Multi-input Single-output Research Articles

Joint Beamforming Design for Intelligent Omni Surface Assisted Wireless Communication Systems

Intelligent reflecting surface (IRS) has been widely considered as one of the key enabling techniques for future wireless communication networks owing to its ability of dynamically controlling the phase shift of reflected electromagnetic (EM) waves to construct a favorable propagation environment. While IRS only focuses on signal reflection, the recently emerged innovative concept of intelligent omni-surface (IOS) can provide the dual functionality of manipulating reflecting and transmitting signals. Thus, IOS is a new paradigm for achieving ubiquitous wireless communications. In this paper, we consider an IOS-assisted multi-user multi-input single-output (MU-MISO) system where the IOS utilizes its reflective and transmissive properties to enhance the MU-MISO transmission. Both power minimization and sum-rate maximization problems are solved by exploiting the second-order cone programming (SOCP), Riemannian manifold, weighted minimum mean square error (WMMSE), and block coordinate descent (BCD) methods. Simulation results verify the advancements of the IOS for wireless systems and illustrate the significant performance improvement of our proposed joint transmit beamforming, reflecting and transmitting phase-shift, and IOS energy division design algorithms. Compared with conventional IRS, IOS can significantly extend the communication coverage, enhance the strength of received signals, and improve the quality of communication links.

A Study on the Impact of Integrating Reinforcement Learning for Channel Prediction and Power Allocation Scheme in MISO-NOMA System.

In this study, the influence of adopting Reinforcement Learning (RL) to predict the channel parameters for user devices in a Power Domain Multi-Input Single-Output Non-Orthogonal Multiple Access (MISO-NOMA) system is inspected. In the channel prediction-based RL approach, the Q-learning algorithm is developed and incorporated into the NOMA system so that the developed Q-model can be employed to predict the channel coefficients for every user device. The purpose of adopting the developed Q-learning procedure is to maximize the received downlink sum-rate and decrease the estimation loss. To satisfy this aim, the developed Q-algorithm is initialized using different channel statistics and then the algorithm is updated based on the interaction with the environment in order to approximate the channel coefficients for each device. The predicted parameters are utilized at the receiver side to recover the desired data. Furthermore, based on maximizing the sum-rate of the examined user devices, the power factors for each user can be deduced analytically to allocate the optimal power factor for every user device in the system. In addition, this work inspects how the channel prediction based on the developed Q-learning model, and the power allocation policy, can both be incorporated for the purpose of multiuser recognition in the examined MISO-NOMA system. Simulation results, based on several performance metrics, have demonstrated that the developed Q-learning algorithm can be a competitive algorithm for channel estimation when compared to different benchmark schemes such as deep learning-based long short-term memory (LSTM), RL based actor-critic algorithm, RL based state-action-reward-state-action (SARSA) algorithm, and standard channel estimation scheme based on minimum mean square error procedure.

Fe3O4 supported UiO-66 (Zr) metal-organic framework for removal of drug contaminants from water: fuzzy logic modeling approach.

The increase in production and consumption of pharmaceuticals and personal care products causes environmental problems. In this study, naproxen and clofibric acid adsorption were studied using Fe3O4-supported UiO-66 (Zr) metal-organic framework (Mag-UiO-66). The adsorption processes were carried out in batch mode at pH value 3.0. The optimum adsorbent quantities, equilibrium periods, pseudo-first-order (PFO), pseudo-second-order (PSO), and intra-particles diffusion kinetic models were calculated. Non-linear Langmuir, Freundlich, Dubinin-Radushkevich (D-R), and Sips isotherm equations were applied to experimental data. Thermodynamic analyses of naproxen and clofibric acid adsorption were also carried out in this study. The Langmuir isotherm qm values were found as 14.15mg/g for naproxen at 308K and 41.87mg/g for clofibric acid at 298K. Both of the adsorption processes were exothermic. MISO (multi-input single-output) fuzzy logic models for removal of both naproxen and clofibric acid adsorptions were designed based on the experimental data to estimate the removal uptake values. It is noteworthy that the results obtained through designed fuzzy logic models matched well with the experimental data and the findings of this study emphasize the validity of designed fuzzy logic models.

Application of Genetic Algorithm in Optimizing LQR Control for Ball and Beam

In this paper, we apply genetic algorithm (GA) to optimize LQR controller – a linear control algorithm which stability is guaranteed by mathematics. This searching algorithm proves its ability in finding better control parameters through generations. Our model is ball and beam (B&B) – a classical single input – multi output (SIMO) system. This system is balanced around equilibrium point in simulation.

Applying DDPG Algorithm to Swing-Up and Balance Control for a Double Inverted Pendulum on a Cart

In this study, we apply the Deep Deterministic Policy Gradient (DDPG) algorithm in reinforcement learning to control a double inverted pendulum on a cart (DIPC)- a high order single input-multi output (SIMO) system . The simulation results demonstrate DDPG's stability and effectiveness in achieving swing-up and balance, showing its potential for tackling challenging control tasks in robotics.

Adaptive-PID Experimental STM32F4 Controller for Rotary Inverted Pendulum

Rotary inverted pendulum (RIP) is a classical model of control engineering. Paper deals with a PID-adaptive structure which is based on structure of neuron to train Kp, Ki, Kd through operation. In simulation, our adaptive controller is proven to work in larger range than classical PID controller. Through experimental model using STM32F4, we prove vibration of system under adaptive-PID is smaller than under classical PID structure. Then, combination of neuron network (NN) and PID control can be used as simple structure for single-input multi output (SIMO) systems which are similar to RIP.

Application of Fuzzy-ANFIS Controller for Ball on Wheel System

The ball-on-wheel (BoW) is a nonlinear single input-multi output (SIMO) system consisting of a ball located on a wheel. The system's challenge is to balance ball at the highest point on the wheel. In this paper, we propose a method of applying ANFIS toolbox in imitating a successful controller (in this case, it is PD controller). This process creates a similar fuzzy controller which controls well this system. Therefore, through this research, ANFIS toolbox shows the ability in imitating an expert‘s control law through collecting data of operations.

A New Single DC Source Five-Level Boost Inverter Applicable to Grid-Tied Systems

In this paper, a single source five-level boost inverter has been proposed and analyzed. The proposed structure includes a five-level inverter and a single-input multi-output (SIMO) boost converter. In this structure, the DC link voltage has been controlled by controlling the implemented DC-DC boost converter. In addition, the inverter switching pattern has been obtained based on the pick current control (PCC) method. In order to test and prove the performance of the proposed structure, this system is tested with the local grid. Since the active and reactive powers have been controlled based on the PCC method, the generated energy in a renewable energy source can be transferred to the local grid with a controlled and high-quality current. In this paper, the proposed structure has been introduced and its operation in different modes has been analyzed completely. Furthermore, the design considerations of the required elements have been investigated. Also, the power loss analysis and the comparison results of the proposed converter with other similar structures have been presented. Finally, in order to test the practical performance of the proposed structure and prove the theoretical analysis, a 620 W laboratory prototype of this structure has been assembled and its performance with the local grid has been tested.

Deep-Chain Echo State Network With Explainable Temporal Dependence for Complex Building Energy Prediction

Building energy prediction plays critical roles in the study of green building and smart city. The most challenging issue is to predict energy demand profiles over multiple time steps, which may have inconsistent timescales. Due to complex temporal dependence, existing prediction approaches cannot satisfy certain requirements in multistep (MS) or multitimescale (MTS) applications. In this article, a deep-chain echo state network (DCESN) is proposed to enhance the mapping capability for the MS demand prediction. The DCESN composed of many submodules of echo state network (ESN) belongs to the single-input multi-output (SIMO) model, and neuron states generated by sequential steps are utilized to prevent accumulative error in the recursive chain. Due to the novel learning mechanism of DCESN, numerical coefficients of temporal dependence are presented to explain the short-term and long-term impacts on future energy consumption. Experimental results in four cases indicate that the proposed DCESN has promising performance on MS and MTS prediction, and temporal dependence can be explained in a visible way. Comparative results of DCESN, sliding-window ESN, and long-short term memory (LSTM) demonstrate that the proposed learning mechanism could prevent error accumulation effectively.

DV-EXCCCII Based Resistor-Less Current-Mode Universal Biquadratic Filter

This study aims to present a new resistor-less current-mode multi-input single-output universal filter. The current-mode’s design approach is used to obtain the proposed circuit. This circuit employs a single differential voltage extra-X current controlled current conveyor (DV-EXCCCII) and two grounded capacitors. This multifunction filter circuit offers low-pass, high-pass, all-pass, band-pass, and band-reject filters at a single output terminal without passive component matching constraints. The same circuit topology can obtain all second-order filter functions with different input conditions. The proposed circuit design is electronically adjustable with the bias current of DV-EXCCCII. Because of its high output impedance, this arrangement is suitable for cascading other current-mode circuits. The proposed circuit is simulated by Cadence Spectre with 0.18 µm UMC CMOS technology process parameters at ± 0.9 V supply voltages. The simulation results agree well with the theoretical concept of the proposed circuit.

Research on surface roughness prediction in turning Inconel 718 based on Gaussian process regression

Nickel-based alloy Inconel 718 is widely used in aircraft engine industry because of its good mechanical properties. Inconel 718 is a typical difficult-to-machine material and its price is relatively expensive. Therefore, accurate prediction of Inconel 718 machined surface roughness with small sample space can improve machining efficiency, optimize process parameters and reduce machining cost. In this paper, a method is proposed to characterize the influence of cutting parameters on roughness by stablishing the corresponding relationship between the proportional hyperparameters in the multivariate kernel function and the cutting speed, cutting deep, feed rate and the rake angle of the tool. A multi input single output (MISO) multivariate Gaussian process regression (GPR) surface roughness prediction model with cutting speed, cutting depth, feed rate and tool rake angle as input variables and surface roughness as output variables is established. The model can not only output the predicted value of surface roughness, but also give the reliability of the predicted value. Experimental results show that the proportional hyperparameter has an independent adjustment function, and the influence of the process parameters characterized by the proportional hyperparameter on the surface roughness is consistent with the experimental results. The experimental results show that the average relative error of MISO multivariate GPR surface roughness prediction model proposed in this paper is 1.5%, which can accurately predict the surface roughness in small sample space.

Nonnegative Consensus Tracking of Networked Systems With Convergence Rate Optimization.

This article investigates the nonnegative consensus tracking problem for networked systems with a distributed static output-feedback (SOF) control protocol. The distributed SOF controller design for networked systems presents a more challenging issue compared with the distributed state-feedback controller design. The agents are described by multi-input multi-output (MIMO) positive dynamic systems which may contain uncertain parameters, and the interconnection among the followers is modeled using an undirected connected communication graph. By employing positive systems theory, a series of necessary and sufficient conditions governing the consensus of the nominal, as well as uncertain, networked positive systems, is developed. Semidefinite programming consensus design approaches are proposed for the convergence rate optimization of MIMO agents. In addition, by exploiting the positivity characteristic of the systems, a linear-programming-based design approach is also proposed for the convergence rate optimization of single-input multi-output (SIMO) agents. The proposed approaches and the corresponding theoretical results are validated by case studies.

Intelligent Reflecting Surface Assisted Wireless Information and Power Transfer With X-Duplex for 6G Networks

Intelligent reflecting surface (IRS) has received widespread attention because it can significantly improve the performance of wireless networks. The X-duplex is a more flexible duplex mode, which can decrease a small sum rate in exchange for a higher quality-of-service performance. In this article, an IRS transmission scheme based on X-duplex isinvestigated to enhance the flexibility and freedom of communication design. We present the X-duplex IRS with the simultaneous wireless information and power transfer, and establish a power consumption model based on real application scenarios. An alternating optimization algorithm is proposed to maximize the energy efficiency (EE) of a multi-input single-output (MIMO) network assisted by the X-duplex IRS. As for the X-duplex relay, we explore an algorithm based on the golden section to optimize the EE. Simulation results comprehensively evaluate the performance of the two auxiliary transmission methods. The X-duplex IRS has higher EE and weighted sum rate (WSR) than the X-duplex relay when sufficient transmission power is available. However, the IRS cannot entirely replace the traditional relay. The X-duplex relay with self-interference energy harvesting achieves a higher WSR and EE when the access point energy is limited or the transmission distance is short.

Secure Control of Networked Inverted Pendulum Visual Servo Systems Based on Active Disturbance Rejection Control

This paper investigates secure control of Networked Inverted Pendulum Visual Servo Systems (NIPVSSs) based on Active Disturbance Rejection Control (ADRC). Firstly, considering the network- and image-induced delays in conjuction with computational errors caused by image processing and image attacks, the model of NIPVSSs is established. Secondly, the limitations of the traditional Single-Input-Single-Output (SISO) ADRC used in NIPVSSs with disturbance are revealed. The limitations are that the ESO used in the traditional SISO ADRC brings large steady-state error, and the NLSEF used in the traditional SISO ADRC can achieve stable control of pendulum angle, but cannot achieve stable control of cart position. Thirdly, a new Single-Input-Multi-Output (SIMO) ADRC method is proposed for NIPVSSs with disturbance. In the new SIMO ADRC method, the new ESO is designed by introducing additional first and second derivatives of error to reduce the steady-state error. In addition, the new NLSEF is developed by taking both the calculated cart position and pendulum angle as inputs to achieve dual stable control of pendulum angle and cart position. Finally, combined with the designed ADRC parameter-tuning strategy, the results from simulation and real-world experiments confirm the effectiveness and feasibility of the proposed method.

Metamutator based universal, single input multi output current mode filter

AbstractThe introduction of Add ‐ Differentiate IC, AD‐IC, a new integrated circuit, into schemes for realizing single input multiple output current mode filters is the motivation behind this paper. In the circuit structure of this filter with only one active device with a minimal number of transistors and five passive components are being used. Non ideal effects of the design are investigated and simulation results of the application, using TSMC 0.25 μm process parameters with ±1.25 V power supply voltage and the bias as 0.8 V resulting in 0.99 mW power dissipation for the entire filter, are also presented to confirm the theoretical analysis. With this new design technique, the tunabilities of the angular frequency ( ) and quality factor ( ) can be performed independently.

Investigation of integrated converter circuit and enriched error tolerant fuzzy logic controller based control approach for solar powered EV system

PurposeThis study aims to extend the driving range by on-board charging with use of photovoltaic (PV) source, avoiding the dependency on the grid supply and energy storage system in addition to that reduce the conversion complexity influenced on converter section of electric vehicle (EV) system.Design/methodology/approachThis paper proposed a PV fed integrated converter topology called integrated single-input multi-output (I-SIMO) converter with enriched error tolerant fuzzy logic controller (EET-FLC) based control technique to regulate the speed of brushless direct current motor drive. I-SIMO converter provides both direct current (DC) and alternating current (AC) outputs from a single DC input source depending on the operation mode. It comprises two modes of operation, act as DC–DC converter in vehicle standby mode and DC–AC converter in vehicles driving mode.FindingsThe use of PV panels in the vehicle helps to reduce dependence of grid supply as well as vehicle’s batteries. The proposed topology has to remove the multiple power conversion stages in EV system, reduce components count and provide dual outputs for enhancement of performance of EV system.Originality/valueThe proposed topology leads to reduction of switching losses and stresses across the components of the converter and provides reduction in system complexity and overall expenditure. So, it enhances the converter reliability and also improves the efficiency. The converter provides ripple-free output voltage under dynamic load condition. The performance of EET-FLC is studied by taking various performance measures such as rise time, peak time, settling time and peak overshoot and compared with conventional control designs.

A multi-input single-output thermal management system design for liquid metal batteries

Liquid metal battery, with three-liquid-layer structure and high operating temperature, is a novel battery technology that shows great potential in grid energy storage. However, unlike lithium-ion batteries, liquid metal batteries are supposed to be heated to a high temperature before operation. Both temperature uniformity among cells and total energy consumption minimization is demanded in the preheating process. In this paper, an efficient thermal management controller design is proposed to optimize the temperature uniformity and energy consumption. Different from previous work, this design replaces the conventional single-input single-output controller with a multi-input single-output controller, which samples temperatures at different locations to control the heating rate. Experimental results show that the final controller error reaches a value of 1.2%. Based on the thermal management system design, a preheating strategy is further proposed to achieve the optimal balance between temperature uniformity and energy consumption. Compared with common strategies, the maximum temperature difference is reduced by 13.7% to 49.85 °C, and the energy consumption is reduced by 6.7% of the total module energy to 6.33 kWh. It is demonstrated in this study that the proposed thermal management system and heating strategy essentially improves the heating process of liquid metal battery stack.

Single-Stage Multi-Input Buck Type High-Frequency Link's Inverters With Multiwinding and Time-Sharing Power Supply

A circuit structure, topological family, and unipolar phase-shift SPWM master-slave power distribution energy management control strategy (EMCS) of the single-stage Buck type multi-input high-frequency (HF) link's inverters with multiwinding time-sharing power supply are proposed in this article, in order to reduce the power conversion stage, volume and weight, and increase conversion efficiency and reliability. The key technologies of this kind of inverter, such as circuit structure and topological family, EMCS, power supply mode (PSM), and steady-state principle characteristics, are deeply studied. The circuit structure is composed of multiple isolated HF inverting bridges, a HF transformer with multi-input single-output, a cycloconverter and an output <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LC</i> filter. The EMCS is to generate the bipolar tristate multilevel SPWM waves by phase-shift between the right arm and the left arm of multiple HF inverting bridges; the cycloconverter demodulates it into the unipolar tristate multilevel SPWM wave, and performs zero voltage commutation. The experimental results of the designed and developed 1kVA multi-input inverter prototype have shown it has the advantages of single-stage power conversion, HF electrical isolation, high conversion efficiency, small volume and weight, high reliability, stable output voltage, multi-input source master-slave power distribution, and smooth switching of different PSMs.

Dictionary Learning-Based Channel Estimation for RIS-Aided MISO Communications

In this letter, we study the channel estimation problem for the reconfigurable intelligent surface (RIS)-aided multi-input single-output (MISO) system. By exploiting the channel sparsity, compressive sensing (CS) based sparse channel estimators can be applied to the system to reduce the training overhead. However, these existing sparse channel estimators adopt predefined dictionaries when formulating the sparse matrix recovery problem, which will cause grid mismatch issues and estimation performance degradation. Hence, in this letter, we formulate the channel estimation problem as a joint dictionary parameter learning and sparse signal recovery problem, in which the dictionary parameter can be optimized to adapt to the channel measurements, thereby improving the robustness of sparse channel representation and estimation performance. Then, we propose an iterative re-weighted algorithm to solve this non-convex problem efficiently. Simulation results show that the proposed algorithm outperforms other benchmark schemes significantly.

Robust identification of multiple-input single-output system response for efficient pickup noise removal from tokamak diagnostics.

Electromagnetic pickup noise in the tokamak environment imposes an imminent challenge for measuring weak diagnostic photocurrents in the nA range. The diagnostic signal can be contaminated by an unknown mixture of crosstalk signals from coils powered by currents in the kA range. To address this issue, an algorithm for robust identification of linear multi-input single-output (MISO) systems has been developed. The MISO model describes the dynamic relationship between measured signals from power sources and observed signals in the diagnostic and allows for a precise subtraction of the noise component. The proposed method was tested on experimental diagnostic data from the DIII-D tokamak, and it has reduced noise by up to 20 dB in the 1-20kHz range.