Switching Interval Research Articles

Robust online monitoring system for PCDD/Fs in a full-scale MSWI by Deans switch: Efficiently separation and purification

Existing online monitoring system for polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs), technically named Thermal Desorption-Gas Chromatography-Tunable Laser Ionization-Time of Flight Mass Spectrometry (TD-GC-TLI-TOFMS), has been applied in several incinerators in China. TD-GC-TLI-TOFMS can realize rapid detection of PCDD/Fs emissions from incineration sources. However, the long-term measurement of unclean flue gas will pollute the instruments in TD-GC-TLI-TOFMS, and interfere with the peak output of the target 1,2,4-trichlorobenzene (1,2,4-TrCBz). In this study, Deans switch (DS) was utilized for the first time in an online monitoring system for PCDD/Fs to separate 1,2,4-TrCBz signal from impurity signals, which improved the anti-interference capability of the system. Laboratory standard gas experiments showed that after adding a DS device between GC and TLI pulse valve, when the pressure set in DS was 4 psi and switched before or near the peak output of 1,2,4-TrCBz, the change of 1,2,4-TrCBz signal intensity was minimal. The impurities near the target peak were removed, and TLI-TOFMS was highly stable during continuous measurement. Moreover, the maximum intensity peak time of 1,2,4-TrCBz was stable after using DS in different switching time intervals. When connecting DS to TD-GC-TLI-TOFMS for field validation on the tail flue gas of a municipal solid waste incinerator (MSWI), results showed that a better 1,2,4-TrCBz signal could be obtained with a 69.52 % reduction of impurity peaks at the moments closer to the target peak. Furthermore, DS improved the sensitivity of the system to low concentration variations of 1,2,4-TrCBz in the flue gas. The robust system developed in this study can be better applied to incineration factories with poor combustion or suboptimal purification technology, facilitating online PCDD/Fs monitoring.

Radar shielding jamming method based on random phase modulation of digital coding metasurface

In this paper, a novel radar jamming method based on random phase modulation of digital coding metasurface was proposed. For typical radar system, the modulation model of coding metasurface and the echo model are deduced firstly. Then, the ordered statistics greatest of the constant false alarm rate (OSGO-CFAR) detection model was chosen to analyze the radar detection performance. Through serials of simulation, the influence of modulation modes, modulation switching interval, the number of coding elements and the signal-to-noise ratio (SNR) on radar detection performance is analyzed. Finally, the Monte Carlo simulation is performed. The result shows that as the modulation switching interval of metasurface decreases, the spectrum energy distribution of the target becomes more dispersed and the detection probability of the target decreases obviously. Traditional radar detectors will become ineffective, and the shielding of the target will be achieved. This implies a new radar jamming method which can realize target shielding instead of traditional stealth by reducing the radar cross section.

Mode-Mixed Effects Based Intralayer-Dependent Impulsive Synchronization for Multiple Mismatched Multilayer Neural Networks.

This article focuses on the intralayer-dependent impulsive synchronization of multiple mismatched multilayer neural networks (NNs) with mode-mixed effects. Initially, a novel multilayer NN model that removes the one-to-one interlayer coupling constraint and introduces nonidentical model parameters is first established to meet diverse modeling requirements in complex applications. To help the multilayer target NNs with mismatched connection coefficients and time delays achieve synchronization, the hybrid controller is designed using intralayer-dependent impulsive control and switched feedback control approaches. Furthermore, the mode-mixed effects caused by the intralayer coupling delays and switched intralayer topologies are incorporated into the novel model and analysis method to ensure that the subsystems operating within the current switching interval can effectively use the topology information of the previous switching intervals. Then, a novel analysis framework including super-Laplacian matrix, augmented matrix, and mode-mixed methods is developed to derive the synchronization results. Finally, the main results are verified via the numerical simulation with secure communication.

Cascaded Interleaved DC–DC Converter for a Bidirectional Electric Vehicle Charging Station

The dc-dc bidirectional step-up interleaved converter coupled by a central capacitor in a cascaded topology is used as a Bidirectional Electric Vehicle Charging Station (BEVCS) for charging and discharging the battery of an Electric Vehicle (EV). In this paper, a unified (coupled) model is proposed combining all switching intervals in a single model, allowing an accurate analysis and a better design for the controllers. In addition, this solution enables not only stage 1 to regulate the dc-link voltage on the terminals of stage 2, but also to use a classical Proportional-Integral ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$PI$</tex-math></inline-formula> ) controller or an adaptive <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{SoC}$</tex-math></inline-formula> -sharing function as a control structure. Finally, stability analysis, simulations, and experimental results are accomplished to evaluate the effectiveness of the proposed approach.

Converse Lyapunov results for switched systems with lower and upper bounds on switching intervals

The topic of this manuscript is the stability analysis of continuous-time switched nonlinear systems with constraints on the admissible switching signals. Our particular focus lies in considering signals characterized by upper and lower bounds on the length of the switching intervals. We adapt and extend the existing theory of multiple Lyapunov functions, providing converse results and thus a complete characterization of uniform stability for this class of systems. We specify our results in the context of switched linear systems, providing the equivalence of exponential stability and the existence of multiple Lyapunov norms. By restricting the class of candidate Lyapunov functions to the set of quadratic functions, we are able to provide semidefinite-optimization-based numerical schemes to check the proposed conditions. We provide numerical examples to illustrate our approach and highlight its advantages over existing methods.

Global Event-Triggered Funnel Control of Switched Nonlinear Systems via Switching Multiple Lyapunov Functions.

In this article, the global event-triggered (ET) funnel tracking control problem is studied for a class of switched nonlinear systems with structural uncertainties, where the solvability of the control problem for each subsystem is not needed. A switching multiple Lyapunov functions (MLFs) method is established, where MLFs are designed to handle switched inverse dynamics, and a switching barrier Lyapunov function is constructed to address switched sampled errors that may compromise system stability. This is achieved alongside a new switching dynamic event-triggering mechanism (DETM). By combining this method with backstepping, a dwell-time state-dependent switching law and an ET funnel controller of each subsystem are constructed, effectively eliminating the issue of the "explosion of complexity" encountered in traditional backstepping without using dynamic surface control or command filters. Additionally, the designed switching DETM ensures that the tracking error always evolves within a performance funnel in any consecutive triggering interval, excluding Zeno behavior, and guaranteeing positive constant lower bounds for two consecutive triggering intervals and any switching interval, respectively. Finally, an example is provided to show the validity of the theoretical results.

Droplet manipulation on an adjustable closed-open digital microfluidic system utilizing asymmetric EWOD.

The closed-open digital microfluidic (DMF) system offers a versatile and powerful platform for various applications by combining the advantages of both closed and open structures. The current closed-open DMF system faces challenges in scaling up due to electrode structural differences between closed and open regions. Here we developed an adjustable closed-open DMF platform by utilizing the modified slippery liquid-infused porous surfaces (SLIPS) with asymmetric electrowetting on dielectric (AEWOD) as a hydrophobic dielectric layer. The consistent electrode structures of the bottom printed circuit board (PCB) electrode array on both the closed and open regions, and the utilization of a transparent acrylic with floating potential as the top plate allow a low-cost and easily scalable closed-open DMF system to be achieved. The impacts of applied voltage, parallel plate spacing, electrode switching interval, and electrode driving strategies on various droplet manipulations were investigated. The results show that the optimal plate spacings range from 340-510 μm within the closed region. Meanwhile, we also studied the influence of the thickness, geometry, and position of the top plate on the droplet movement at the closed-open boundary. Through force analysis and experimentation, it is found that a thin top plate and a bevel of ∼4° can effectively facilitate the movement of droplets at the boundary. Finally, we successfully achieved protein staining experiments on this platform and developed a customized smartphone application for the accurate detection of protein concentration. This innovative closed-open DMF system provides new possibilities for future applications in real-time biological sample processing and detection.

Algorithm Optimization for PWM Signal Generation with Selective Harmonic Elimination Using the Walsh Transform.

The use of the Walsh transform in DC-AC PWM waveform generation allows the calculation of the switching angles by means of linear equations dependent on the fundamental amplitude. After the description of the mathematical method used to find the solutions, it is presented a method to reduce the computation time needed to find all the switching intervals that give a useful range variation of the fundamental amplitude.

Command filter‐based adaptive event‐triggered control for switched nonlinear systems with full state constraints

AbstractAn adaptive event‐triggered control issue based on command filter and dynamic surface control is studied for switched nonlinear systems with full state time‐varying constraints in this article. To handle state constraints, the nonlinear mapping conversion technique is used. Radial basis function neural networks (RBFNNs) are utilized to approximate unknown nonlinear continuous functions. Adaptive control algorithm is designed based on command filtered backstepping technology. Novel even triggered control is constructed for two different cases in the switching interval. Furthermore, all signals in the switched system are proved to be semi‐globally uniformly ultimate bounded (SGUUB) by the aid of dynamic surface control method under arbitrary switching. Meanwhile, all states do not violate the preset constraints and Zeno phenomenon is avoided. The validity of the presented approach is confirmed through a numerical simulation result.

62 Neural Correlates of Cognitive Function in the Basal Ganglia

Objective:Timing, or the decision of when to act, is essential to mammalian behaviors from escaping predators to driving a car. It requires cognitive functions such as working memory for time-based rules and attention to the passing of time. Thus, it can be used as a proxy for higher order executive functions that are difficult to measure but are impaired in many neurological disorders. Therefore, insights from studies of interval timing, tasks which require estimating time intervals of several seconds, have great value for our understanding of human disease. Crucial to timing is the basal ganglia, which integrates cortical activity with midbrain dopamine signals and sends out signals to the spinal cord that regulate movement, motivation, and other behaviors. We have previously found that within the basal ganglia, medium spiny neurons of the striatum exhibit ramping activity in time-related tasks. In other words, they gradually increase or decrease firing frequency across a timed interval, and this is thought to encode time. Yet it is still unknown how the encoding of time is translated into time-based motor responses. To answer this question, we turned to the external globus pallidus (GPe) because it is a regulatory hub within the basal ganglia and is thus well positioned to regulate timing behavior. We sought to examine how the GPe functions in response to time-based demands.Participants and Methods:We recorded from neuronal ensembles using 16 channel electrode arrays implanted in the GPe of five mice while they performed an interval timing task called the switch interval timing task. Spike sorting was then used to identify signal from individual neurons.Results:Data were compiled from 43 neurons over several trials. Principal component analysis of neural firing activity was then conducted and revealed a downward ramping pattern in GPe neurons during interval timing trials. Data were then separated based on trials in which mice made correct decisions and those in which mice made a mistake. We found that when mice make correct timing decisions, there is downward ramping activity in the GPe, yet when mice make timing mistakes, this ramping pattern is lost.Conclusions:Our findings suggest that the GPe processes timing signals through ramping activity, before projecting to the output nuclei of the basal ganglia. This is a novel finding and contributes to a growing understanding of the temporal code of the basal ganglia. The full extent of this code is still unknown, but this insight contributes to a better understanding of how the globus pallidus represents cognition. If we can better explain the neural correlates of timing, we can use this knowledge to inform therapeutic interventions for basal ganglia dysfunction, which could have profound implications for diseases like Parkinson’s disease, which affects millions around the world.

Formation tracking of multi-robot systems with switching directed topologies based on Udwadia-Kalaba approach

In this paper, a distributed formation tracking protocol is proposed for multi-robot systems with switching directed topologies based on the Udwadia-Kalaba approach. The basic idea is to use the consensus-based scheme to reconstruct formation tracking control requirement into a second-order constraint first, and then apply the Udwadia-Kalaba approach to obtain the constraint force required to achieve the formation tracking, and afterward derive the explicit equations of motion for the constrained multi-robot systems for the control algorithm design. The convergence analysis of the tracking error system is conducted to affirm that the proposed control algorithm can achieve the formation tracking when the switching directed topologies have a directed spanning tree across each switching time interval. Numerical simulations are performed to validate the effectiveness of the proposed algorithm.

An Improved Advanced Driver-Assistance System: Model-Free Prescribed Performance Adaptive Cruise Control

To maintain a safe distance between the autonomous vehicle and the leader, ensure that the vehicle runs at its expected speed as far as possible, and achieve various control requirements such as speed, distance and collision avoidance, a model-free prescribed performance adaptive cruise control (ACC) algorithm based on funnel control is proposed. The contributions of this paper are that the designed ACC algorithm only requires the speed and position information and can constrain their tracking errors within a predetermined range. When the follower is far away from the leader, the speed-prescribed performance controller adjusts the follower vehicle’s speed to the reference velocity. When the follower vehicle approaches the leader vehicle, a distance-prescribed performance controller is designed to adjust the distance between the follower and the leader. On this basis, the prescribed performance function can expand the switching interval, thereby improving the robustness of the speed and distance control switching process. The effectiveness of the designed algorithm is demonstrated in three scenarios, such as approaching and following, emergency braking, and frequent starting and stopping. The results show that during the speed control stage, the designed algorithm allows the vehicle’s operating speed to vary within a predetermined spatial range; in the distance control stage, the designed algorithm strictly limits the distance error within the preset range. The speed and distance of the vehicle change smoothly, and there is no overshoot during the initial state adjustment, emergency braking, and frequent start and stop stages, demonstrating a good control effect.

A novel approach for synchronized switch damping control based on voltage sources in parallel

This paper presents a novel hybrid model, termed Synchronized Switch Damping with Voltage Sources in Parallel (SSDVP), for enhancing the performance of synchronized switch damping (SSD) control by addressing the flipping voltage and voltage drop issues in piezoelectric elements. The effectiveness of SSD control is closely related to the voltage applied to the piezoelectric element. However, at high voltages, such as the maximum capacity of 1.5 kV for piezoelectric materials, charge leakage becomes significant and cannot be ignored. This results in substantial voltage drop during switching intervals, particularly in low-frequency applications. The proposed SSDVP method combines the advantages of SSDI and Bang-bang control to generate a stable high voltage across the piezoelectric element with minimal power consumption. The working principle and mathematical model of the SSDVP method are described, including the expression of the voltage across the piezo patch and the energy consumption of the control circuit. Simulation and experimental comparisons are conducted among the traditional SSDV model, Bang-bang control, and the proposed SSDVP model to demonstrate the advantages of the novel approach.

Thermo-sensitive electrical parameters of high-power IGBTs based on gate-emitter voltage measurement during switching delay intervals

High-power insulated gate bipolar transistors (IGBTs) are widely used for wind power conversion and electric traction. Accurate estimation of IGBT junction temperature and active thermal control improve the reliability of power converters in such applications. Among the various available methods for junction temperature estimation, thermo-sensitive electrical parameter (TSEP) based estimation is simple, fast and amenable to real-time implementation. Evaluation of many TSEPs (e.g. forward voltage, threshold voltage, peak dv/dt, peak di/dt etc.) require measurement of multiple electrical quantities in the power circuit and/or in the gate-drive circuit. The measurement systems need to have high precision and/or high bandwidth as well. Moreover, the quantities in the power circuit require isolated measurement systems. This paper explores possible new TSEPs, that could be obtained through a single measurement of low-voltage quantity, namely, the gate-emitter voltage. Since the gate-emitter voltage varies primarily during turn-on and turn-off delay intervals, these switching intervals and their parameters are studied over a wide range of operating conditions. Since many practical converters are expected to operate at an ambient temperature as low as −35 °C, the experimental study is carried out over a wide temperature range from −35 °C to 125 °C. Further, to ensure wider applicability of the findings of the study, four IGBTs of different makes are considered for the experimental investigations. The experimental results bring out four different parameters pertaining to turn-on delay and another four parameters pertaining to turn-off delay, which are potential TSEPs. Of these eight TSEPs, five of them can be evaluated based on measurement of gate-emitter voltage only, and four of these have not been reported in the literature previously. In addition, the experimental data are useful for development of temperature-sensitive device models for simulation, performance assessment and design of gate-drive circuit for different temperatures, and for temperature-sensitive compensation of inverter dead-time effect.

Mean-square convergent continuous state estimation of randomly switched linear systems with unobservable subsystems and stochastic output noises

This paper studies mean-square (MS) convergent observers for estimating continuous states of randomly switched linear systems (RSLSs) with unobservable subsystems that are subject to stochastic output observation noises. When subsystems are unobservable and switching sequences are random, the classical Kalman–Bucy filters that are applied to observable sub-states are shown to be potentially divergent. It is also shown that unless the switching interval T can be selected to be sufficiently small from the outset, MS convergence may never be achieved, regardless of how the observers for the subsystems are designed. The critical threshold Tmax on T is derived for MS convergent observers to be achievable. Under the condition T<Tmax, this paper introduces design algorithms for subsystem observers to generate a globally MS convergent observer for the entire continuous state. A fundamental design tradeoff between convergence speeds and steady-state estimation errors is analyzed. This paper extends our recent new framework and algorithms for strong convergent observer design in RSLSs by including observation noises, considering multi-output systems, establishing new algorithms for MS convergence, and developing design tradeoff analysis. Examples and a practical case study are presented to illustrate the design procedures, main convergence properties, and error analysis.

Real-World Immunosuppressant Treatment Patterns for Patients with Lupus Nephritis in the United States.

Lupus nephritis (LN) treatment aims to control and prevent flares and irreversible kidney damage. Around 30% of patients are unresponsive to treatment; however, real-world LN treatment patterns have not been reported. Objectives of this retrospective cohort study (GSK 209758) were to quantify the time to switching/re-initiating induction therapy in patients with LN initiating immunosuppressant therapy and conversion from induction to maintenance immunosuppressant therapy, and to assess corticosteroid use. Patients with LN initiating induction or maintenance immunosuppressant therapy were identified using claims data. Patients were followed up from the index date (immunosuppressant initiation date) until treatment discontinuation, death, disenrollment, administrative censoring, or the end of follow-up period. The cumulative incidence of switching/re-initiating induction therapy and conversion to maintenance therapy was estimated using outpatient pharmacy claims and procedure codes. Corticosteroid use was estimated using pharmacy claims; a mean daily dose of ≥ 7.5mg/day was considered high. In total, 5000 patients with LN contributed 5516 treatment episodes (induction cohort, N = 372; maintenance cohort, N = 5144). In the induction cohort, the cumulative incidence (95% confidence interval) of switching between induction therapies was 24.6% (20.1-30.0) at 12months, while 59.6% (52.4-66.1) of patients converted to maintenance therapy at 12months. In the maintenance cohort, 21.2% (19.9-22.5) re-initiated induction therapy at 12months. Oral corticosteroid use decreased during the follow-up in both cohorts, but 21.5% of patients remained on a high dose at 12months in the induction cohort, while 15.8% in the maintenance cohort were taking a high dose at 24months. Around a quarter of patients with LN initiating immunosuppressant therapy switched within 12months, while a fifth re-initiated induction therapy within 12months. Use of high corticosteroid doses were observed during 24months of follow-up. These data suggest that many patients do not respond to existing standard LN therapies.

Performance assessment of active insulation systems in residential buildings for energy savings and peak demand reduction

Active insulation systems (AISs) in buildings are envelopes that integrate thermal insulation, thermal energy storage, and controls. Although different designs for AISs have been proposed in the literature, a comprehensive analysis of feasible AISs is lacking. This paper discusses the energy performance, peak demand reduction potential, and performance characteristics of an AIS that uses a concrete wall as thermal mass sandwiched between two solid-state thermal switches (STSs). These STSs change their thermal conductivity using an on/off metal switch to create or break a thermal bridge across the STS. This paper first describes the experimental setup, used to determine the ratio of thermal resistance during R-high (low thermal conductivity) and R-low (high thermal conductivity) states of the STSs. This ratio was then used in whole-building energy simulations to evaluate the performance of AIS walls across different climate zones with/without a freeze timer of 60 min. The timer was added to reduce the number of switches of STSs from one state to another, and hence the energy needed for these switches. Analysis of the switching frequency and interval of STSs, thermal conductivity of walls, impact of wall orientation, and heat transfer through the wall from the use of AIS at different climate zones/locations were performed. The simulation results show that the AIS can achieve energy savings ranging from ∼ 980 to 2,290 kWh in a single-family home with a floor area of ∼ 220 m2 compared with an IECC 2018 baseline. The energy savings was higher in dry climate zones which represent 17% of residential buildings in the United States, compared to humid or marine climate.

Pricing Variance Swaps under MRG Model with Regime-Switching: Discrete Observations Case

In this paper, we creatively price the discretely sampled variance swaps under the mean-reverting Gaussian model (MRG model in short) with regime-switching asymmetric double exponential jump diffusion. We extend the traditional MRG model by further considering the trend of the financial market as well as a sudden and unexpected event of the market. This new model is meaningful because it uses observable Markov chains that represent market states to adjust its parameters, which helps capture the movement of the market and fluctuations in asset prices. By utilizing the characteristic function and the conditional transition characteristic function, we obtain analytical solutions for pricing formulae. Note that this is our first effort to provide the analytical solution for the ordinary differential equations satisfied by the Feynman–Kac theorem. To achieve this, we have developed a new methodology in Proposition 2 that involves dividing the sampling interval into more detailed switching and non-switching intervals. One significant advantage of our closed-form solution is its high computational accuracy and efficiency. Subsequent semi-Monte Carlo simulations will provide specific validation results.

Finite-time peak-to-peak analysis for switched generalized neural networks comprised of finite-time unstable subnetworks

This research is concerned with finite-time stability and peak-to-peak performance analysis for the discrete-time switched generalized neural networks (SGNNs) with time-varying delay. Compared with the reported results, each individual subnetwork of the SGNNs is considered to be finite-time unstable in the present study. To accomplish the anticipatory objective, the quasi-time-dependent Lyapunov–Krasovskii functional is constructed, and the associated sufficient conditions are simultaneously formulated to confirm that the disturbance-free SGNNs are finite-time stable when the subnetwork satisfies a certain switching time interval. In addition, a prescribed disturbance attenuation level is also achieved for the perturbed SGNNs in the sense of peak-to-peak performance. Finally, the provided simulation example corroborates the effectiveness and applicability of the established finite-time analysis framework in the absence of finite-time stable subnetworks.

Asynchronous finite-time [formula omitted] control of discrete-time switched systems: An inverse weighted switching scheme

The work proposes an asynchronous convex Lyapunov function and an inverse weighted switching scheme to study the asynchronous finite-time H∞ control issue of discrete-time switched systems. Based on the manipulation that the switching interval is partitioned into three sections, the designed Lyapunov function is constructed in convex structure, reducing the conservatism caused by the low degree of freedom. Owing to the finite-time parameter, the inverse weighted form of the dwell time can be used to characterize the exponent term. The inverse weighted switching is raised to erase the dwell time restriction of each subsystem, only requiring that the inverse weighted sum of all dwell times is less than a constant. Based on the asynchronous convex Lyapunov function and inverse weighted switching, the l2-gain analysis is conducted to derive the asynchronous finite-time H∞ controller. Finally, two numerical examples and an application example are given to verify the potential and validity of the proposed results.