Signal Transmission Delay Research Articles

Distributed Synchronization Control With Signal Transmission Delay and Its Applications to Genetic Regulatory Networks

This paper investigates the adaptive distributed synchronization control problem of multi-agent systems, where the information transmission in the network is subject to unknown constant signal transmission delay. Firstly, an adaptive distributed control scheme is designed for a class of certain multi-agent systems to guarantee the synchronization among all agents is achieved. In the scheme, the unknown constant delay can be estimated online. Furthermore, compared with some existing works where some global information are required to design the controllers for the agents, the scheme proposed in our paper does not need the global information. By algebraic graph theory with Lyapunov stability theory, the whole closed-loop system stability analysis is provided. Then, the method is extended to a class of uncertain nonlinear MASs. Finally, genetic regulatory networks (GRNs) are used to show the effectiveness of the proposed design scheme.

Preliminary study of the treatment of brain diseases by remote manipulation of deep brain stimulation with 5G communication

Objective To verify the feasibility of remote manipulation of deep brain stimulation (DBS) with 5G communication for the treatment of brain diseases. Methods In March 2019, 3 patients(2 parkinson′s disease and 1 essential tremor) who planned to undergo DBS at Department of Neurosurgery, the First Medical Center of the PLA General Hospital were prospectively recruited into this study. The 5G communication links were established between Beijing and Sanya, Hainan (Hainan Hospital of PLA Gerenal Hospital). The operation group completed the steps such as installation of head frame, craniotomy, microelectrode puncture for the patients in the operation room in Beijing. The remote-control group completed the steps such as remote surgery planning, remote control of microelectrode recording (MER), and imaging results confirmation of electrode implantation. Results During remote surgical procedures under 5G communication, the maximum downlink peak rate was 119 Mbps, the uplink peak rate was 27 Mbs, and the average delay was 76 ms. During the operation, the remote control of the MER drive system ran smoothly and there was no delay of signal transmission. The voice and picture of the operating room and the MER signal were transmitted smoothly. The operation was completed successfully. There were no complications such as puncture bleeding, infection and skin ulceration after the operation. At 3-month follow-up, based on clinical rating scale for tremor (CRST) the score of essential tremor patient was improved by 43.6%; the UPDRS (unified Parkinson disease rating scale)-Ⅲ scores of 2 Parkinson′s disease patients were improved by 84.9% and 90.5% respectively. Conclusion The remote control of MER in DBS surgery with 5G communication seems to have certain feasibility. Key words: Parkinson disease; Essential tremor; Deep brain stimulation; 5G communication; Remote control surgery

Nonfragile Retarded Sampled-Data Switched Control of T–S Fuzzy Systems and Its Applications

This article investigates the nonfragile retarded fuzzy sampled-data control design of Takagi–Sugeno (T–S) fuzzy system. When compared to the traditional sampled-data control scheme, a constant signal transmission delay is involved with uncertainties in control gain parameters, which exploited for the first time to tackle the stabilization analysis of the closed-loop T–S fuzzy systems based on the switched control idea. By constructing a fuzzy membership function (FMF)-dependent Lyapunov–Krasovskii functional (LKF) and utilizing the time-derivative information of the FMF, the sufficient conditions are carried out in terms of linear matrix inequalities (LMIs) such that the resultant T–S fuzzy systems can achieve the globally asymptotically stable under the designed control. Then, the desired control gain is determined by solving the obtained LMIs. Finally, the numerical simulations are demonstrated to show the advantage and applicability of the derived sufficient conditions.

Non-fragile memory filtering of T-S fuzzy delayed neural networks based on switched fuzzy sampled-data control

This paper deals with the non-fragile memory filtering issue of T-S fuzzy delayed neural networks with randomly occurring time-varying parameters uncertainties and variable sampling rates. Compared with existing sampled-data control schemes, an improved switched fuzzy memory sampled-data control protocol is designed for the first time, which involves not only a signal transmission delay but also switched topologies. By developing some new terms and taking full advantage of the variable characteristics related to the actual sampling pattern, a modified loose-looped fuzzy membership functions (FMFs) dependent Lyapunov-Krasovskii functional (LKF) is constructed based on the information of the time derivative of FMFs. Meanwhile, some relaxed matrices chosen in LKF are not consequentially positive definite. Moreover, with the LKF methodology and employing the developed estimation technique, several optimized control algorithms with both a larger sampling period and upper bound of time-varying delays for achieving the stabilization of the resultant T-S fuzzy delayed neural networks are derived. Finally, a numerical example is presented to demonstrate the superiority and applicability of the theoretical results.

Stability Analysis of Nonlinear Networked Control System with Integral Quadratic Constraints Performance in Takagi-Sugeno Fuzzy Model

This paper focuses on the stability analysis of nonlinear networked control system with integral quadratic constraints (IQC) performance, dynamic quantization, variable sampling intervals, and communication delays. By using input-delay and parallel distributed compensation (PDC) techniques, we establish the Takagi-Sugeno (T-S) fuzzy model for the system, in which the sampling period of the sampler and signal transmission delay are transformed to the refreshing interval of a zero-order holder (ZOH). By the appropriate Lyapunov-Krasovskii-based methods, a delay-dependent criterion is derived to ensure the asymptotic stability for the system with IQC performance via the H∞ state feedback control. The efficiency of the method is illustrated on a simulation exampler.

Delay compensation position tracking control of electro-hydraulic servo systems based on a delay observer

In this article, the position control problem of electro-hydraulic servo systems with feedback signal transmission delay is studied. In order to improve the control accuracy of the system, a hybrid controller which combines a delay observer, a nonlinear disturbance observer and a backstepping controller is proposed. The controller has the characteristics of compensating the delay of signal transmission, restraining the uncertain disturbance of control systems and high control precision. In order to verify the stability and validity of the proposed hybrid controller, a single-degree-of-freedom electro-hydraulic shaking table is used to verify the experimental results. The experimental results show that the proposed controller has better control effects than proportional integral derivative and backstepping controller.

Comparative Study of DDS with Different Types of Phase Accumulators

The paper deals with the problems of delayed transfer signals in the direct digital synthesizer (DDS) phase accumulator adders. Transfer delay is one of the factors that affect the maximum output frequency of the DDS synthesizer. The main types of adders used in DDS synthesizers are described. Separately, attention was paid to the adder with a consistent transfer of the transfer signal, adders with a transmission carry signal with a fixed block length, adders with a signal transmission delay with a variable block length, and a mathematical analysis of the origin and duration of the delay of the transfer signal in them. It was found that the use of a transfer adder with a variable length of a block in the core of a direct digital synthesizer would increase the maximum output frequency by 2.4 times compared to the adder with a parallel transfer, and by 1.43 times as compared with the adder with a fixed length the block.

Deep fusion technology of optical communication channel based on multicarrier modulation

To solve the problems of long transmission delay and high energy consumption caused by the number of optical communication signals and sub-nodes in the network, a deep fusion method of optical communication channel based on multi-carrier modulation is proposed in this paper. Based on noise modeling of wireless optical communication network and attenuation modeling of optical communication signal, the symbol of optical communication signal to be transmitted is divided into several blocks. Several subcarriers are formed after series–parallel conversion. Symbols are modulated onto these orthogonal subcarriers. The modulation signal of optical communication in OFDM network is formed by superposition, and the multi-carrier modulation of optical communication signal is completed. The stable transmission of optical communication signal after multi-carrier modulation is realized using DCHP protocol. By introducing the data fusion technology of neural network, a deep fusion model of optical communication channel based on BP neural network is established. To realize the deep integration of optical communication channels, a hierarchical routing protocol is proposed. The experimental results show that this method can reduce the transmission delay and energy consumption of optical communication signals. It can solve the problems in optical communication and has high application value in real life.

Delay-Dependent Impulsive Distributed Synchronization of Stochastic Complex Dynamical Networks With Time-Varying Delays

This paper studies the problem of synchronization for a class of stochastic complex dynamical networks. It designs for the first time a distributed impulsive protocol based on pinning control that involves a constant signal transmission delay to tackle synchronization issues of such networks. Novel criteria on network synchronization are established by employing a time-dependent Lyapunov functional and a mathematical induction approach, where information on the state variables themselves and their neighbors is sufficiently utilized. Moreover, it is shown that the frequency of impulsive occurrence, impulsive input delays, stochastic perturbations, and the feedback control strength can significantly affect the synchronization performance. Numerical simulations are given to illustrate the effectiveness of the derived theoretical results.

Performance and Power Optimization for Intercalation Doped Multilayer Graphene Nanoribbon Interconnects

In this work, thickness of AsF5 intercalation doped, Multilayer Graphene Nanoribbon (MLGNR) interconnects is optimized by minimizing various performance and power metrics (i.e. crosstalk-induced signal transmission delay, noise parameters, Noise-delay product (NDP), Energy-delay product (EDP), Power-delay product (PDP) and Noise bandwidth ratio (NBWR)). For perfectly and nearly specular MLGNRs (i.e. specularity index, P = 1 and P = 0.8 respectively), the optimal thicknesses for minimizing the crosstalk-induced delay are 25 nm and 100 nm for intermediate and global level MLGNR interconnects, respectively. However, it is observed that perfectly and nearly specular MLGNRs are immune to noise for thickness less than 40 nm at intermediate level and 200 nm at global level interconnects. They outperform Cu in terms of signal transmission delay, noise width and noise area at both levels of interconnect. But in terms of peak crosstalk noise, Cu is better than all types of MLGNR interconnects. When we talk about NDP, EDP and NBWR, MLGNR thickness optimizes at 25 nm for intermediate level and 100 nm for global level. As far as power dissipation is concerned, perfectly and nearly specular, intermediate and global level MLGNRs are many times better than copper in the whole range of its thickness which can be optimized by minimizing its PDP at 100 nm irrespective of the level of interconnects. It can be inferred from the simulated results that global level MLGNR interconnects are amenable to scaling, whereas intermediate level interconnects have adverse effects of scaling.

Feedforward Motion Control With a Variable Stiffness Actuator Inspired by Muscle Cross-Bridge Kinematics

High mechanical impedance, sensor resolution, and computing bandwidth are desirable for achieving stability in feedback control. In contrast, although the human body is physically inferior to man-made systems in terms of feedback control due to signal transmission delay, humans can achieve not only stable but also robust and adaptive control ability. For these reasons, the significance of the feedforward control of the human has been emphasized in neuroscience. In previous studies, virtual trajectory control and internal model hypotheses were used to explain the principle of human feedforward control in view of the mechanical stiffness of joints and the internal model of the brain, respectively. Inspired by these insights, in this paper, we focus on the relationship between the joint stiffness and inverse model accuracy and attempted to apply it to the field of robotics. We present a variable stiffness actuator developed using variable radius gear transmission inspired by muscle cross-bridge kinematics. The developed mechanism allows the joint stiffness to be accurately controlled without any sensor-based feedback control. Using the developed actuator, we conduct a feedforward motion generating experiment with respect to variations in the inverse dynamics model uncertainty and verified that joint stiffness can compensate for inverse model uncertainty and external disturbances. These results indicate that the developed variable stiffness actuator can be applied to the robotics field for feedforward applications and support the hypothesis that humans may utilize joint stiffness to compensate for the inverse dynamics model uncertainty.

Influence of Time Delay in Signal Transmission on Synchronization between Two Coupled FitzHugh-Nagumo Neurons

In this paper, the energy method is employed to analytically investigate the influence of time delay in signal transmission on synchronization between two coupled FitzHugh-Nagumo (FHN) neurons. Unlike pre-existing methods that deal with synchronization problems, our major idea is to consider the change rate of the energy of the synchronization error system, since the original system’s synchronization is equivalent to the disappearance of the energy of the error system. In rewriting the original coupled system in the corresponding energy coordinates based on the energy method, we find that the change rate of energy of the error system can be divided into two parts (periodic and non-periodic). The synchronization criterion for the original system can then be obtained by letting the non-periodic part of the change rate of the energy be less than zero. The correctness of the analysis is illustrated with numerical simulations. Our analytical results show that time delay in signal transmission has very significant effects on the synchronization between two FHN neurons. If the time delay in signal transmission is not taken into account in the two coupled FHN neurons, synchronous spikes cannot be achieved in the system for any given coupling strength. By adjusting the value of the time delay in signal transmission, the neural system can freely switch between neural rest and synchronous spikes. This means that time delay in signal transmission is crucial for the occurrence of synchronous spikes in the FHN neural system, which contributes to our understanding of the interaction between neurons. We analytically show the influence of the time delay on the synchronization between two FHN neurons, which was seldom considered by other researchers.

Gel casting of mullite for radome applications

AbstractGel‐casting method was used to fabricate mullite ceramics because this method has not been applied to fabricate dense mullite in literature. Gel‐casting parameters such as monomer types (acrylamide and metyhlenebisacrylamide), effect of initiator (ammonium persulfate), and catalyst (tetramethylenediamine) were studied. All samples reached to relative densities of 97% to 98.2% after sintering at 1540°C for 2 hours. Backscattered SEM images revealed anisotropically grown mullite grains, glassy phase located at triple junctions, hexagonally‐shaped and faceted Al2O3 grains, and white‐color grains consisting of ZrO2 and undissolved TiO2. A Weibull modulus of 12.33 (ie, indicating tough ceramics) and characteristic strength of 186 MPa were calculated, below which no failure was expected. A critical quenching temperature was found as about 400°C (ΔT ~ 380°C) by indentation‐quench method. In addition, a thermal shock resistance parameter (eg, R in °C) of 142.4°C was calculated, which was higher than commonly used Al2O3 ceramics. Lower dielectric constant and loss are desired for wide frequency band application and shorter signal transmission delay time in radomes. The dielectric constant was found as nearly 7.5 up to 10 GHz and loss tangent was 0.0031 at 5 MHz. Dielectric and thermomechanical results suggest that mullite is a suitable candidate ceramic for radome applications.

Resilient wide‐area multi‐mode controller design based on Bat algorithm for power systems with renewable power generation and battery energy storage systems

Modern power systems consist of power electronics devices, which are used in renewable energy (RE) conversion. However, these devices, associated controllers, and uncertainty in RE output could bring new challenges to power system stability, especially oscillatory stability. Hence, the integration of battery energy storage systems (BESSs) is being developed to minimise the uncertainty and variability in renewables. Furthermore, to tackle the complex dynamics and inertia-less characteristics of wind and PV plants additional controllers such as power oscillation damping (POD) control and virtual inertia scheme are sought. However, the primary challenges associated with the wide-area oscillation damping controller are signal transmission delay, loss of communication signal, data drops, and others. This paper proposes a bat algorithm (BA) based resilient wide-area multi-mode controller (MMC) for enhancing oscillatory stability margin with high penetration of renewable power generations (RPGs) and BESSs. The Java 500 kV Indonesian grid is used to evaluate the performance of the resilient wide-area MMC. From the results, it is found that the proposed controller effectively damp the critical mode of oscillation in the system even under communication failure as well as certain damping controller failures.

Memory-based State Estimation of T–S Fuzzy Markov Jump Delayed Neural Networks with Reaction–Diffusion Terms

This paper investigates the problem of state estimation for Takagi–Sugeno (T–S) fuzzy Markov jump delayed neural networks with reaction–diffusion terms. A memory-based control scheme that contains a constant signal transmission delay is adopted, which is the first attempt to handle the issue of state estimation for fuzzy neural networks. Firstly, several conditions that guarantee the stability of the considered system are derived. Then, the fuzzy memory-based controller design scheme is proposed. Finally, three numerical examples are given to demonstrate the validity of the proposed method.

Programmable Hierarchical C-RAN: From Task Scheduling to Resource Allocation

Traffic delay is a key metric to measure the quality-of-service of next-generation wireless communication networks. In this paper, we consider a cloud radio access network architecture with a hierarchical structure of virtual controllers and multiple clusters of remote radio heads (RRHs). A high-level controller coordinates control plane decisions among local controllers and each local controller is in charge of a cluster of RRHs. Moreover, each local controller is equipped with one server for creating virtual machines (VMs) to execute the users’ baseband processing tasks. Then, under the considered architecture, we aim to minimize the average delay consisting of task execution delay and signal transmission delay under total power constraint, by joint optimization of task scheduling and resource allocation, including VM allocation and RRH assignment. Due to the non-deterministic polynomial-time hardness (NP-hardness) of the joint optimization problem, we translate it into a matroid constrained submodular maximization problem and propose heuristic algorithms to find solutions with 0.5-approximation. Besides, both centralized and distributed control schemes are considered. In the centralized control scheme, all decisions about task scheduling, VM allocation, and RRH assignment are made in the high-level controller. But in the distributed control scheme, the high-level controller is only in charge of task scheduling based on graph theory and the local controllers are responsible for their respective VM allocation and RRH assignment. The simulation results show that the proposed algorithms can achieve better performance than the separate optimization of VM allocation and RRH assignment.

Копирующее управление удаленными роботами с задержкой в передаче сигнала

Копирующее управление удаленными роботами с задержкой в передаче сигнала

Event-Triggered Fault Detection for Networked LPV Systems

This paper is concerned with the fault detection problem for a class of continuous-time linear parameter-varying systems with signal transmission delays. An effective event-triggered communication scheme is introduced to reduce the burden of the shared network where the current sampled data will be sent only when the certain condition is satisfied. By properly designing a novel fault detection filter and augmenting the states of the original system, the addressed fault detection problem can be transformed into a $$H_\infty $$ filtering problem for the filtering error system with uncertain parameters. According to the parameter-dependent Lyapunov–Krasovskii functional method and free-weighting matrix technique, the sufficient conditions, which guarantee the filtering error system satisfying the prescribed $$H_\infty $$ performance constraint, are derived in the form of parameterized linear matrix inequalities. The basic functions and gridding technique are used to deal with the corresponding parameterized convex problem. Here, the LPVTools is used to convert the infinite-dimensional feasibility conditions into a finite-dimensional set of LMIs. Then, the MATLAB LMI toolbox is applied for solving the LMI problem of finite dimensions. Moreover, the explicit expressions of the target filter parameters are also obtained. Finally, two simulation examples are provided to illustrate the validity of the proposed fault detection method.

Nonfragile Sampled-Data Synchronization for Delayed Complex Dynamical Networks With Randomly Occurring Controller Gain Fluctuations

In this paper, the problem of nonfragile sampled-data synchronization of delayed complex dynamical networks with randomly occurring controller gain fluctuations (ROCGFs) is studied. First, more applicable nonfragile memory sampled-data controllers are designed, which involve the signal transmission delay and ROCGFs. The controller gain fluctuations appear in a random way, which obey certain Bernoulli distributed white noise sequences. Second, a modified piecewise Lyapunov-Krasovskii functional (LKF), which involves cubic sawtooth structure term, is constructed for the first time. Third, based on the LKF, less conservative synchronization criteria are established. In comparison with the existing results, the constraint condition of the positive definition of the LKF is less restrictive, since it does not need to be positive definite for all time, but is only required to be positive definite at sampling times. Finally, the effectiveness and advantages of the obtained results are illustrated by two numerical examples.

Central Heating System Constrained Control with Input Delay Based on Neural Networks

An output constrained control with input delay is proposed for a central heating system. Due to the delay of signal transmission and valves opening time, an input delay is considered into the system and an auxiliary system is employed to handle this issue by converting the delayed input into a delay-free one. Moreover, to ensure the output supply water temperature within a limited range, Barrier Lyapunov algorithm is involved to achieve desired control accuracy. Finally, external disturbance and model uncertainty are incorporated into the dynamic system and neural networks (NN) are trained in an online fashion for the compensation. The stability of the control system is guaranteed through rigorous Lyapunov analysis and the excellent control performance over traditional PID control is demonstrated via numerical simulation study.