Dynamic Time Delay Research Articles

Online monitoring of resistance spot welding electrode wear state based on dynamic resistance

The electrode state is an important factor affecting the welding quality in the process of resistance spot welding, but there is still lack of an effective method to monitor the electrode wear state. In this paper, a novel online monitoring method of electrode wear state is proposed by exploring the variation pattern of dynamic resistance. The evaluation method of time series similarity is important for dynamic resistance data processing, and two types of evaluation methods including static evaluation method and dynamic evaluation method are proposed in this paper. The static evaluation methods include shape change factor, dynamic resistance decrease ratio and peak time delay, and the dynamic evaluation method refers to the trend change factor. The welding process parameters and the welding material are kept unchanged during the experiment, the newly polished electrode is used to weld 1300 times continuously, and dynamic resistance is collected. According to the results of data processing, the change of the electrode state can be divided into three stages: The electrode state is stable when the welding number is less than 360. When the welding number is in the range of 360–800, the electrode state is in the transition stage, and the electrode state rapidly deteriorates with the increase of the welding number. When the welding number is more than 800, the electrode state is completely deteriorated, and the electrode needs to be dressed. This study may pave the way for online monitoring of electrode wear state.

Distributed Multi-robot Circumnavigation with Dynamic Spacing and Time Delay

Circumnavigation is the process whereby a single agent or multiple agents rotate around a target while preserving a circular formation, which has promising potential in real-world applications such as entrapping a malicious target or escorting an important member. For the multi-robot circumnavigation problem, spacing (i.e., the angle differences) among robots plays an important role in forming a desirable circular formation. The spacing is usually assumed to be a unified constant in most of the studies. However, when robots have different or even time-varying kinematic capabilities, a fixed and equal spacing is probably not effective for accomplishing such task as preventing an enclosed target from fleeing, and thus dynamic spacing is naturally proposed and preferred. The variations of spacing are caused by the “weights” (termed utilities) of robots. This paper relaxes the condition of piecewise constant utilities and provides the ultimate bound and the input-to-state (ISS) stability conditions for the spacing error and its dynamics respectively. In addition, since time delay is ubiquitous in practical engineering systems while seldom considered in the current studies on circumnavigation, the maximum allowable time delay within which the circumnavigation remains stable is derived using both the frequency domain method and the Lambert-W function. Finally, the theoretical results are validated by a practical simulation system.

Robust Adaptive Neural Network Control For A Class Of Multiple‐Input Multiple‐Output Nonlinear Time Delay System With Hysteresis Inputs And Dynamic Uncertainties

AbstractThis paper studies an adaptive neural control for nonlinear multiple‐input multiple‐output systems with dynamic uncertainties, hysteresis input, and time delay. The studied systems are composed of N nonlinear time‐delay subsystems and the interconnection terms are contained in every equation of each subsystem. Adaptive neural control algorithms are developed by introducing a well‐defined smooth function. The unknown time‐varying delays and the unmodeled dynamics are dealt with by constructing appropriate Lyapunov–Krasovskii functions and introducing an available dynamic signal. The main advantage of the proposed controllers is that they contain fewer parameter estimates that need to be updated online. Consequently, the accuracy of ultimate tracking errors asymptotically approaches a pre‐defined bound, and all signals in the closed‐loop systems are also ensured to be uniformly ultimately bounded. Finally, a simulation example is provided to illustrate the effectiveness and merits of the proposed adaptive neural network control schemes.

A novel convolutional neural network based approach to predictions of process dynamic time delay sequences

It is practical that correlated process variables always involve dynamic time-delay sequences. In this paper, a novel convolutional neural network (CNN) based approach is proposed to predict dynamic time delay sequences. Firstly, according to the calculating similarities between correlated process variables, the time delay sequence is extracted offline using a dynamic time delay analysis by elastic windows (EW-DTDA) method. In addition, through an additional correlation analysis between the time delay sequence and process variables data, the process variables majorly influencing the time delay sequences can be obtained. Finally, a deep learning CNN model between the extracted time delay sequence and the obtained majorly influencing variables is constructed to predict the time delay sequence online. In order to validate the effectiveness of the proposed method, the method is applied to a real distillation column for analyzing dynamic time delay sequences, the simulation results conformed the effectiveness of the proposed approach.

Near MAP Dynamical Delay Estimator and Bayesian CRB for Coded QAM Signals

This paper presents an off-line algorithm for dynamical time delay recovery for which the whole observation block is used. The time offset varies over the observation interval following a random walk model. The proposed synchronizer applies to data-aided (DA), non-data-aided (NDA), and code-aided (CA) modes. Theoretical performance of the off-line technique is derived and compared with simulation results. The Bayesian Cramer-Rao Bound (BCRB) is also evaluated for DA, NDA, and CA modes and for both the off-line and on-line scenarios. Simulation results show the improvement brought by the off-line and the CA schemes. The presented algorithm outperforms the conventional on-line estimator, which only considers the current and previous observations, and its mean square error approaches the BCRB.

A novel dynamic timed fuzzy Petri nets modeling method with applications to industrial processes

Time fuzzy Petri nets (TFPNs) have been widely used to describe the transfer correlations among industrial process variables. However, the assignments of parameters associated with traditional TFPNs mostly rely on human expert knowledge. Additionally, traditional TFPNs have limited abilities to deal with dynamic time delays between correlated variables. In response to these problems, a dynamic timed fuzzy Petri nets (DTFPNs) modeling approach based on the dynamic time delay analysis (e-DTA) is proposed. Firstly, the basic structure of Petri nets is determined by taking advantage of process knowledge. Subsequently, as an improvement, a colored graph describing dynamic time delays between correlated variables is created using data mining techniques. A reachability analysis with temporal constraints is accordingly performed to track the system evolution dynamically. The proposed method is applied to a numerical case and a distillation column simulation, verifying the effectiveness of the contribution.

A similarity elastic window based approach to process dynamic time delay analysis

Due to a large number of correlated process variables involved in industrial processes, dynamic characteristics of time delays between correlated process variables are generally major concerns. Traditionally, the time delay is approximately estimated by static sliding time windows, which could not better deal with the dynamics of time delays. In response to this problem, this paper proposes a dynamic time delay analysis (e-DTA, dynamic time delay analysis by elastic windows) method based on similarity elastic windows, which is aiming at effectively estimating the transfer time delay between process variables. According to contrast similarities between correlated variables, the size of the elastic window is self-tuned and the dynamic delay time can be estimated offline. Subsequently, through an additional correlation analysis for time series of the time delay estimated from historical data, main variables influencing the time delay can be obtained. By providing relevant trend variables, an improved fuzzy interpolation prediction method is suggested to estimate the transfer time delay between process correlated variables online. In addition, an e-DTA dynamic directed time graph is created by combining dynamic transfer time delays of mutually dependent variables. Finally, performances of the e-DTA method are tested through a numerical study and a distillation column simulation.

A dynamic time delay analysis approach for correlated process variables

Generally, dynamic characteristics of time delays between process correlated variables are major concerns in the process control community. However, traditional delay time analysis methods have limited ability to deal with dynamics of time delays. In response to this problem, this paper proposes a dynamic time delay analysis (DTA) based on the technology of time series data mining, aiming at effectively estimating transfer time delays between process correlated variables. Employing dynamic sliding windows, dynamic time delays can be estimated offline by calculating similarities between correlated variables. Subsequently, through an additional correlation analysis between the time delay and process variables, main variables influencing the time delay can be obtained. By providing relevant trend variables, an improved fuzzy interpolation prediction method is suggested to estimate the transfer time delay between process correlated variables online. In addition, a DTA dynamic directed time graph is created by combining dynamic transfer time delays of mutually dependent variables. Finally, performances of the DTA method are tested through a numerical study and a distillation column simulation.

Guest editorial: special issue on Consensus‐based Applications in Networked Systems

Guest editorial: special issue on Consensus‐based Applications in Networked Systems

Dynamic behavior analysis and time delay feedback control of gear pair system with backlash non-smooth characteristic

The present work investigates the non-smooth vibration characteristic and time delay feedback control of a gear pair system involving backlash and time-varying mesh stiffness. Firstly, a gear pair model with backlash non-smooth characteristic is established. Then in combination with the discontinuity mapping method, Floquet theory is presented to determine the stability and bifurcation of periodic response, and the period doubling bifurcation has been accurately predicted. Moreover, the maximal Lyapunov exponent is obtained to determine the chaos state in gear pair system which is conform to the bifurcation diagram and Poincare section. Finally, a time delay feedback is introduced to control the dynamic behaviors of the system, and numerical simulation results show that the system can be effectively controlled from chaotic motion into stable periodic motion by increasing the delay feedback gain or delay time.

Effects of dynamic synapses, neuronal coupling, and time delay on firing of neuron

Neuronal firing plays a key role in the neuronal information transmission, and different neuronal firing patterns are reported, such as spiking, bursting. A number of neuron models are introduced to reproduce the firing patterns of single neuron or neuronal network. The key factors determining the firing pattern gain more and more attention in the study of neuron system, such as noise, network topology. Noise is able to induce sub-or super-threshold coherent neuronal firing easily, and a number of coherence resonances are reported in the noise induced firing. The network topology determines the synchronization of the firing patterns of the neuronal network, and the change of network topology may induce fruitful synchronization transitions. It is well known that synapses exhibit a high variability with a diverse origin during information transmission, such as the stochastic release of neurotransmitters, variations in chemical concentration through synapses, and spatial heterogeneity of synaptic response over dendrite tree. The collective effect of all of these factors might result in the notion of dynamic synapses. In reality, the neuronal network often involves time delay due to the ?nite signal propagation time in biological networks. Recently, neuronal networks with time delay have received considerable attention. Delay-sustained neuronal firing patterns may be relevant to neuronal networks for establishing a concept of collective information processing in the presence of delayed information transmission. According to the above-mentioned motivations, the firing dynamics of the single postsynapic neuron is investigated based on a simple postsynaptic neuron model by using numerical simulation and Fourier transform analysis. In this model, the postsynapic neuron receives dynamic synaptic currents from a population of presynaptic neurons. It is found that the firing rate resonance between the pre-and postsynaptic neuron determines the firing of the postsynaptic neuron. Stimulus currents in specific frequency range are easy to stimulate postsynaptic neuron firing. The random currents released from dynamic synapses determine the postsynaptic firing rate. Then the single postsynaptic neuron is extended to a neuronal network, in which 100 neurons connect to its 4 nearest neighbors regularly and receive delayed synaptic currents from connected neurons. All the neurons in the network receive the same dynamic synaptic currents from the presynaptic neurons. The results show that the synaptic coupling in the network is able to promote the neuron firing in the network, and time delay in the synaptic coupling could reinforce the promotion, but the mode of the promotion is not changed.

Indirect Adaptive Fuzzy Supervisory Control with State Observer for Unknown Nonlinear Time Delay System

This paper proposes an indirect adaptive fuzzy neural network (FNN) controller with state observer and supervisory controller for a class of uncertain nonlinear dynamic time delay systems. First, the approximate function of unknown time delay system is inferred by the adaptive time delay FNN system. Next, a state observer is designed to estimate the unknown system states and the indirect adaptive fuzzy controller is constructed. Finally, the closed loop controller is obtained by incorporating the supervisory controller with the indirect adaptive fuzzy controller. Therefore, if the system tends to unstable, i.e., error dynamics is larger than a prescribed constraint which is determined by designer, the supervisory controller will activate to force the state to be stable. The free parameters of the indirect adaptive FNN controller can be tuned online by observer-based output feedback control law and adaptive laws by means of Lyapunov stability criterion. The resulting simulation example shows that the performance of nonlinear time delay chaotic system is fully tracking the reference trajectory. Meanwhile, simulation results show that the adaptive control effort of the proposed control scheme is much less due to the assist of the supervisory controller.

Prediction of Stent-Retriever Thrombectomy Outcomes by Dynamic Multidetector CT Angiography in Patients with Acute Carotid T or MCA Occlusions.

The selection of patients for endovascular therapy is an important issue in stroke imaging. The aim of this study was to determine the predictive value of 3 different dynamic CT angiography parameters, occlusion length, collateralization extent, and time delay to maximum enhancement, for latest generation of stent retriever thrombectomy recanalization outcomes in patients with acute ischemic stroke. In this study, subjects were selected from an initial cohort of 2059 consecutive patients who had undergone multiparametric CT, including whole-brain CT perfusion. We included all patients with a complete occlusion of the M1 segment of the MCA or the carotid T and subsequent intra-arterial stent retriever thrombectomy. Dynamic CT angiography was reconstructed from whole-brain CT perfusion raw datasets. Angiographic outcome was scored by using the modified TICI scale; and clinical outcome, by using the modified Rankin Scale. Logistic regression analyses were performed to determine independent predictors of a favorable angiographic (mTICI = 3) and clinical outcome (mRS ≤2). Sixty-nine patients (mean age, 68 ± 14 years; 46% men) were included for statistical analysis. In the regression analysis, a short occlusion length was an independent predictor of favorable angiographic outcome (OR, 0.41; P < .05). Both collateralization grade (OR, 1.00; P > .05) and time delay to peak enhancement (OR, 0.90; P > .05) failed to predict a favorable angiographic outcome. None of the dynamic CT angiography predictors were significantly associated with clinical outcome on discharge (OR, 0.664-1.011; P = .330-.953) or at 90 days (OR, 0.779-1.016; P = .130-.845). A short occlusion length as determined by dynamic CT angiography is an independent predictor of a favorable angiographic outcome of stent retriever thrombectomy in patients with ischemic stroke.

Adaptive bilateral control for nonlinear uncertain teleoperation with guaranteed transient performance

SUMMARYDue to the special working environment of teleoperation, there are usually uncertainties existing in the dynamics of teleoperating robots. In this paper, an adaptive bilateral control scheme is proposed for the nonlinear teleoperation with parameterized dynamic uncertainties and time delays. Compared to the existing time-delay adaptive bilateral controllers, the proposed scheme has the advantage of faster and more accurate parameter adaptation. In this way, the transient performance of teleoperators can be improved without increasing the control gains, which is helpful for stability of teleoperation. The adaptive bilateral controller is designed to be applicable both in free motion and in constrained motion. The synchronization errors in the unconstrained subspace are asymptotically convergent under time delays. In the constrained subspace, the contact force remains bounded with the proposed controller. The stability of teleoperation is proved using Lyapunov methods and the passivity theory. Simulation results demonstrate the effectiveness of the proposed method.

DDGni: Dynamic delay gene-network inference from high-temporal data using gapped local alignment

Inferring gene-regulatory networks is very crucial in decoding various complex mechanisms in biological systems. Synthesis of a fully functional transcriptional factor/protein from DNA involves series of reactions, leading to a delay in gene regulation. The complexity increases with the dynamic delay induced by other small molecules involved in gene regulation, and noisy cellular environment. The dynamic delay in gene regulation is quite evident in high-temporal live cell lineage-imaging data. Although a number of gene-network-inference methods are proposed, most of them ignore the associated dynamic time delay. Here, we propose DDGni (dynamic delay gene-network inference), a novel gene-network-inference algorithm based on the gapped local alignment of gene-expression profiles. The local alignment can detect short-term gene regulations, that are usually overlooked by traditional correlation and mutual Information based methods. DDGni uses 'gaps' to handle the dynamic delay and non-uniform sampling frequency in high-temporal data, like live cell imaging data. Our algorithm is evaluated on synthetic and yeast cell cycle data, and Caenorhabditis elegans live cell imaging data against other prominent methods. The area under the curve of our method is significantly higher when compared to other methods on all three datasets. The program, datasets and supplementary files are available at http://www.jjwanglab.org/DDGni/.

Cross-platform validation for a model of a low-cost Stewart platform

Low-cost motion systems have been proposed for certain training tasks that would otherwise be performed on high-performance full flight simulators. These systems have shorter stroke actuators, lower bandwidth, and higher motion noise. The influence of these characteristics on pilot perception and control behavior is unknown, and needs to be investigated. A possible approach to this would be to simulate a platform with limited capabilities with a high-end platform, and then remove the platform limitations one by one. The effects of these platform limitations on pilot behavior can then be investigated in isolation. In this paper, a model of a low-cost simulator was validated for simulation on a high-performance simulator. A dynamic model of the MPI Stewart platform was analyzed and compared with measurements of the baseline simulator response. Measurements for validation of the implementation of the model on the SIMONA Research Simulator showed that the dynamics of the MPI Stewart platform could be represented well in terms of dynamic range, time delay, and noise characteristics. The implementation of the model of the MPI Stewart platform will be used in experiments on the effects of these characteristics on pilot control behavior.

Synchronization Control for an Uncertain Dynamical Network

Synchronization of an uncertain dynamical network time-delay is investigated by means of robust control schemes. This paper is devoted to the robustness analysis of controlled complex network concerning the effect of communication delay and uncertainty, where the topology is not exactly known in the synchronized process. First, we analyze the synchronization error dynamical model of the uncertain networks concerning time-delay. Then, based on this system, we derive a sufficient condition for globally asymptotical synchronization of the controlled uncertain network without time-delay in terms of linear matrix inequality by using Lyapunov approach. Furthermore, we investigate robustness property of the controlled uncertain network affected by time-delay, and give a design criterion for the control to guarantee globally asymptotical synchronization. Finally, A simulation is provided to show effectiveness of our strategies.

Design of Control Network in Video-Intercommunication System of Intelligent Communities

In order to construct a low-priced, reliable video-intercommunication system and meet the requirements of safety and information in an intelligent community, bus technology and one of its settling programs — CAN technology are investigated. After analyzing the defects of RS485 in master-slave mode, control network of equivalent mode in multiple computers communication is proposed. Dynamic time delay control is proved to be more efficient than constant time delay in equivalent mode and is more suitable in a video-intercommunication system. Based on present situation and development tendency of intelligent building systems, CAN+RS485 bottom network is designed and improved to achieve the control network of a video-intercommunication system.

FPGA-implementation of dynamic time delay neural network for power amplifier behavioral modeling

In this paper, we propose new architectures for FPGA-implementation of a dynamic neural network power amplifier behavioral modeling. The real-valued time-delay neural network (RVTDNN) and the backpropagation (BP) learning algorithm were implemented on FPGA using Xilinx system generator for DSP and the Virtex-6 FPGA ML605 evaluation kit. Different RVTDNN architectures are proposed for various values of the number of hidden neurons, the activation function resolution, and the fixed-point data format. These architectures are evaluated and compared in terms of modeling performances and resource utilization using 16-QAM modulated test signal.

An Improved Dynamic Load Balancing Algorithm for a Distributed System in LAN

A dynamic time delay model for load balancing was proposed in distributed heterogeneous system by Hayat. But how to compute the load-transfer delay and the gain coefficient were not gived, so it is imposible to quantitatively analyse the performance of a distributed system. In this paper, an improved dynamic load balancing model with load-transfer delay is designed for LAN, and the gain coefficient is defined by a number of simulation test, so to meet requirements of LAN. At last, the simulation test is presented. The conclusion is that the improved algorithm not only saves the resources, but also realizes a reliable and efficient operation of the system. So the improved algorithm can be used to the distributed system in LAN.