Switching Parameters Research Articles

Adaptive control of T-S fuzzy systems with Markov switching parameters through observer-based sliding mode approach

This paper addresses the problem of observer-based adaptive sliding mode control (SMC) for nonlinear Markov jump systems (MJSs), using a Takagi-Sugeno (T-S) fuzzy model with the premise variables of fuzzy rules depend on system state. Firstly, an integral sliding surface is designed based on fuzzy observer system; Secondly, sufficient conditions for stochastic stability and $ H_{\infty} $ perturbation attenuate level are provided for the obtained sliding mode dynamics and error systems using linear matrix inequality techniques. Furthermore, an adaptive SMC law is combined with the observed state variables to ensure finite time reachability of the sliding surface. Lastly, the theory is validated with simulations based on a practical example.

A non-invasive approach to the resistive switching physical model of ultra-thin organic-inorganic dielectric-based ReRAMs.

The race to next-generation non-volatile memory is on, and ultra-thin (<5 nm) organic-inorganic hybrid dielectric-based ReRAMs are a top contender. However, their extremely small thickness hinders their processability through material characterization techniques, leaving gaps in our understanding of the resistive switching (RS) dynamics in the hybrid dielectric layer. Furthermore, the poor uniformity of key switching parameters remains a persistent issue in ReRAMs, which impedes any trends to be clearly defined through electrical characterization. This work uses electrical manipulation through a ramped-pulse series (RPS) method to improve the voltage and resistance fluctuations in the reset process of ultra-thin Al/Hf-hybrid/Ni devices. By analyzing their electrical behavior under different pulse and temperature conditions, we propose a comprehensive physical model that describes the operating mechanism of the device. Our results confirm the coexistence in the conductive filament (CF) of a hybrid metallic portion composed of Al and Hf3Al2 and an oxygen vacancy portion. The vacancies are found to play a significant role in RS, with most of them generated during the CF forming process and participating to different degrees in the filament rupture of the RPS-processed and non-RPS-processed devices via Joule heating, drift, and Fick forces. Additionally, we identify the cause of switching failure events to be based on the presence of an Al2O3 interlayer in the Al/Hf-hybrid interface.

Investigation of Optical-Switching Mechanism Using Guided Mode Resonances

Recently, photonic crystals have paved the way to control photonic signals. Therefore, this research numerically investigated the design of the optical switch using the guided-mode resonances in photonic crystals operating in a communication window around 1.55 μm. The design of the device is based on a dielectric slab waveguide to make it compatible with optical waveguides in photonic circuits. Moreover, two signals are used and are termed as the data signal and control signal. The data signal is coupled into the optical waveguide using an out-of-the-plane vertical coupling mechanism, whereas the control signal is index-guided into the optical waveguide to amplify the data signal. The switching parameters of the optical switch are adjusted by changing the number of the photonic crystal periods and implementing a varying radius PhC-cavity within the middle of the PhC-lattice, where the optical characteristics in terms of resonant wavelength, reflection peaks, linewidth, and quality factor of the data signal can be adjusted. The numerical simulations are carried out in open-source finite difference time domain-based software. Congruently, 7% optical amplification is achieved in the data signal with a wavelength shift of 0.011 μm and a quality factor of 12.64. The amplification of the data signal can be utilized to implement an optical switching mechanism. The device is easy to implement and has great potential to be used in programmable photonics and optical integrated circuits.

Electrical characteristics and conductive mechanisms of AlN-based memristive devices

Aluminum nitride (AlN) memristive devices have attracted a great deal of attention because of their compatibility with the CMOS fabrication technology, and more likely to be extended to power electronic devices. However, the conductive mechanism and the variability of resistance switching (RS) parameters are major issues for commercial applications. In this paper, we have obtained electrical characteristics of the Al/AlN/Pt memristors under the current compliance limits of 1 𝜇𝜇𝜇𝜇 and 10 𝜇𝜇𝜇𝜇, respectively. Furthermore, the statistics of switching parameters has been done in the Set and Reset processes. Finally, a quantum point contact model has been developed to account for conducting mechanisms and shows the evolution of the conductive filament during RS transitions.

Stop and go strategy for Lagrange stability of quaternion‐valued memristive neural networks

This paper introduces a novel approach to study the quaternion‐valued memristive neural networks and uses this method to investigate the Lagrange stability of the memristive system. By utilizing the logical switching signal, a stop and go strategy is proposed, which makes the model translated into a system contain two different switching parameters; thus, the derived conclusions contain much more information of the states. Moreover, the difficulty of comparing the “size” of quaternions can be avoided. In the case of discrete‐time system, a very useful lemma is presented, which makes the analysis of the discrete‐time quaternion‐valued memristive network easy to operate. Through simulations, the main results regarding to the Lagrange stability are illustrated.

Gestalt road to Necker cube perception

Abstract The study of cases of illusory or unstable perception of some visual stimuli allows exploration of the psychology of perception of the surrounding world. The wired construction known as “Necker cube” is one such stimulus: it can be perceived as a cube whose front face is seen higher than the back face or vice versa. The switch can occur intentionally or spontaneously. The investigations were focused on switching parameters, relation of the switching to eye position, pre-history, and environment. Here we define that the kernel of the problem is recognizing the 2D drawing as a 3D Necker cube. To this end, we have expanded Gestalt's psychology methods that allow us to recognize 2D figures in drawings for recognizing 3D figure in a flat drawing (including the Necker cube). The presented algorithm for recognizing the cube based on the imitation principle allowed the development of the model of switching between two possible perceptions of the Necker cube. The paper shows that the predictions are in conformity with previously available experimental data. The results confirm the imitation principle of perception, and suggest expanding our research on perception to a wider class of 3D figures, opening a window into the internal processes of perception.

Fault Detection for Semi-Markov Switching Systems in the Presence of Positivity Constraints.

The fault detection issue is investigated for complex stochastic delayed systems in the presence of positivity constraints and semi-Markov switching parameters. By choosing a mode-dependent fault detection filter (FDF) as a residual generator, the corresponding fault detection is formulated as a positive [Formula: see text] filter problem. Attention is focused on the design of a mode-dependent FDF to minimize the error between the residual signal and the fault signal. The designed FDF features good sensitivity of the faults and robustness against the external disturbances. Subsequently, by means of the linear copositive Lyapunov functional (LCLF), stochastic stability is proposed to satisfy an expected [Formula: see text]-gain performance. Some solvability conditions for the desired mode-dependent FDF are established with the help of a linear programming approach. Finally, an application example of a data communication network model is provided to demonstrate the effectiveness of the theoretical findings.

Hysteresis in the Dynamics of Employment by Activity Sector

ABSTRACT Hysteresis, referring to the property of a system whereby some temporary shocks cause permanent effects, has showed to be important to explain the dynamics of aggregate employment. What is not yet addressed properly in the literature is the differentiation of hysteresis effects according to activity sectors. This subject is important in order to understand whether severe recessions, like the one that followed the COVID-19 pandemic with its recognized heterogeneous immediate effects at the sectoral level, have permanent effects, and prospectively for applying adequate stabilizing fiscal and monetary policies, and possibly other specific measures, to prevent excessive market shocks even if they are perceived to be temporary. For this purpose, we apply the play hysteresis model, which describes a dynamic process whereby non-convex adjustment costs create intervals of weak reaction of employment to forcing variables, and spurts in employment in reaction to large or cumulative small labour demand shocks. The model is implemented by means of a switching employment equation. The switching parameter is interpreted as an ‘employment band of inaction’ that summarizes the magnitude of the hysteretic effects. We have found significant hysteresis effects in the employment dynamics across sectors, and these are especially important for industry.

A Statistical Study of Resistive Switching Parameters in Au/Ta/ZrO2(Y)/Ta2O5/TiN/Ti Memristive Devices

Variability is an inherent property of memristive devices based on the switching of resistance in a simple metal–oxide–metal structure compatible with the standard complementary metal–oxide–semiconductor fabrication process. For each specific structure, the variability should be measured and assessed as both the negative and positive factors for different applications of memristive devices. In this report, it is shown how this variability can be extracted and analyzed for such main parameters of resistive switching as the set and reset voltages/currents and how it depends on the methodology used and experimental conditions. The obtained results should be taken into account in the design and predictive simulation of memristive devices and circuits.

Diversified sine–cosine algorithm based on differential evolution for multidimensional knapsack problem

The sine–cosine algorithm (SCA) is one of the simplest and efficient stochastic search algorithms in the field of metaheuristics. It has shown its efficacy in solving several real-life applications. However, in some cases, it shows stagnation at local optima and premature convergence issues due to low exploitation ability and insufficient diversity skills. To overcome these issues from the SCA, its enhanced version named ISCA is developed in this paper. The proposed ISCA is designed based on modifying the original search mechanism of the SCA and hybridizing it with a differential evolution (DE) algorithm. The search procedure in the ISCA switches between the modified search mechanism of the SCA and DE based on the evolutionary states of candidate solutions and a parameter called the switch parameter. The modified SCA enhances the exploitation ability and convergence speed, while the DE maintains the diversity of the population to avoid local optimal solutions. The parameters of the ISCA are tuned in such as way that they could balance the exploration and exploitation features. Validation of the ISCA is conducted on a benchmark set of 23 continuous optimization problems through different performance measures, which reveals its effectiveness as a better optimizer for continuous optimization problems. Furthermore, the proposed ISCA is extended to develop its efficient binary version named BISCA for solving multidimensional knapsack problems. A benchmark collection of 49 instances is used for the performance evaluation of the BISCA. Comparison of results produced by the BISCA with other algorithms and previous studies indicates its better search efficiency and verifies it as an effective alternative for solving the MKP.

Boundary dynamics of a non-smooth memristive Hindmarsh-Rose neuron system.

In this paper, the complex dynamics of non-smooth Hindmarsh-Rose (HR) neuron system with local active memristors is discussed through the theory of discontinuous dynamical system. Six mapping structures through two boundaries are employed to illustrate the various switching behaviors of the system. The analytical conditions of the HR system for the switching motions, such as passable motion and grazing motion, are investigated, and the extreme multistability of the improved HR neuron is revealed through switching bifurcations, parameter maps, and attraction basins. The firing activities of various modes in the HR system and the coexistence of various firing behaviors are observed by changing the initial values. Furthermore, the hardware circuit implement of the HR system is established to validate the numerical results.

Energy- and time-controlled switching of ultrashort pulses in nonlinear directional plasmonic couplers.

We propose an ultracompact nonlinear plasmonic directional coupler for switching of ultrashort optical pulses. We show that this device can be used to control the routing of ultrashort pulses using either the energy or the duration of each individual pulse as switching parameters. The coupler is composed of two cores of a nonlinear dielectric material embedded into metallic claddings. The intricate nonlinear spatiotemporal dynamics of the system is simulated by the finite-difference time-domain technique.

Reinforcement learning-based modified cuckoo search algorithm for economic dispatch problems

One of the crucial tasks of social development is carbon neutrality, which is mainly due to the emission of greenhouse gases. However, thermal power, which produces plenty of harmful gases, is still the main component of electric energy. Therefore, Economic Dispatch (ED) is proposed to utilize energy resources more efficiently and reduce the cost of power generation. ED is a nonlinear and nonconvex-constrained optimization problem that is difficult to optimize. In this paper, we propose a Reinforcement Learning-based Modified Cuckoo Search algorithm (RLMCS) to solve ED problems. The proposed algorithm employs the concept of Reinforcement Learning (RL) and develops an RL-based method to process population obtained from the explorative phase. The RL-based method can dynamically enhance the population based on cumulative rewards and the current environmental state. Thus, the comprehensive search ability of RLMCS has been well improved. Moreover, some proven technologies, i.e., Gaussian random walk, quasi-opposition learning, and adaptive switch parameter, are introduced to further enhance the efficiency of RLMCS. The performance of the RLMCS is tested on standard ED problems (6 and 11 units) and ED problems with valve-point effects (10, 14, and 40 units). RLMCS is also compared with some well-established CS variants. The experimental results have demonstrated that RLMCS is more competitive and robust.

A Novel Asymmetric Car Following Model for Driver-Assist Enabled Vehicle Dynamics

Adaptive cruise control (ACC) vehicles are proving to be the first generation of driver-assist enabled vehicles. In order to study the impacts of ACC vehicles on string stability and traffic flow characteristics, accurately calibrating microscopic car following models is crucial to simulate inter-vehicle dynamics. While many car following models have been used to simulate car following behavior, a single, continuous function may not describe both acceleration and braking realistically. We propose an asymmetric model which is based on the symmetric optimal velocity relative velocity (OVRV) model and switch parameters under different conditions to realize and reproduce car following dynamics of ACC vehicles. We conduct an analytical string stability analysis and the string stability criterion is derived. The calibration and simulation results show that the proposed asymmetric ACC model reduces model spacing error by up to 38% compared with the symmetric OVRV model. Compared with other commonly used asymmetric car following models in the transportation community, the proposed asymmetric ACC model can reduce spacing error by 44.8%. Furthermore, we validate the derived string stability criterion with a numerical test simulating with a string of vehicles. We conclude that an asymmetric car following model shows more accurate performance in the capture of ACC car following behavior.

Fuzzy SMC for Quantized Nonlinear Stochastic Switching Systems With Semi-Markovian Process and Application.

This article is concerned with the issue of quantized sliding-mode control (SMC) design methodology for nonlinear stochastic switching systems subject to semi-Markovian switching parameters, T-S fuzzy strategy, uncertainty, signal quantization, and nonlinearity. Compared with the previous literature, the quantized control input is first considered in studying T-S fuzzy stochastic switching systems with a semi-Markovian process. A mode-independent sliding surface is adopted to avoid the potential repetitive jumping effects. Then, by means of the Lyapunov function, stochastic stability criteria are proposed to be dependent of sojourn time for the corresponding sliding-mode dynamics. Furthermore, the fuzzy-model-based SMC law is proposed to ensure the finite-time reachability of the sliding-mode dynamics. Finally, an application example of a modified series dc motor model is provided to demonstrate the effectiveness of the theoretical findings.

A Hamming Weight Calculation of Binary String in One nMOS Transistor–One Ag/HfO2/Black Phosphorus/Pt Memristor

Memristors hold promise for efficient digital computing and information storage due to their nanoscale size and nonvolatile storage capability. However, the randomness of the area formed by the conductive filaments (CFs) causes unstable switching parameters of the memristor. In this study, a new double-layer structure with Ag/HfO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> /black phosphorus/Pt was designed on a silicon substrate to improve the performance of the memristor. The results show that the devices have highly stable state switching and concentrated resistance distribution characteristics, better retention capacity, and better endurance. In addition, we designed one transistor–one memristor (1T–1M) structure based on the performance of the memristor and used it to calculate the Hamming weight (HW) of the binary string. The results show that the average error of the current values is only 1.04% and 1.01% for the 4- and 8-bit binary strings, respectively, and that there is an excellent linear fit between the output current and the HWs. The error rate and calculation stability are better than the existing calculation methods. This work provides new ideas in efficient physical computing and applications in areas such as information coding.

Quasi-reflection based multi-strategy cuckoo search for parameter estimation of photovoltaic solar modules

To efficiently model, evaluate and track the maximum power point on Photovoltaic (PV) systems, it is of great significance to estimate appropriate model parameters. In this study, a Quasi-Reflection based Multi-Strategy Cuckoo Search algorithm (QRMSCS) is proposed for accurate parameter estimation in different PV systems. In QRMSCS, the Quasi-Reflection operation is introduced as the initialization operation to create a promising search area and improve the convergence speed. Besides, the previous Lévy flight is replaced by a three-period search strategy consisting of multi-dimension learning, Gaussian distribution, and single-dimension learning to ensure search ability and balance the exploration and exploitation. Meanwhile, the adaptive switch parameter Pa is also applied instead of the constant one to improve convergence speed. The optimization of QRMSCS is firstly validated on the benchmark test briefly. Then, the performance of QRMSCS in parameter estimation problems is evaluated in detail through standard PV models and two datasets at different temperature and irradiance. Extensive experimental results prove that QRMSCS is the most robust and superior algorithm compared with other included algorithms. Consequently, QRMSCS is a promising algorithm that can be utilized in future practical applications.

Synchronization of multiple reaction–diffusion memristive neural networks with known or unknown parameters and switching topologies

This paper investigates the synchronization problems of multiple reaction–diffusion memristive neural networks (MRDMNNs) with known or unknown parameters and switching topologies via distributed control. In complex environment, the proposed system may face switching topologies, diffusion effects, external disturbance, parameter mismatch or uncertainty, so designing security control laws to synchronize the proposed system is a enormous challenge. The parameter mismatch terms herein are extended to be unbounded, which is different from the general boundedness hypothesis. Since the parameter mismatch term would be known or unknown, we design a novel parameter-dependent controller for the known condition and a novel parameter-independent adaptive controller for the unknown condition respectively. By constructing new Lyapunov functions, using some properties of M-matrix and robust control method, the synchronization errors will all converge into a bound set via the two designed nonlinear controllers. And we are able to select appropriate control parameters to control the upper bound. Moreover, the obtained theorems can determine the feedback control gains, which can be used for security control. Finally, two numerical simulations are given to support the obtained theoretical results.

Enhanced resistive switching performance of hafnium oxide-based devices: Effects of growth and annealing temperatures

Although hafnium oxide-based resistive switching (RS) devices have been explored extensively, extremely high operating voltages and currents hinder their application in low power operations. In this study, the growth temperature and annealing conditions have been varied to significantly improve the characteristics of hafnium oxide-based RS devices. Deposited using a rf magnetron sputtering at different temperatures, HfOx films are subsequently subjected to vacuum annealing to create a high density of oxygen vacancies. It is observed that the crystallinity, defect density, and RS device performance of these films are influenced by these thermal treatments. The HfOx film annealed at 450 ºC exhibited bipolar RS with the substantial improvement in the device performance, such as electroforming, set/reset voltages, and operating currents as low as ~1 V, 0.5/− 0.5 V, and ~1 µA, respectively, in addition to large ON/OFF ratio> 103, good endurance (>103 cycles), and retention capability (>104 s). The localized conductive filaments composed of oxygen vacancies are found to be responsible for the RS behavior in Al/HfOx/FTO devices, and the simple vacuum annealing approach considerably increases the proportion of oxygen vacancies. With the insights into the influence of annealing temperatures on the switching parameters, this work paves the way for the design of low-power devices for data storage applications.

Input–Output Finite-Time Asynchronous SMC for Nonlinear Semi-Markov Switching Systems With Application

This article investigates the input–output finite-time stability (IO-FTS) for nonlinear hidden semi-Markov switching systems (S-MSSs) via the asynchronous sliding mode control (SMC) approach. Under the assumption that a detector is set up to estimate the mode value, the mode of the original system is not directly accessible. The asynchrony between the system and controller is described as a hidden semi-Markov model (HSMM). Many practical factors, such as semi-Markov switching parameters, finite-time interval, asynchronous phenomenon, uncertain parameters, and nonlinearity, are taking into account during the SMC design process. We aim to design an efficient finite-time asynchronous SMC scheme under a hidden semi-Markov switching effect. A novel sliding switching surface (SSS) is constructed, in which the SMC law rises asynchronously with original S-MSSs. Then, by means of finite-time stability, an asynchronous SMC law is synthesized to guarantee that the associated hidden S-MSSs fulfill the reaching condition within a finite time. Furthermore, sufficient conditions are derived in view of the IO-FTS of sliding mode dynamics under the framework of a novel inequality lemma. Results are given for the application of this control design method to a single-link robot arm model (SLRAM).