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Summary: There are two ideal memristive devices, first‐order memristor and second‐order memristor, which could be widely applied in nonlinear electronic circuits. The main difference between them is their ϕ − q characteristics. However, there are only a few studies available in the literature about the different dynamic behavior that can be observed when they are connected in the same circuit in turns. In this paper, to demonstrate the different consequences for the above situation, the complex dynamics of a memristive band‐pass filter (BPF) are analyzed in detail as one of the classical first‐order circuits. Also, the existence of coexisting attractors, hysteretic dynamics, and antimonotonicity are confirmed. Subsequently, the impact of memristor coefficients on the working dynamics of BPF is revealed. The conclusion is that a higher‐order memristor can lead to a greater number of chaotic attractors and more complex nonlinearity, which is the basis to determine that substitutability and interchangeability of equivalent memristance do not apply for memristor devices with the different ϕ − q relationship when analyzing memristive circuits. Finally, the experimental simulations are presented to further prove the above theoretical analysis. Additionally, other circuits, such as the Chua circuit and the Jerk circuit, are also investigated in order to verify our analysis. In particular, a full Feigenbaum tree is demonstrated in the Chua circuit. [ABSTRACT FROM AUTHOR]

An impedance network dc–dc boost converter is developed in this letter. Compared to the conventional boost converter, it can reach a higher voltage gain with fewer diodes and a small duty cycle (smaller than 33%) and, meanwhile, avoid instability caused by saturation of its inductors, whereas the conventional boost converters realize high-voltage gains at rather large duty cycles (normally exceeding 50%) resulting in saturation of inductors. Furthermore, it can well fulfill the stringent industrial requirements, particularly of renewable power systems. The proposed converter with continuous conduction modes is analyzed for different states of its components. Then, it is shown how to determine its various parameters. Finally, simulations and a 250-W prototype are conducted to verify the converter's effectiveness. [ABSTRACT FROM PUBLISHER]

In this work, the issue of finite-time synchronization is discussed for complex-valued neural networks (CVNNs) by applying aperiodically intermittent sliding mode control and a non-separation approach. Firstly, to simplify the theoretical analysis process, some new differential inequalities are established, and new estimated time rules are given. Secondly, to avoid the traditional approach of separating CVNNs, a novel complex-valued aperiodically intermittent sliding mode control strategy is designed, which has never been applied to achieve CVNNs synchronization. Furthermore, by adopting the newly established lemmas, novel controllers and Lyapunov functional, some novel finite-time synchronization criteria of CVNNs are acquired. At last, the validity of the resulting conclusions is indicated by some numerical examples. [ABSTRACT FROM AUTHOR]

In this paper, electromagnetic interference in operated switched reluctance motor (SRM) drive is analyzed, which includes mechanism of electromagnetic interference (EMI) noise generation in SRM drive, performance comparison of noise source internal impedance extraction methods. Moreover, the solution of SRM drive EMI noise is optimized by making use of economic integration based decision-making parameters modeling method. The conductive EMI noise in SRM drive is suppressed by the optimized method. The experimental results confirm the effectiveness of the analysis method proposed in this paper. [ABSTRACT FROM PUBLISHER]

Unlike existing single-stage ac/dc converters with uncontrolled intermediate bus voltage, a new single-stage ac/dc converter achieving power factor correction (PFC), intermediate bus voltage output regulation, and output voltage regulation is proposed. The converter is formed by integrating a boost PFC converter with a two-switch clamped flyback converter into a single power stage circuit. The current stress of the main power switch is reduced due to separated conduction period of the two source currents flowing through the power switch. A dual-loop current mode controller is proposed to achieve PFC, and ensure independent bus voltage and output voltage regulations. Experimental results on a 24-V/100-W hardware prototype are given to confirm the theoretical analysis and performance of the proposed converter. [ABSTRACT FROM AUTHOR]

To explore the applications of the memcapacitor in the conservative circuits, the nonlinear dynamics of a memcapacitor-based hyperchaotic conservative circuit are studied in detail. Specifically, the conservative condition of the system is obtained by combining divergence and Hamiltonian energy, and the perpetual points and equilibrium points of the memcapacitor-based system are also analyzed in detail. Subsequently, the influences of system parameters and initial conditions on the dynamics of the memcapacitor-based hyperchaotic conservative system are discussed through the dynamic map and the basin of attraction, where three dynamics phenomena can be observed, such as interior crisis, largest Lyapunov exponent jump, and coexisting conservative flows. Finally, the theoretical results are verified by the circuit experiment simulation through MULTISIM and digital signal processing; a pseudorandom number generator based on the hyperchaotic conservative system is also designed and compared with another system through an NIST test. [ABSTRACT FROM AUTHOR]

Ancillary service provision and peak shaving (PS) play essential roles in the current day‐to‐day power system operation, which is challenged by the increasing renewable generation penetration. Providing these critical services using classical approaches such as peak load generators has been limited due to high operational costs and environmental impacts. The use of battery energy storage systems (BESS) is another popular method that is limited by high initial investment costs and high degradation rates. In this work, a novel approach to utilize industrial loads and BESS to provide multiple power system services in different stages is proposed. Industrial loads such as aluminium crushers are known for their intensive electricity consumption. Nevertheless, when applied in frequency regulation (FR), they perform poorly due to their discrete nature in operation. This drawback and the aforementioned BESS shortcomings are addressed by combining on‐site BESS with plant machinery to provide FR services and recover BESS related costs. Later, depending on the optimal capacity distribution, BESS usage is extended into the energy arbitrage market to provide PS services. This approach resulted in higher earnings for participating customers and network operators, as well as in less CO2${\rm {CO}}_{2}$ emissions, and minimal BESS degradations. An Australian case study of the South West Interconnected System, along with Worsley Alumina refinery data of Western Australia has been used to showcase the model performances. [ABSTRACT FROM AUTHOR]

This article proposes a novel duty ratio regulated virtual voltage vector (VV) model predictive torque control (MPTC) strategy for post-fault eight switch three-phase inverter fed induction motor drive with reduced neutral point (NP) voltage fluctuation. The proposed method contributes to substantially improve the steady-state torque control performance under post-fault conditions. Furthermore, compared with the conventional MPTC, it reduces the torque ripples as well as the NP voltage fluctuations. The mathematical model of eight-switch three-phase post-fault inverter fed induction motor is presented. Based on this, the synthesis of virtual VV is analyzed. Simulation and experimental results are presented to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A new step-up converter with an ultrahigh voltage conversion ratio is proposed in this paper. Two power switches of such a converter, which conduct synchronically, and its output voltage, which has common ground and common polarity with its input voltage, lead to the simple control circuit. No abrupt changes in the capacitor voltage and the inductor current of the proposed step-up converter mean that it does not suffer from infinite capacitor current and inductor voltage. Two input inductors with different values can still allow the proposed step-up converter to work appropriately. An averaged model of the proposed step-up converter was built and one could see that it was still fourth-order even with its five storage elements. Some theoretical derivations, theoretical analysis, Saber simulations, and circuit experiments are provided to validate the effectiveness of the proposed step-up converter. [ABSTRACT FROM AUTHOR]

Studying the firing dynamics and phase synchronization behavior of heterogeneous coupled networks helps us understand the mechanism of human brain activity. In this study, we propose a novel small heterogeneous coupled network in which the 2D Hopfield neural network (HNN) and the 2D Hindmarsh–Rose (HR) neuron are coupled through a locally active memristor. The simulation results show that the network exhibits complex dynamic behavior and is different from the usual phase synchronization. More specifically, the membrane potential of the 2D HR neuron exhibits five stable firing modes as the coupling parameter k 1 changes. In addition, it is found that in the local region of k 1 , the number of spikes in bursting firing increases with the increase in k 1 . More interestingly, the network gradually changes from synchronous to asynchronous during the increase in the coupling parameter k 1 but suddenly becomes synchronous around the coupling parameter k 1 = 1.96. As far as we know, this abnormal synchronization behavior is different from the existing findings. This research is inspired by the fact that the episodic synchronous abnormal firing of excitatory neurons in the hippocampus of the brain can lead to diseases such as epilepsy. This helps us further understand the mechanism of brain activity and build bionic systems. Finally, we design the simulation circuit of the network and implement it on an STM32 microcontroller. [ABSTRACT FROM AUTHOR]

This paper presents a chaotic circuit based on a nonvolatile locally active memristor model, with non-volatility and local activity verified by the power-off plot and the DC V–I plot, respectively. It is shown that the memristor-based circuit has no equilibrium with appropriate parameter values and can exhibit three hidden coexisting heterogeneous attractors including point attractors, periodic attractors, and chaotic attractors. As is well known, for a hidden attractor, its attraction basin does not intersect with any small neighborhood of any unstable equilibrium. However, it is found that some attractors of this circuit can be excited from an unstable equilibrium in the locally active region of the memristor, meaning that its basin of attraction intersects with neighborhoods of an unstable equilibrium of the locally active memristor. Furthermore, with another set of parameter values, the circuit possesses three equilibria and can generate self-excited chaotic attractors. Theoretical and simulated analyses both demonstrate that the local activity and an unstable equilibrium of the memristor are two reasons for generating hidden attractors by the circuit. This chaotic circuit is implemented in a digital signal processing circuit experiment to verify the theoretical analysis and numerical simulations. [ABSTRACT FROM AUTHOR]

Recently, the application of memristors in chaotic systems has been extensively studied. Unfortunately, there is limited literature on the introduction of discrete memristors into chaotic maps, especially into non-classical multidimensional maps. For this reason, this paper establishes a new three-dimensional parabolic chaotic map model; in order to improve the complexity and randomness of the map, it is coupled with a square-charge-controlled discrete memristor to design a new four-dimensional memristive hyperchaotic map. Firstly, the stability of the two maps is discussed. And their dynamical properties are compared using Lyapunov exponential spectra and bifurcation diagrams. Then, the phase diagram and iteration sequence of the 4D memristive hyperchaotic map are obtained. Meanwhile, we investigate the hyperchaotic states, the transient chaos, state transfer and attractor coexistence phenomena of the four-dimensional memristive map. In particular, the special state transfer phenomenon of switching from a periodic attractor to a quasi-periodic attractor and the special coexistence phenomenon of a quasi-periodic attractor coexisting with a quasi-periodic attractor around fixed points are found, which have not been observed in other systems. Finally, the phase-track diagrams and iterative sequence diagrams of the four-dimensional memristive map are verified on a digital experimental platform, revealing its potential for practical applications. [ABSTRACT FROM AUTHOR]

In this paper, a state observer-based adaptive fuzzy dynamic surface control is developed for uncertain discrete-time non-linear pure-feedback multiple-input-multiple-output (MIMO) systems with network-induced time-delay. The uncertainties are approximated by a set of adaptive fuzzy logic systems, with the adjusted parameters updated by a simplified recursive least squares estimation algorithm, combined with a state observer. For a constant known network-induced time-delay, the proposed modified dynamic surface control utilising the predicted system states, expands the acceptable network-induced time-delay and stable operating range for a discrete-time non-linear pure-feedback MIMO system in the network. The simulation results indicate that the presented method is effective. [ABSTRACT FROM AUTHOR]

In this paper, a novel dynamic surface control methodology with a predictive event-triggered strategy is proposed for discrete-time strict-feedback systems. In order to evaluate immeasurable state variables, a least square evaluation method is adopted. A dynamic surface controller is designed for discrete-time strict-feedback systems. When the transmission time of Sensor-to-Controller (S-C) or Controller-to-Actuator (C-A) channel increases, the stability and dynamic performance of the system deteriorates are improved by the proposed predictive event-triggered control strategy. Furthermore, the proposed methodology decreases the use of network resources while the whole system still keeps stable and exhibits an acceptable dynamic performance. The simulation results demonstrate that the proposed methodology is effective. [ABSTRACT FROM AUTHOR]

This paper presents a novel method to determine the optimal routing of medium voltage distribution networks in sparse rural areas. The objective is evaluated through minimizing the net present cost of the network over a selected time period. A problem specific genetic algorithm is proposed to address the optimal network routing problem and compute the optimized topology throughout a geographically constrained region. The proposed method simultaneously considers nonfixed candidate lines to overcome search space restrictions through a variable length encoding structure and the use of Steiner points, and a shortest path algorithm to traverse between point-to-point connections in the constrained region. Geographical restrictions on network routing are considered through the formation of a rasterized map. The network is modeled at the branch level and considers both greenfield and expansion planning to highlight the effects of accessibility restrictions. The optimization model is applied to a real rural distribution network in the South–West of Western Australia. Alternative network topologies are found to provide significant improvements over the existing network and traditional reconfiguration based methods for evaluating the minimum cost sparse rural distribution network. [ABSTRACT FROM AUTHOR]

The traditional switched-inductor technique for high-step-up conversion is widely utilized in industrial applications, especially multicell technique is combined to realize ultra-high voltage. However, the component currents are accordingly increasing with the increasing number of cells, which results in high cost, low efficiency, and low stability. In order to solve these problems, an improved multicell structure of switched inductor is devised, which can be utilized to replace traditional switched-inductor cells for higher actual boost ratio and lower diode currents. An example of the proposed structure-based boost converter is demonstrated for a detailed analysis, and it is followed with a comparison to verify its features. Finally, simulations and experiments are conducted to validate the effectiveness. [ABSTRACT FROM AUTHOR]

Current imbalance is a major problem in multistring LED lighting systems. Significant research efforts have been directed to solve the problem by either an active or passive approach. However, in view of some system requirements or other emerging lighting applications, independent control of different LED strings is getting more important to obtain different desirable brightness and/or color spectrums. The existing active and passive approaches have their limitations to perform current balancing and independent control simultaneously, thus posing another great challenge for the design of LED drivers. In this paper, a fully switched-capacitor-controlled $LCC$ resonant converter is designed to address this challenge. With the replacement of all fixed capacitors in the resonant tank of the $LCC$ converter with switch-controlled capacitors (SCCs), the regulation of the common ac bus voltage, current balancing, and individual current control of LED strings can be readily achieved with a single LED driver by varying the effective capacitances of the SCCs under constant switching frequency. In this way, the circuit complexity can be greatly reduced while the circuit performances can be significantly improved. An experimental prototype of driving LED growth light for indoor farming applications is designed and constructed to verify the idea. [ABSTRACT FROM PUBLISHER]

Cascading failure analysis in power systems draws a wide attention from researchers due to frequent occurrence of blackouts all over the world during past decades. A variety of mathematical models and analysis tools have been proposed in order to better understand the complicated mechanisms during the cascading failure. This paper provides a brief overview on cascading failure analysis and categorizes the most relevant literatures and analysis models. Different features related to cascading failures have been demonstrated and discussed. Comparisons between different models have been presented. Advantages and disadvantages of these models are summarized. The paper also highlights the possible future trends. [ABSTRACT FROM AUTHOR]

Aiming at the influence of wind power and load uncertainty on the transient stability of a power system under low carbon mode, this paper first proposes a collaborative preventive and emergency control model of transient stability by distribution preserving graph representation learning (DPG). Second, the uncertainty set of wind power output and load demand is studied, and the mathematical form of the two-stage robust transient stability collaborative control model is proposed. Then, the latest artificial intelligence technology is embedded into the global optimization algorithm of the model so as to further improve the solving efficiency of the algorithm. Finally, based on the developed improved two-stage robust optimization framework, an effective collaborative control method for transient stability is developed. The transient stability prediction and control system developed in this project is not only conducive to large-scale wind power grid connection but also expected to make academic contributions to development of power system transient stability and practical simulation verification. [ABSTRACT FROM AUTHOR]

Chaotic systems, especially hyper-chaotic systems are suitable for digital image encryption because of their complex properties such as pseudo randomness and extreme sensitivity. This paper proposes a new color image encryption algorithm based on a hyper-chaotic system constructed by a tri-valued memristor. The encryption process is based on the structure of permutation-diffusion, and the transmission of key information is realized through hyper-chaotic synchronization technology. In this design, the hash value of the plaintext image is used to generate the initial key the permutation sequence with the Hash table structure based on the hyper-chaotic sequence is used to implement pixel-level and bit-level permutation operations. Hilbert curves combining with the ciphertext feedback mechanism are applied to complete the diffusion operation. A series of experimental analyses have been applied to measure the novel algorithm, and the results show that the scheme has excellent encryption performance and can resist a variety of attacks. This method can be applied in secure image communication fields. [ABSTRACT FROM AUTHOR]

Memcapacitors (MCs) are promising candidates for the future design of low-power integrated neuromorphic computing systems, with particular emphasis on dynamical spiking neuron models that exhibit rich temporal behaviors. We present a novel floating flux-controlled MC that is designed using only three current feedback amplifiers, one analog multiplier, one capacitor and one resistor. Compared with existing floating MC emulators, our proposed design has a simpler structure without the need for DC biasing voltage sources, and can operate at higher working frequencies, and therefore enabling rapid prototyping of applied MC circuits for experimental verification of large-scale MC arrays. The consistency of the theoretical analysis, simulation and experimental results confirms the correctness and practicability of this new memcapacitor emulator. To further demonstrate a potential use of our MC, in this work, we apply the MC as the first parameterizable leaky integrator for spiking neuron through simulation and experiments. The intrinsic tunable capacitance of the MC can bring about novel short-term memory dynamics to neuronal circuits by dynamically modifying the membrane time constant on-the-fly, which ultimately resembles long-term potentiation, and can thus offer longer term memory. Our results highlight the potential for integrating heterogeneous spiking neural networks with richer temporal dynamics that rely on MC-based circuits to further the capability of neuromorphic computing. [ABSTRACT FROM AUTHOR]

Locally-active memristors blessed with an edge-of-chaos domain, which can be destabilized for generating action potentials, are natural candidates for emulating biological neurons. Pinpointing the edge-of-chaos domain, where neuromorphic behaviors may occur, is important for studying neuromorphic dynamics of memristors. This paper proposes a short-cut method for locating the edge-of-chaos domains in two kinds of generic memristors, and in several typical memristive 1-port circuits using only the Jacobian matrix in terms of their equations. Taking the Chua Corsage Memristor (CCM) and several CCM-based memristive 1-port circuits as examples, we verify the proposed new methods, and calculates their edge-of-chaos domains. Also, we carry out a complete classification of all the parameter regions of the CCM and several CCM-based memristive 1-port circuits, namely the locally-passive, locally-active but unstable, and edge-of-chaos domains, under both voltage and frequency control. Near the calculated edge-of-chaos domain, we uncover some new neuromorphic behaviors. To confirm the physical interpretations and predictions of the edge-of-chaos theorem, this paper presents an inexpensive electronic circuit realization of the complete set of equations defining the CCM using only off-the-shelf circuit components. When this poor-man's memristor is connected to various linear passive R, L, C circuits, and a battery, the resulting circuit can be tuned to generate action potentials, and a garden variety of neuromorphic phenomena, including chaos. But the highlight of this paper is reserved for the CCM itself where the world's first oscilloscope picture of a contiguous self-intersecting triple-branch DC V–I curve of the CCM is displayed in real time. [ABSTRACT FROM AUTHOR]

This paper presents a novel particle filter based dynamic state estimation scheme for power systems where the states of all the generators are estimated. The proposed estimation scheme is decentralized in that each estimation module is independent from others and only uses local measurements. The particle filter implementation makes the proposed scheme numerically simple to implement. What makes this method superior to the previous methods which are mainly based on the Kalman filtering technique is that the estimation can still remain smooth and accurate in the presence of noise with unknown changes in covariance values. Moreover, this scheme can be applied to dynamic systems and noise with both Gaussian and non-Gaussian distributions. [ABSTRACT FROM AUTHOR]

The rolling bearing is a crucial component of the rotating machine, and it is particularly vital to ensure its normal operation. In addition, the selection of different category features will add uncertainty and bias to the classification results. In order to decrease the interference of these factors to fault diagnosis, a new method that automatically learns the features of the data combined with Markov transition field (MTF) and convolutional neural network (CNN) is proposed in this paper, namely MTF-CNN. The MTF contributes to convert the original time series into corresponding figures, and the CNN is used to extract the deep feature information in the figure to complete the fault diagnosis. The effectiveness of the proposed method is verified by two public data sets. The experimental results show that MTF-CNN can classify different types of faults, and the highest accuracy rate can reach 100%. Likewise, the classification accuracy of this method is higher than some existing methods. [ABSTRACT FROM AUTHOR]

Neuromorphic computing plays an important role in brain-inspired computers, which can solve complex problems with higher efficiency and lower energy. As a promising candidate of neuromorphic devices, a locally active memristor has the ability to emulate complex neuromorphic functions, which has the potential to implement large-scale hardware spiking neural networks to unleash the efficiency and intelligence of the brain. In this paper, we analyze the characteristics of the locally active 4-lobe Chua corsage memristor (CCM) using small-signal equivalent circuits. In addition, we design a CCM-based third-order neuron circuit and illustrate its rich neuromorphic behaviors near the edge of chaos, such as self-sustained oscillation, periodic spiking, chaos, transient chaos, resting states, burst-number adaption, spike latency, and refractory period. Furthermore, collective behaviors such as exotic chimera states (e.g. coherent and incoherent patterns) have been observed in the ring neural network made of third-order memristive neurons with linear and nonlocal coupling. [ABSTRACT FROM AUTHOR]

This article considers the problem of exponential synchronization of inertial neural networks (INNs) with mixed delays via a novel hybrid control scheme consisting of pinning and periodic intermittent control. Both the discrete delay and distributed delay are taken into account in the network model. Through the proper variable substitution, the original system is transferred into a simply formed differential equation. Meantime, some new sufficient conditions are derived by the Lyapunov stability theory for the exponential synchronization of INNs. Eventually, numerical simulations are given to present the effectiveness of the theoretical method. [ABSTRACT FROM AUTHOR]

Summary: In this study, an adaptive model predictive control (MPC) strategy is proposed for a class of discrete‐time linear systems with parametric uncertainty. In the presented adaptive MPC, an updating law is firstly designed to update the estimated parameters online. By utilizing the estimated parameters, a standard MPC optimization problem for unconstrained systems is established. Then, to deal with constrained systems, the min–max MPC technique is developed under the set‐based approach for the estimated parameters. Furthermore, it is shown theoretically that the recursive feasibility and closed‐loop stability can be rigorously proved, respectively. Finally, numerical simulations and comparative analysis are presented to illustrate the superiority of the proposed algorithms in control performance. [ABSTRACT FROM AUTHOR]

Grid-connected converters may operate under grid fault conditions, such as the short-/open-circuit and the transmission line switching, which may be accompanied by a phase angle difference between the grid and converter. This variation will affect the normal operation of the converter since its phase-locked loop is not able to track the grid voltage phase angle immediately. In this article, an instability phenomenon with unregulated ac line current is identified in a modular multilevel converter (MMC) based high-voltage direct-current transmission system when there is a sudden phase angle change on the grid side. A set of analytical constraints is developed for the MMC in terms of the Lyapunov stability, the modulation index, and the maximum power transfer capability. Then, a multiconstraint operating region of the MMC under various phase disturbances is derived. Moreover, the parameter influence on the operating region is analyzed and two stability-enhancing methods for the MMC system are proposed. Finally, the boundaries of the operating region and the stability-enhancing methods are verified by simulations and laboratory measurements of three-phase grid-connected MMCs. [ABSTRACT FROM AUTHOR]

In this paper we describe a developed switched reluctance linear launcher system with diagrams and photographs, which consists of a three-phase 6/4 structure simple side-switched reluctance linear motor, a three-phase asymmetric bridge power converter, and a TMS320F28335 DSP digital controller. The acceleration closed-loop control of the mover is proposed and verified for the first time. The acceleration closed-loop control of the mover is implemented by a fuzzy control algorithm. The turn-on angle and the turn-off angle of the main switches in the power converter are fixed, and the triggering signals of the main switches are modulated by the pulse width modulation (PWM) signal. The deviation of the acceleration between the given acceleration and the feedback practical acceleration, and the variation from the deviation of the acceleration are the two input control parameters. The increment of the duty ratio of the PWM signal is the output control parameter. The duty ratio of the PWM signal is regulated based on the fuzzy control algorithm. Flow diagrams of the software, such as the main program, the encoder unit time interrupt subroutine, analog-to-digital converter interrupt subroutine, and the fuzzy control algorithm subroutine are given. The developed prototype is tested experimentally. It is shown that the velocity response of the mover is satisfactory. [ABSTRACT FROM PUBLISHER]

This paper presents a thermal model of power converter used in switched reluctance motor (SRM) drive under constant convective heat transfer. The distribution of the system temperature rise is derived based on the average heating power measured as SRM is running steadily, and the temperature rise is independent of ambient temperature which is proved in this paper. The accuracy of the thermal model is verified by comparing experimental data with the results derived from finite-element method (FEM) simulation. Furthermore, this study achieves optimization in heat sink geometry and the placement of power components with the present model. [ABSTRACT FROM PUBLISHER]

In this paper, an invariant set of the weight of the perceptron trained by the perceptron training algorithm is defined and characterized. The dynamic range of the steady-state values of the weight of the perceptron can be evaluated by finding the dynamic range of the weight of the perceptron inside the largest invariant set. In addition, the necessary and sufficient condition for the forward dynamics of the weight of the perceptron to be injective, as well as the condition for the invariant set of the weight of the perceptron to be attractive, is derived. [ABSTRACT FROM AUTHOR]

In this paper, a behavioral SPICE memristor model for digital logic implementation is presented and demonstrated in LTSpice. We show binarized state switching and voltage thresholding in the model, which are both important features in practical digital systems. The use of this SPICE model is straightforward and intuitive because almost all parameters in the model can be changed according to the application, including the threshold voltage and the memristance. The LTSpice circuit simulation shows the same characteristics as the original MATLAB numerical implementation, which means that the circuit-level SPICE model can be integrated with other designs. Three types of memristor digital logic circuits are simulated with the LTSpice model with positive results, which proves that the behavioral memristor model has potential application in digital system design. [ABSTRACT FROM AUTHOR]

This article proposes a novel filter, which is based on the event-triggered schedule and the cubature Kalman filter (CKF), to perform the nonlinear remote state estimation in the wireless sensor network with communication constraints. For the purpose of relieving the communication burden between sensors and the remote filter, the event-triggered schedule is utilized to determine whether the current observations should be sent. To deal with the non-Gaussian property due to the nonlinear transformation, the third-degree spherical-radial cubature rule is adopted to provide the accurate estimation. Moreover, the stochastic stability of the designed event-triggered CKF (ETCKF) is analyzed deriving the relationships among the estimation performance, the communication rate and the design parameter. Therefore, the proposed ETCKF can effectively balance the communication burden and the estimation accuracy. Finally, the numerical simulation on IEEE-39 bus system and unmanned aerial vehicles remote attitude monitoring system are performed to verify the practicability of ETCKF. [ABSTRACT FROM AUTHOR]

This paper studies a linear matrix inequality (LMI)‐based robust event‐triggered model predictive control (ET‐MPC) for a class of discrete linear time‐invariant systems subject to bounded disturbances. In the presented robust event‐triggered MPC, the event‐triggered mechanism is first set by the deviation between the optimal state and actual state. In addition, the Lyapunov‐based stability condition is employed in the constraints for simplifying parameters and enhancing the universality. Subsequently, a novel design framework that involves LMIs approach is developed. In such a framework, the Lyapunov weight matrix is predesigned by solving a convex optimization problem with LMIs and the infinite horizon MPC optimization problem is also transformed as LMIs such that the computational complexity is significantly reduced. To guarantee robust constraint satisfaction, a dual‐mode control strategy is adopted. In this way, the proposed robust ET‐MPC has the ability to deal with the constrained system. Furthermore, the theoretical analysis of the recursive feasibility and stability is provided. The numerical simulations and comparison studies demonstrate that the proposed robust ET‐MPC not only has satisfying control performance but also significantly reduces the computational burden. [ABSTRACT FROM AUTHOR]

To explore the complexity of the locally active memristor and its application circuits, a tristable locally active memristor is proposed and applied in periodic, chaotic, and hyperchaotic circuits. The quantitative numerical analysis illustrated the steady-state switching mechanism of the memristor using the power-off plot and dynamic route map. For any pulse amplitude that can achieve a successful switching, there must be a minimum pulse width that enables the state variable to move beyond the attractive region of the equilibrium point. As local activity is the origin of complexity, the locally active memristor can oscillate periodically around a locally active operating point when connected in series with a linear inductor. A chaotic oscillation evolves from periodic oscillation by adding a capacitor in the periodic oscillation circuit, and a hyperchaotic oscillation occurs by further putting an extra inductor into the chaotic circuit. Finally, the dynamic behaviors and complexity mechanism are analyzed by utilizing coexisting attractors, dynamic route map, bifurcation diagram, Lyapunov exponent spectrum, and the basin of attraction. [ABSTRACT FROM AUTHOR]

The interleaved dual boost converter (IDBC) is a promising topology to interface high power solar photovoltaic (PV) generation or energy storage systems to dc microgrids (MGs). It provides a high boost ratio for voltage transformations and helps significantly to reduce ripples in the currents drawn from dc sources. However, the conventional control methods of IDBC cannot guarantee system stability in the presence of tightly regulated and rapidly varying power electronic loads which behave as constant power loads (CPLs). Moreover, the uncertainties of converter systems may further affect the stability of MGs. In this context, a large signal stabilization scheme, which comprises finite-time observers (FTOs) and a finite-time controller (FTC), is proposed. By considering CPLs and parameter dispersions as system disturbances, FTOs are able precisely observe the disturbances in finite time. Then the FTC exactly offsets the estimated values and stabilizes all system states at their designated points in finite time. By doing so, the finite-time large signal stability can be obtained and the corresponding results are proved with Lyapunov theorems. A detailed control parameter selection guideline is provided for practical applications. Simulations show that the proposed method gives a wider stability margin than the conventional PI (proportional-integral) control. Furthermore, experiments verify its effectiveness and feasibility. [ABSTRACT FROM AUTHOR]

In this paper, a dual-solver framework based on model predictive control (MPC) is proposed, $E-$ solver and $L-$ solver. The economic scheduling problem is formulated using mixed-integer linear programming (MILP), which can be solved in an efficient way by using commercial solver ($E-$ solver). While the transmission loss problem is formulated using non-linear programming (NLP), which can be solved in the interior point method, namely $L-$ solver. The $E-$ solver provides an economic priority power scheduling plan for the $L-$ solver, and the $L-$ solver solves the entire microgrid accurate power flow scheduling plan. The proposed planning model decomposition technique aims to solve the planning model in a time-sharing manner and combines the characteristics of the two optimizers with a reasonable matching algorithm to achieve economic, efficient, and fast real-time control. A case study of a DC microgrid is employed to assess the performance of the online optimization-based control strategy. Simulations based on eight-node DC microgrid show that the method reduces the operating cost by $12.10\%$ and increases the calculation speed by $80.18\%$ compared with the traditional interior point method. With a shorter delay, the proposed optimization method will facilitate the implementation of real-time control and optimization. [ABSTRACT FROM AUTHOR]

Summary: In this paper, two nonlinear circuits are constructed based on the HP‐type flux‐/ charge‐controlled memory elements in parallel and series connections. Then, the phasor method is utilized to analyze and verify the frequency doubling mechanism between pinched hysteresis loops and the applied sinusoidal excitation. The expressions of equivalent admittance (denoted as YM) and impedance (denoted as ZM) for memory elements connected in parallel and series and the unified forms of which are also derived, respectively. Moreover, the mathematical models for the parallel‐/serial‐connected circuits are obtained and their characteristics are described. Meanwhile, the dual relationships, which come from the reciprocal relationship between YM and ZM, are also discovered based on their models. Furthermore, the gradual steady‐state oscillation and temporal behaviors are demonstrated for two nonlinear circuits. Finally, the experimental verification shows a good agreement between theoretical analysis and experimental results. [ABSTRACT FROM AUTHOR]

Nowadays, processing of data via neuromorphic non‐linear dynamics is a key element for brain‐inspired computers. In particular, an appropriate neuromorphic device is essential to emulate neuromorphic functions. In this brief, it is found that the Chua Corsage memristor has rich neuromorphic behaviours, which has potential values to realise the hardware construction for neuromorphic computing. The theory of local activity and edge of chaos are applied to analyse the neuromorphic dynamics of the second‐order Chua Corsage memristor based neuron model, and find that the periodic spikes (action potentials) appear either on or near the edge of chaos domain, which are excited by the subcritical Hopf bifurcation or the unstable state of the neuron model, respectively. Furthermore, the third‐order Chua Corsage memristor based neuron model can generate self‐sustained oscillations, periodic spikes, and chaos near the edge of chaos domain. [ABSTRACT FROM AUTHOR]