Single-output Systems Research Articles

Prescribed-time control algorithm: application to a two-link robot arm

This paper proposes a prescribed-time controller that stabilises a single-input single-output first- and an n-order systems. The proposed prescribed-time controller employs a logarithm function in its design, its convergence time is defined by the user, is independent of initial conditions and any other system parameters. The controller is first developed for a first-order system, then for a second- and an n-order systems. In case an external disturbance is present, a sign function through an integral action is employed to reject it. Under minor modifications, the developed controller for single-input single-output systems is extended to perturbed systems with multiple inputs and multiple outputs. The obtained results demonstrate that the proposed control algorithm drives the states to the origin in a prescribed time even in the presence of external disturbances and remain there for future times. A numerical simulation has been carried out on a two-link robot arm to show the effectiveness of the proposed control algorithm.

A Deep Evolution Policy-Based Approach for RIS-Enhanced Communication System.

This paper investigates the design of active and passive beamforming in a reconfigurable intelligent surface (RIS)-aided multi-user multiple-input single-output (MU-MISO) system with the objective of maximizing the sum rate. We propose a deep evolution policy (DEP)-based algorithm to derive the optimal beamforming strategy by generating multiple agents, each utilizing distinct deep neural networks (DNNs). Additionally, a random subspace selection (RSS) strategy is incorporated to effectively balance exploitation and exploration. The proposed DEP-based algorithm operates without the need for alternating iterations, gradient descent, or backpropagation, enabling simultaneous optimization of both active and passive beamforming. Simulation results indicate that the proposed algorithm can bring significant performance enhancements.

Adaptive wireless-powered network based on CNN near-field positioning by a dual-band metasurface

With the improvement of industry, the connectivity of electronic devices gradually shift from wired to wireless. As a solution for power delivery, the non-contact power transfer holds promising ways to charge for moving terminals, enabling battery-free sensing, processing, and communication. Based on a dual-band metasurface, this study proposes an adaptive wireless-powered network (AWPN) to realize the simultaneous wireless localization and non-contact power supply. It first achieves localization with 3 cm resolution on a single-input single-output (SISO) system, by combining space-time-coding (STC) and convolutional neural network (CNN). With precise position information, AWPN real-time aligns power beams to the terminals for stable energy transmission. Then, battery-free terminals enable to perceive the environmental data and uploads the results. From the measurement results, AWPN gets more than 98% CNN classification accuracy and can tolerate certain environmental changes. Thus, being adaptive and contactless, our study will propel the advancement in Internet of Things (IoT), intelligent metasurface, and the robot industry.

Suppression Limit Cycles in 2x2 Nonlinear Systems with Memory Type Nonlinearities

For several decades, the importance and weight-age of prediction of nonlinear self-sustained oscillations or Limit Cycles (LC) and their quenching by signal stabilization have been discussed, which is confined to Single Input and Single Output (SISO) systems. However, for the last five to six decades, the analysis of 2x2 Multi Input and Multi Output (MIMO) Nonlinear Systems gained importance in which a lot of literature available. In recent days’ people have started discussing suppression of LC which limits the performance of most of the physical systems in the world. It is a formidable task to suppress the limit cycles for 2x2 systems with memory type nonlinearity in particular. Backlash is one of the nonlinearities commonly occurring in physical systems that limit the performance of speed and position control in robotics, automation industry and other occasions like Load Frequency Control (LFC) in multi area power systems. The feasibility of suppression of such nonlinear self-oscillations has been explored in case of the memory type nonlinearities. Backlash is a common memory type nonlinearity which is an inherent Characteristic of a Governor, used for usual load frequency control of an inter-connected power system and elsewhere. Suppression LC using pole placement technique through arbitrary selection and optimal selection of feedback Gain Matrix K with complete state controllability condition and Riccati Equation respectively and is done through state feedback. The Governing equation is d/dt [X(t)] =(A-BK) X: which facilitates the determination of feedback gain matrix K for close loop Poles / Eigen values placement where the limit cycles are suppressed/eliminated in the general multi variable systems. The complexity involved in implicit non-memory type or memory type nonlinearities, it is extremely difficult to formulate the problem for 2x2 systems. Under this circumstance, the harmonic linearization/harmonic balance reduces the complexity considerably. Still the analytical expressions are so complex which loses the insight into the problem particularly for memory type nonlinearity in 2x2 system and the method is made further simpler assuming a 2x2 system exhibits the LC predominately at a single frequency. Hence in the proposed work an alternative attempt has been made to develop a graphical method for the prediction of Limit Cycling Oscillations in 2x2 memory type Nonlinear systems which not only reduces the complexity of formulations but also facilitates clear insight into the problem and its solution. The present techniques are well illustrated with an example and validated / substantiated by digital simulation (developed program using MATLAB codes) and use of SIMULINK Tool Box of MATLAB software. The present work has the brighter future scope of: Adapting the Techniques like Signal Stabilization and Suppression LC for 3x3 or higher dimensional nonlinear systems through an exhaustive analysis. Analytical/Mathematical methods may also be developed for signal stabilization using both deterministic and random signals based on Dual Input Describing function (DIDF) and Random Input Describing Function (RIDF) respectively. The phenomena of Synchronization and De-synchronization can be observed/identified analytically using Incremental Input Describing Function (IDF), which can also be validated by digital simulations.

Quenching and Suppression of Limit Cycles in 3x3 Nonlinear Systems

For several decades, the importance and weight-age of prediction of nonlinear self-sustained oscillations or Limit Cycles (LC) and their quenching by signal stabilization have been discussed which is confined to Single Input and Single Output (SISO) system. However, for the last five to six decades, the analysis of 2x2 Multi Input and Multi Output (MIMO) Nonlinear Systems gained importance in which a lot of literature available. In recent days few literatures are available which addresses the exhibition of LC and their quenching/suppression in 3x3 MIMO Nonlinear systems. Poor performances in many cases like Load Frequency Control (LFC) in multi area power system, speed and position control in robotics, automation industry and other occasions have been observed which draws attention of Researchers. The complexity involved, in implicit nonmemory type and memory type nonlinearities, it is extremely difficult to formulate the problem in particular for 3x3 systems. Under this circumstance, the harmonic linearization/ harmonic balance reduces the complexity considerably. Still the analytical expressions are so complex which loses the insight into the problem particularly for memory type nonlinearity in 3x3 system. Hence in the present work a novel graphical method has been developed for prediction of limit cycling oscillations in a 3x3 nonlinear system. The quenching of such LC using signal stabilization technique using deterministic (Sinusoidal) and random (Gaussian) signals has been explored. Suppression LC using pole placement technique through arbitrary selection and optimal selection of feedback Gain Matrix K with complete state controllability condition and Riccati Equation respectively. The method is made further simpler assuming a 3x3 system exhibits the LC predominantly at a single frequency, which facilitates clear insight into the problem and its solution. The proposed techniques are well illustrated with example and validated/substantiated by digital simulation (a developed program using MATLAB codes) and use of SIMULINK Tool Box of MATLAB software. The Signal stabilization with Random (Gaussian) Signals and Suppression LC with optimal selection of state feedback matrix K using Riccati Equation for 3x3 nonlinear systems have never been discussed elsewhere and hence it claims originality and novelty. The present work has the brighter future scope of: i. Adapting the Techniques like Signal Stabilization and Suppression LC for 3x3 or higher dimensional nonlinear systems through an exhaustive analysis. ii. Analytical/Mathematical method may also be developed for signal stabilization using both deterministic and random signals based on Dual Input Describing function (DIDF) and Random Input Describing Function (RIDF) respectively. iii. The phenomena of Synchronization and De-synchronization can be observed/identified analytically using Incremental Input Describing Function (IDF), which can also be validated by digital simulations.

Navigating Decision Making with Generalized Temporal Intuitionistic Fuzzy Sets and Soft Sets

The study addresses the challenges posed by evolving data within generalized intuitionistic fuzzy sets. Traditional methods often fall short in handling such complexity. To overcome this, we introduce the concept of a generalized temporal intuitionistic fuzzy set, extending the traditional framework to incorporate temporal dynamics. Additionally, we define a generalized temporal intuitionistic fuzzy soft set, integrating temporal aspects into the soft set framework. Recognizing the need for advanced operations like union and intersection to merge opinions across different periods, we propose practical solutions for decision-making in this dynamic context. Two novel multi-criteria decision-making methods are introduced, specifically designed to address decision-making problems within generalized temporal intuitionistic fuzzy soft sets. We develop Scilab codes for these methods, enabling the creation of a multiple-input single-output system. This system is applied to complex real-life examples, involving numerous parameters, time moments, and decision-makers. This comprehensive approach aims to provide robust tools and methodologies for decision-making processes amidst the intricate dynamics of temporal data within the framework of generalized temporal intuitionistic fuzzy sets.

Navigating Decision Making with Generalized Temporal Intuitionistic Fuzzy Sets and Soft Sets

The study addresses the challenges posed by evolving data within generalized intuitionistic fuzzy sets. Traditional methods often fall short in handling such complexity. To overcome this, we introduce the concept of a generalized temporal intuitionistic fuzzy set, extending the traditional framework to incorporate temporal dynamics. Additionally, we define a generalized temporal intuitionistic fuzzy soft set, integrating temporal aspects into the soft set framework. Recognizing the need for advanced operations like union and intersection to merge opinions across different periods, we propose practical solutions for decision-making in this dynamic context. Two novel multi-criteria decision-making methods are introduced, specifically designed to address decision-making problems within generalized temporal intuitionistic fuzzy soft sets. We develop Scilab codes for these methods, enabling the creation of a multiple-input single-output system. This system is applied to complex real-life examples, involving numerous parameters, time moments, and decision-makers. This comprehensive approach aims to provide robust tools and methodologies for decision-making processes amidst the intricate dynamics of temporal data within the framework of generalized temporal intuitionistic fuzzy sets.

Use of Fuzzy Logic in Determining the Academic Achievement of Secondary School Students in Mathematics Class

Many studies have been found in the field of education using the fuzzy logic approach. However, no study has been found examining the academic achievement of secondary school students in mathematics. The research data consists of the exam notes of the 1st term 1st and 2nd Mathematics course of the 2023-2024 academic year in Kütahya. The study consists of the exam scores of 876 students studying in 4 secondary schools in the center of Kütahya. To study a fuzzy logic-based two-input and single-output system was designed using Fuzzy Logic Toolbox in the Matlab program. Totally 9 fuzzy rules were created. The academic success was evaluated first with classical method and then with the designed fuzzy-based model. According to the results obtained, it has been observed that there is a high positive relationship between fuzzy logic method and classical logic method. It was academic success score obtained fuzzy logic method was less than classical logic method for grades 5 and 7. But it was obtained fuzzy logic method was more than classical logic method for grades 6 and 8. In conclusion was reached in favor of fuzzy logic due to fuzzy logic method gives more flexible results.

Big Data Analysis of the Speed Performance of a 176k DWT Bulk Carrier in Real Operating Conditions

Assessment of ship performance under in-service conditions is challenging due to the complex effects of many environmental disturbances. ISO 15016 and ISO 19030 standards are commonly used to evaluate ship operating performance. However, ISO 15016 requires numerous variables, a complex calculation formula, and considerable time and cost, and ISO 19030 only evaluates the reduction of ship speed caused by wind and neglects the effect of waves. To improve both standards and achieve a more accurate ship performance assessment, this study proposes a new performance prediction model, the multi-input single-output (MISO) system, which assumes that each ship has specific frequency characteristics according to type and size. Based on this new model, in-service navigation data collected from a 176k DWT bulk carrier, which amount to 5.7 million data points, are analyzed to assess the speed performance of the vessel subject to environmental disturbances. The proposed model was validated by comparing its results with ISO 19030 and specifically assessing the speed–power curves and speed reduction measured in operational data with the influence of environmental disturbances removed.

Hybrid Beamforming Structure Using Grouping with Reduced Number of Phase Shifters in Multi-User MISO

This paper proposes a novel hybrid beamforming (HBF) structure for gain-aware grouping transmit antennas and users in multiuser multiple-input single-output (MU-MISO) systems. In the conventional HBF structure, all transmit antennas form a beam to each user. In this case, the gain of each antenna varies depending on the location of the base station and each user, and the transmit power after the digital beamformer is allocated to the antenna with the smallest gain. Signals transmitted from antennas with small gains are susceptible to noise and interference. Therefore, this paper proposes an HBF structure in which only the antenna with the highest gain forms the beam for each user. In the proposed scheme, the transmitting antennas are grouped and the beam is formed only by the group of antennas with the highest gain for each user. Simulation results show that the proposed scheme can reduce the number of phase shifters used on the transmit side compared to the conventional HBF scheme while maintaining sum-rate performance when the number of transmit antennas and users are the same. It was also shown that there is a trade-off between the reduction in the number of phase shifters used to form the beam and the improvement in performance as the number of transmit antennas increases. Furthermore, it is shown that when antenna selection is used, although there is a trade-off between the number of phase shifters and performance improvement, the number of phase shifters can be reduced while maintaining performance even when the number of transmit antennas increases.

A comprehensive control paradigm for prescribed performance attainment in complex nonlinear systems

This paper presents a comprehensive control framework tailored for achieving Prescribed Performance Control (PPC) in the context of complex nonlinear systems, addressing multifaceted challenges prevalent in control design. The focus is on a control scenario characterized by the simultaneous presence of nonlinearity, external disturbances, time delay, and unknown control direction—issues that pose considerable obstacles for existing solutions. To surmount these challenges, our proposed approach integrates well-established techniques, including neural networks, Nussbaum-type gains, and adaptive control strategies within a unified control design framework. The specific technical challenge addressed in this work involves the effective management of these intricate complexities in Single-Input Single-Output (SISO) systems. Our contributions extend the theoretical foundations, presenting an ideal PPC control design and introducing two adaptive neural network-based control methods capable of accommodating both known and unknown control directions. Utilizing Lyapunov–Krasovskii functionals, we showcase a unique integration that surpasses a mere combination of individual techniques. This work advances the theoretical underpinnings of control engineering tailored for real-world scenarios. The proposed controller’s efficacy is validated through rigorous simulations and compared with recent results and benchmark PPC controllers, establishing its superiority in addressing the intricacies of complex control scenarios.

Intelligent-Reflecting-Surface-Assisted Single-Input Single-Output Secure Transmission: A Joint Multiplicative Perturbation and Constructive Reflection Perspective.

Due to the inherent broadcasting nature and openness of wireless transmission channels, wireless communication systems are vulnerable to the eavesdropping of malicious attackers and usually encounter undesirable situations of information leakage. The problem may be more serious when a passive eavesdropping device is directly connected to the transmitter of a single-input single-output (SISO) system. To deal with this urgent situation, a novel IRS-assisted physical-layer secure transmission scheme based on joint transmitter perturbation and IRS reflection (JPR) is proposed, such that the secrecy of wireless SISO systems can be comprehensively guaranteed regardless of whether the reflection-based jamming from the IRS to the eavesdropper is blocked or not. Moreover, to develop a trade-off between the achievable performance and implementation complexity, we propose both element-wise and group-wise reflected perturbation alignment (ERPA/GRPA)-based IRS reflection strategies, respectively. In order to evaluate the achievable performance, we analyze the ergodic secrecy rate (ESR) and secrecy outage probability (SOP) of the SISO secure systems with the ERPA/GRPA-based JPRs, respectively. Finally, by characterizing the simulated and numerical ESR and SOP performance results, our proposed scheme is compared with the benchmark scheme of random phase-based reflection, which strongly demonstrates the effectiveness of our proposed scheme.

Design of a MIMO implantable antenna with ultra-miniaturized volume and reduced SAR

Abstract This paper presents an ultra-miniaturized skin implantable Multiple Input Multiple Output (MIMO) antenna for biotelemetry in the Industrial, Scientific, and Medical (ISM) band (2.4–2.48 GHz). The two-element configuration exhibits a compact planar volume of 4.5 × 6.2 × 0.25 mm³, with an edge-to-edge distance of 0.8 mm and a 75.85 % size reduction. Fabricated on a 0.25 mm thick Roger 10.2LM high permittivity substrate, the antenna achieves a Fractional Bandwidth (FBW) of 9.27 % and a realized gain of −28.2 dBi, with over 24.3 dB isolation across the entire band. Experimental results in multilayer animal tissue aligned well with simulations. Specific Absorption Rate (SAR) analysis at 1 W input power showed values of 848 W/kg (1-g) and 88.46 W/kg (10-g), yielding allowable input powers of 3.77 mW and 45.2 mW, respectively, adhering to international safety guidelines. The 2 × 2 element antenna demonstrated efficient communication over 7 m with a data rate of 78 Mbps/s. Multiple Input Multiple Output channel analysis revealed a Channel Capacity of 10.8 bps/Hz at a 20 dB SNR, nearly double that of an ideal Single Input Single Output system, making it suitable for high data rate skin-implantable applications.

Prediction of tool wear and surface finish using ANFIS modelling during turning of Carbon Fiber Reinforced Plastic (CFRP) composites

Carbon fiber-reinforced plastics (CFRP) are widely used in various industries due to their high strength to weight ratio, corrosion resistance, durability, and excellent thermo-mechanical properties. The machining of CFRP composites has always been a challenge for the manufacturers. In this study, CNC turning operation with coated carbide tool is used to machine a specific CFRP and the relationship between the cutting parameters (Speed, Feed, Depth of Cut) and response parameters (Vibration, Surface Finish, Cutting Force and Tool Wear) are investigated. An adaptive-network-based fuzzy inference system (ANFIS) model with two multi-input–single-output (MISO) system has been developed to predict the tool wear and surface finish. Speed, feed, depth of cut, vibration and cutting force have been used as input parameters and tool wear and surface finish have been used as output parameters. Three sets of cutting parameter have been used to gather the data points for continuous turning of CFRP composite. The model merged fuzzy inference modeling with artificial neural network learning abilities, and a set of rules is constructed directly from experimental data. This model is capable of predicting the cutting tool wear and surface finish during turning of CFRP composite. The predicted tool wear and surface finish data are compared to the experimental results. The predicted data agreed well with the actual experimental data with 98.96 % accuracy for tool wear and 99.61 % accuracy for surface finish.

Advanced diffractive optical elements implementing multiple-input spatial multiplexing of terahertz radiation

In recent years, there has been growing interest in the utilization of terahertz (THz) technologies in telecommunications applications. In such systems, radiation multiplexing is required. In one of the solutions, multiplexing can be achieved through the application of appropriate passive optical components. In this study, three different designs of passive diffractive optical elements (DOEs) that meet the functionality of spatial multiplexing of THz radiation are presented. The functionality was achieved by combining two spatially separated optical channels into a single optical path with an addition of the focusing effect, creating multiple-input single-output (MISO) system performance. Two structures were designed as various combinations of off-axis segmentation of kinoform focusing lenses with particular shifts. However, the distribution of the third structure was created with the interactive algorithm. The performance of all structures was verified with numerical simulations using propagation based on the modified convolution method, giving promising results. In this study, the novel, specially dedicated method of 3D modeling was applied. MISO structures were manufactured using fused deposition modeling (FDM) 3D printing technology from styrene-butadiene copolymer (SBC) material, which is remarkably transparent for the THz radiation range (its absorption coefficient value is less than 1 cm−1 in the range up to 750 GHz). The manufactured structures were examined in the experimental setup. All three MISO structures demonstrated proper functionality by redirecting and focusing radiation from two spatially separated radiation sources into the single focal spot located on the main optical axis. However, the structure designed with the iterative algorithm significantly surpassed the performance of the other structures. This structure redirected over two times higher intensity in the desired manner and exhibited approximately 68.12% higher relative efficiency compared to the others. The structure designed with an iterative method is recommended for further investigation and future application in 6G telecommunication systems.

A Deep Learning-Based Low-Overhead Beam Tracking Scheme for Reconfigurable Intelligent Surface-Aided Multiple-Input and Single-Output Systems with Estimated Channels

A Deep Learning-Based Low-Overhead Beam Tracking Scheme for Reconfigurable Intelligent Surface-Aided Multiple-Input and Single-Output Systems with Estimated Channels

Quantifying rain-driven NO3-N dynamics in headwater: value of applying SISO system identification to multiple variables monitored at the same high frequency

The nitrate–nitrogen (NO3-N) concentration is a key variable affecting the ecosystem services supported by headwater streams. The availability of such data monitored continuously at a high frequency (in parallel to hydrometric and other water quality data) potentially permits a greater insight into the dynamics of this key variable. This study demonstrates how single-input single-output (SISO) system identification tools can make better use of these high-frequency data to identify a reduced number of numerical characteristics that support new explanatory hypotheses of rain-driven NO3-N dynamics. A second-order watershed managed for commercial forestry in upland Wales (United Kingdom) provided the illustrative data. Fifteen-minute rainfall time series were used to simulate NO3-N concentration dynamics and the potentially associated dynamics in dissolved organic carbon (DOC) and runoff, monitored at the same high resolution for two 30-day periods with a differing temperature regime. The approach identified robust, high-efficiency models needing few parameters. Comparison of only three derived dynamic response characteristics (DRCs) of δ, TC, and SSG for the three variables for the two different periods led to new hypotheses of rain-driven NO3-N dynamics for further exploratory field investigation.

Output fluctuation and overshoot restraining model-free adaptive control for a class of discrete-time nonlinear single-input single-output systems

Output fluctuation and overshoot restraining model-free adaptive control for a class of discrete-time nonlinear single-input single-output systems

A novel class of non-Gaussian system performance assessment and controller parameter tuning methods

Traditional variance-based control performance assessment (CPA) and controller parameter tuning (CPT) methods tend to ignore non-Gaussian external disturbances. To address this limitation, this study proposes a novel class of CPA and CPT methods for non-Gaussian single-input single-output systems, denoted as data Gaussianization (inverse) transformation methods. The idea of quantile transformation is used to transform the non-Gaussian data with the goal of maximizing mutual information into virtual Gaussian data. In addition, optimal system data for the virtual loop are mapped back to the actual non-Gaussian system using quantile inverse transformation. Furthermore, a CARMA model-based recursive extended least square algorithm and a CARMA model-based least absolute deviation iterative algorithm are used to identify virtual Gaussian and non-Gaussian system process models, respectively, while implementing the CPT. Finally, a unified framework is proposed for the CPA and CPT of a non-Gaussian control system. The simulation results demonstrate that the proposed strategy can provide a consistent benchmark judgment criterion (threshold) for different non-Gaussian noises, and the tuned controller parameters have good performance.

INVSID 1.0: An Inverse System Identification Toolbox for MATLAB

The inverse system identification toolbox named INVSID 1.0 for MATLAB, which is used to identify the inversion of single-input single-output systems, is developed. The complete process from theoretical derivation to toolbox creation of developing the toolbox is demonstrated. Afterwards, numerical examples are illustrated to describe how the toolbox can be used to solve inverse identification problems. Simulation results demonstrate the effectiveness of the toolbox.