSE-TSR) tracking control scheme is widely used to regulate power production for large-scale modern wind turbines. Although very effective, such an advanced control scheme, based on the prior model information, is highly dependent on external measurements. For partial-load region control, the only external information involved is commonly the measured rotor or generator speed. Inaccuracy in such sole measurement results in an unintended turbine operation and might lead to sub-optimal power production and instability. This paper presents a fault-tolerant control (FTC) method, which aims to eliminate the sensor fault effects for modern wind turbine systems. To fulfil this goal, an iterative learning scheme is proposed to detect and estimate the multiplicative sensor fault, on which an adaptive FTC law is formulated such that the effects of the sensor fault are eliminated. Case studies show that the proposed iterative learning FTC method performs well in detecting, estimating, and accommodating the sensor fault under realistic turbulent wind conditions. The advanced wind turbine controller can maintain its control performance even under faulty conditions, preventing further damage to other turbine components and allowing for continuous power production.

Flower pollination global peak search algorithm for partially shaded solar photovoltaic system

Photovoltaic (PV) systems are required to function at their maximum power point (MPP) to fully utilize solar energy. Solar PV panels are configured with maximum power point tracking (MPPT) systems to enhance the generation and supply of the maximum available power. However, owing to external environmental factors and partial shading conditions (PSCs), the tracking process becomes complex. Conventional MPPT methods cannot always achieve the global MPP. Thus, an advanced metaheuristic MPPT scheme for PV systems, the flower pollination algorithm (FPA), is implemented in this study to find the best global duty cycle to obtain the maximum power output, and a Levy flight is utilized to enhance FPA convergence. The FPA MPPT model was established, and its performance was compared with the conventional, and some meta-heuristic-based MPPT techniques; perturb and observe (PO), incremental conductance (INC), particle swarm optimization (PSO), cuckoo search (CS), grey wolf optimization (GWO), genetic algorithm (GA), double integral sliding mode control (DISMC), and hill-climbing (HC). Different scenarios were considered for the simulation: partial shading, complex partial shading, long-width, and long-length partial shading, and different temperature conditions employing series and series-parallel PV array configurations. In addition, real-world field atmospheric irradiance data for Daejeon city is used for the study. In terms of efficiency, the FPA attains at most 83%–96% of the expected GMP from the PV array as compared with the other MPPT techniques in all scenarios considered. The adopted FPA obtained the following root mean square error (RMSE), mean relative error (MRE), and mean absolute error (MAE) values respectively for each Scenario as follows: Scenario I: RMSE: 0.0061, MRE: 0.0463, MAE: 6.0745e-06, Scenario II: RMSE: 0.0116, MRE: 0.0430, MAE: 1.0738e-05, Scenario III: RMSE: 0.0640, MRE: 0.206, MAE: 4.8239e-05, Scenario IV (varying temperature conditions): RMSE: 0.0062–0.0074, MRE: 0.0460–0.0548, MAE: 6.1333e-06 – 7.4922e-06. A unique sensitivity performance comparison analysis is done employing the power spectral analysis to validate the effectiveness of the adopted FPA to the other MPPT techniques. The FPA performs better than all the techniques in all scenarios in comparing their maximum power tracked, convergence speed and accuracy, system efficiency, and optimum statistical results attained.

A survey of multi-agent Human–Robot Interaction systems

This article presents a survey of literature in the area of Human–Robot Interaction (HRI), specifically on systems containing more than two agents (i.e., having multiple humans and/or multiple robots). We identify three core aspects of “Multi-agent” HRI systems that are useful for understanding how these systems differ from dyadic systems and from one another. These are the Team structure, Interaction style among agents, and the system’s Computational characteristics. Under these core aspects, we present five attributes of HRI systems, namely Team size, Team composition, Interaction model, Communication modalities, and Robot control. These attributes are used to characterize and distinguish one system from another. We populate resulting categories with examples from the recent literature along with a brief discussion of their applications. We also analyze how these attributes in multi-agent systems differ from the case of dyadic human–robot systems. Through this survey, we summarize key observations from the current literature, and identify challenges and promising areas for future research in this domain. In order to realize the vision of robots being part of the society and interacting seamlessly with humans, there is a need to expand research on multi-human–multi-robot systems. Not only do these systems require coordination among several agents, they also involve multi-agent and indirect interactions which are absent from dyadic HRI systems. Including multiple agents in HRI systems requires more advanced interaction schemes, behavior understanding and control methods to allow natural interactions among humans and robots. In addition, research on human behavioral understanding in mixed human–robot teams also requires more attention. This will help formulate and implement effective robot control policies in HRI systems with large numbers of heterogeneous robots and humans; a team composition reflecting many real-world scenarios.

4 × 25 Gb/s PAM4 optical transmitter for micro‐ring modulator with thermal control

AbstractThe explosive growth of bandwidth demands of data center and super‐computing systems spur the appliance and development of optoelectronic‐based solutions such as micro‐ring modulator (MRM) transmitters, and advanced modulation schemes such as four‐level pulse‐amplitude modulation (PAM4). In this paper, a 4 × 25 Gb/s PAM4 MRM transmitter fabricated in 0.13 μm SiGe BiCMOS technology is presented. It consists of four‐channel MRM driver and thermal control loop. Capacitor and resistor degeneration techniques and inductor peaking techniques are adopted to expand channel bandwidth. Meanwhile, the predistortion technique is utilized to compensate for MRM nonlinearity, and auto gain control (AGC) circuits to enable the MRM driver to realize a fixed differential output swing of 3.5 Vpp. Furthermore, a thermal control method based on analog to digital converter, digital to analog converter, and logic control circuits is proposed to compensate for the temperature sensitivity of MRM. The measurement results show that the 25 Gb/s nonreturn‐to‐zero optical eye achieves an extinction ratio of 5.63 dB, and the PAM4 eye can be observed up to 40 Gb/s.

Protection Schemes in HPON Networks Based on the PWFBA Algorithm.

In this paper, possibilities for network traffic protection in future hybrid passive optical networks are presented, and reasons for realizing and utilizing advanced network traffic protection schemes for various network traffic classes in these networks are analyzed. Next, principles of the Prediction-based Fair Wavelength and Bandwidth Allocation (PFWBA) algorithm are introduced in detail, focusing on the Prediction-based Fair Excessive Bandwidth Reallocation (PFEBR) algorithm with the Early Dynamic Bandwidth Allocation (E-DBA) mechanism and subsequent Dynamic Wavelength Allocation (DWA) scheme. For analyzing various wavelength allocation possibilities in Hybrid Passive Optical Networks (HPON) networks, a simulation program with the enhancement of the PFWBA algorithm is realized. Finally, a comparison of different methods of the wavelength allocation in conjunction with specific network traffic classes is executed for future HPON networks with considered protection schemes. Subsequently, three methods are presented from the viewpoint of HPON network traffic protection possibilities, including a new approach for the wavelength allocation based on network traffic protection assumptions.

Development of cryogenic undulators and measurement equipment for PETRA IV

DESY is planning to upgrade its current storage ring PETRA III into the ultra-low emittance photon source PETRA IV. The current H6BA cell design for the PETRA IV lattice will allow for insertion devices of up to 4 m length in the standard straight sections. In order to allow some beamlines to not only benefit from the performance improvements by the machine but also from the undulators, we started looking into using advanced undulator schemes as well. We decided to start the design of cryogenically cooled permanent magnet undulators (CPMUs) based on (Nd,Pr)FeB magnets for PETRA IV, since this technique has already been proven successfully by various other facilities. We will present the planned design for the magnet keepers, the pole tuning mechanism as well as a testbed for keepers under cryogenic conditions. We will also show a preliminary design for the in-situ Hall-probe bench currently developed for the upcoming PETRA IV CPMUs.

Spatio-Temporal Neural Network for Channel Prediction in Massive MIMO-OFDM Systems

In massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, a challenging problem is how to predict channel state information (CSI) (i.e., channel prediction) accurately in mobility scenarios. However, a practical obstacle is caused by CSI non-stationary and nonlinear dynamics in temporal domain. In this paper, we propose a spatio-temporal neural network (STNN) to achieve better performance by carefully taking into account the spatio-temporal characteristics of CSI. Specifically, STNN uses its encoder and decoder modules to capture the spatial correlation and temporal dependence of CSI. Further, the differencing-attention module is designed to deal with the non-stationary and nonlinear temporal dynamics and realize adaptive feature refinement for more accurate multi-step prediction. Additionally, an advanced training scheme is adopted to reduce the discrepancy between STNN training and testing. Evaluated on a realistic channel model with enhanced mobility and spherical waves, experimental results show that STNN can effectively improve the accuracy of prediction and perform well with respect to different signal to noise ratios (SNRs). Visualization and testing for unit root illustrate STNN is able to learn CSI time-varying patterns by alleviating series non-stationarity.

Finite-Control-Set Model Predictive Control of Permanent Magnet Synchronous Motor Drive Systems—An Overview

Permanent magnet synchronous motors (PMSMs) have been widely employed in the industry. Finite-control-set model predictive control (FCS-MPC), as an advanced control scheme, has been developed and applied to improve the performance and efficiency of the holistic PMSM drive systems. Based on the three elements of model predictive control, this paper provides an overview of the superiority of the FCS-MPC control scheme and its shortcomings in current applications. The problems of parameter mismatch, computational burden, and unfixed switching frequency are summarized. Moreover, other performance improvement schemes, such as the multi-vector application strategy, delay compensation scheme, and weight factor adjustment, are reviewed. Finally, future trends in this field is discussed, and several promising research topics are highlighted.

Distributed power flow controller based on fuzzy-logic controller for solar-wind energy hybrid system

The demand of electricity globally led to the concept of renewable energy resources for power generation that are eco-friendly and freely available from nature. The solar photovoltaic systems and wind-based power generators are considered as primary renewable resources and are called as Distributed Generation units as they are scattered in nature. These are operated with bidirectional converters by providing auxiliary services at grid side and load side in either mode of microgrid operation. Besides, the DC power generation units’ integration gets converted into AC system by means of inverters. These types of systems not only increase voltage and current harmonics, power frequency deviations but also drive the distribution system to risky operating zone. This emphasizes the stipulation of advanced control schemes for microgrid architecture. Consequently, power electronic converters introduce harmonics in the system and affect the system performance. To report these expanded issues, the authors recognized an advanced custom power device entitled distributed power flow controller. The proposed hybrid solar-wind energy system is first studied with a distributed power-flow controller. Later the system is examined by replacing proportional integral controller with fuzzy logic controller (FLC) for shunt control of distributed power-flow controller. To validate the investigations, MATLAB/Simulink software is used.

Theoretical study of the disc chopping apparatus of the feed chopper

The paper presents the results of a study in the kinematics and dynamics of a multi-blade disc cutting device. In the advanced hardware scheme, the clipping process is performed as punches. We have a straight blade edge rotated 200 degrees back from the radial direction.As a result, the sliding motion process is reduced and the trunk is easily cut. For this reason, it is advisable to reduce the sliding process when cutting a hard stem, and it is preferable that the blade be made in the form of a hyperbolic spiral, and not an Archimedes spiral, the curvature of which is directed towards the stem, and not in a straight line.Kinematic and dynamic studies performed on a multi-knife disk shearing device accelerate the technological process of the device, increase the pressure of the blade on the trunk, and make it possible to cut it intensively.

Exploiting Liquid Surface Tension in Microrobotics

Surface tension effects are known to be dominant at the submillimeter scale. Within this context, the literature has extensively described the underlying physics (e.g., surface tension, wetting, surface texturation, and coatings), and capillary forces have been exploited in a variety of applications (e.g., capillary picking, self-alignment, capillary sealing, and capillary bearings). As several stimuli can be used to control liquid menisci, these forces have been used mainly in microrobotics in open loop (i.e., without real-time feedback). However, at least two major sources of uncertainty hinder these forces from working properly in open loop: the variability due to contact-angle hysteresis (the difference between wetting and unwetting) and the variability in the involved volume of liquid. To be able to reject these disturbances, successful sensor integration and associated advanced control schemes need to be embedded in capillary microrobotic microsystems. This article analyzes research contributions in this field from three different perspectives: the stimulus action of the surface tension effect (light, B-field, etc.), the application field (actuation, picking, sealing, etc.), and the sensing and control schemes. Technologically complex developments coexist with elegant and straightforward engineering solutions. Biological aspects of surface tension are not included in this review.

Advanced Modulation Scheme of a Dual-Active-Bridge Series Resonant Converter (DABSRC) for Enhanced Performance

This paper proposes a two-degree-of-freedom (2-DoF) modulation technique for the efficient optimization of an open-loop three-phase dual-active-bridge series resonant converter (3P-DABSRC). The efficiency and performance of an conventional dual-active-bridge (DAB) converter decrease when it is operated over a wide range of voltage gain. The efficiency and performance of a DAB converter depend upon the switching and conduction losses. Circulating current is the main cause of conduction loss, and hard switching of active switches adds a switching loss. To increase the performance of DAB converters, the first objective is to minimize the conduction loss, and the second objective is to reduce the switching loss. Still, unfortunately, it is not easy to achieve these two objectives simultaneously. Circulating current helps us to reduce the switching loss, but the unbridled amount of circulating current will increase the root-mean-square inductor tank current, and as a result, the conduction loss will be increased. This paper presents an advanced modulation scheme for a 3P-DABSRC that can be used not only in low-power applications, but also in high-power applications. The DABSRC consists of a series LC resonant tank, isolated high-frequency transformer, and dual active bridge connected with the primary and secondary sides of the transformer. The proposed 2-DoF modulation technique not only minimizes the circulating current, but also eliminates the switching loss. Keeping the minimum phase shift between the primary and secondary bridges reduces the circulating current, and thus, all switches can be operated with zero-voltage switching (ZVS) for the entire power range. The power is controlled by changing the switching frequency from 45 to 63 kHz. To confirm the proposed topology and modulation scheme, a 1500 W DABSRC, which interfaces a 300 V DC bus with a 75 V DC bus, is simulated. A loss model of the proposed topology is also made to verify the results. The simulation results are used to confirm the proper operation of the 3P-DABSRC.

Blue, green, and turquoise pathways for minimizing hydrogen production costs from steam methane reforming with CO2 capture

Rising climate change ambitions require large-scale clean hydrogen production in the near term. “Blue” hydrogen from conventional steam methane reforming (SMR) with pre-combustion CO2 capture can fulfil this role. This study therefore presents techno-economic assessments of a range of SMR process configurations to minimize hydrogen production costs. Results showed that pre-combustion capture can avoid up to 80% of CO2 emissions cheaply at 35 €/ton, but the final 20% of CO2 capture is much more expensive at a marginal CO2 avoidance cost around 150 €/ton. Thus, post-combustion CO2 capture should be a better solution for avoiding the final 20% of CO2. Furthermore, an advanced heat integration scheme that recovers most of the steam condensation enthalpy before the CO2 capture unit can reduce hydrogen production costs by about 6%. Two hybrid hydrogen production options were also assessed. First, a “blue-green” hydrogen plant that uses clean electricity to heat the reformer achieved similar hydrogen production costs to the pure blue configuration. Second, a “blue-turquoise” configuration that replaces the pre-reformer with molten salt pyrolysis for converting higher hydrocarbons to a pure carbon product can significantly reduce costs if carbon has a similar value to hydrogen. In conclusion, conventional pre-combustion CO2 capture from SMR is confirmed as a good solution for kickstarting the hydrogen economy, and it can be tailored to various market conditions with respect to CO2, electricity, and pure carbon prices.

A HEURISTIC COMPUTING APPROACH USING SEQUENTIAL QUADRATIC PROGRAMMING TO SOLVE THE FIFTH KIND OF INDUCTION MOTOR MODEL

The purpose of the current investigation is to solve the fifth kind of induction motor model using an advanced computational scheme by operating the artificial neural networks (ANNs), global scheme as genetic algorithm (GA) along with the rapid local search sequential quadratic programming technique (SQPT), i.e. ANN-GA-SQPT. ANNs are implemented to discretize the fifth kind of induction motor model to express the merit function based on the mean square error. The numerical presentation of the proposed ANN-GA-SQPT is pragmatic for three different problems based on the fifth kind of induction motor model to authenticate the efficacy, consistency and importance of the proposed ANN-GA-SQPT. Moreover, statistical representations are provided in order to check the precision, convergence and accuracy of the present ANN-GA-SQPT.

Analysis of Peak to Average Power in the 5G NOMA-FBMC Waveform

Objectives: In this work, we investigate suitable techniques to reduce the Peak to Average Power Ratio (PAPR) for advanced modulation schemes in order to obtain better performance than current or commonly used modulation schemes for Fourth Generation (4G) and Fifth Generation (5G). Methods: The proposed scheme incorporates a combination of Selective Mapping (SLM) and Partial Transmission Scheme (PTS) and thereby efficiently minimizes the PAPR and the complexity of the framework. Further, it is seen that the proposed algorithm is crucial to achieving better spectral and power characteristics compared with the existing waveforms. Findings: The comparative results of the bit error rate (BER) and PAPR of the advanced SLM-PTS when applied to the OFDM, FBMC, NOMA, and NOMA-FBMC structures are shown, and it is found that the power and complexity are significantly decreased in the advanced waveforms, which makes the proposed algorithm efficient for the advanced waveforms. Novelty: A natural motivation for future modulation schemes is to harmoniously merge the newer modulation technique, Filter Bank Multi Carrier (FBMC), with the Non-Orthogonal Multiple Access (NOMA) framework. This has led to a recent modulation paradigm called FBMC-NOMA, wherein the NOMA power domain principle is applied to a group of FMBC modulated signals. The proposed SLM-PTS-based NOMA-FBMC structure efficiently enhances the throughput and PAPR performance for 5G and beyond 5G systems. Keywords: PAPR; FBMC; SLM; PTS; NOMA

Characterization of the Thermal Time Constants of HAMR Media

Here we describe experimental spin stand measurements as well as simulations of the time constants associated with thermal transients in heat-assisted magnetic recording (HAMR) media. The thermal transients associated with both increasing and decreasing steps in laser power contain at least two time constants each, one on the order of 1 ns and another on the order of 10 ns. These time constants are associated with the central thermal peak in the media and the diffuse thermal background, respectively. In addition, the measured thermal fall time is longer than the thermal rise time due to the disk’s motion relative to the head. Whereas in conventional HAMR a constant laser power is applied during writes, a number of proposed advanced recording schemes use pulsed laser power to improve either recording performance or reliability. The thermal time constants reported here suggest care must be taken when implementing such schemes so that transition locations are not shifted to the detriment of areal density capability.