Time Delay Estimation Research Articles

Assessing the quality of the time delay estimate in acoustic leak localisation

The problem considered in this paper is assessing the quality of the time delay estimate between leak signals measured on water pipes. This is practically important, as a quantitative assessment of the accuracy of time delay estimation (TDE) results makes it possible to infer the reliability of acoustic leak localisation results in a given situation. Three quality assessment approaches are developed by considering the statistical properties of the cross-correlation function (CCF): information-criterion, processing gain, and statistical approaches. In the information-criterion approach, the Bayes factor (BF) is employed to decide the most likely probability distribution of observed CCF peak values. The processing gain approach determines the quality of the time delay estimate using indices that indicate detectability of the CCF peak, namely, the peak-to-side lobe ratio (PSR) and the peak-to-mean ratio (PMR). In the statistical approach, an index termed inconsistency score (ICS) is used to describe the quality of TDE results based on root-mean square of deviations of time delay estimates from their statistical mode. Experimental results show that the proposed approaches provide effective means of assessing the accuracy of the time delay estimate in acoustic leak detection applications. Also, the proposed indices can be employed as figures of merit for selecting best parameters for TDE, for example, filter cut-off frequencies.

Design of a robust intelligent controller based neural network for trajectory tracking of high-speed wheeled robots

The control design of wheeled mobile robots is often accomplished based on the robot’s kinematics which imposes critical challenges in the motion tracking control of such systems. In the related literature, most works designed dynamic controllers based on the terms of voltage or torque; however, the velocity is often utilized in industrial and commercial applications. To address this issue, this paper focuses on the design of a velocity-based control for the trajectory-tracking problem of mobile robots in practical applications including measurement acquisition. The control design of the mobile robot is realized in two separate parts using kinematic control and dynamic control. The design of kinematic control is carried out based on the robot’s kinematics where the motion is described without considering the forces and torques. A radial basis function (RBF) neural network (NN) based on the model-free controller is designed for the dynamic controller of mobile robots without relying on the system dynamics. In the proposed dynamic control, a time-delay estimation is adopted to estimate the disturbances and noises in the mobile robot and remove them from the feedback loop control. Several typical scenarios of mobile robots with high-speed movements are conducted to assess the feasibility of the proposed NN controller-based time-delay estimation under noises and disturbances. To show the supremacy of the suggested controller, the dynamic responses of the mobile robot test system are compared with the prevalent controllers. The extensive simulation and real-time examinations reveal the capability of the proposed NN controller-based time-delay estimation to control high-speed mobile robots under high-level noises and disturbances.

Acoustic leak localization for water distribution network through time-delay-based deep learning approach

Water leakage within water distribution networks (WDNs) presents significant challenges, encompassing infrastructure damage, economic losses, and public health risks. Traditional methods for leak localization based on acoustic signals encounter inherent limitations due to environmental noise and signal distortions. In response to this crucial issue, this study introduces an innovative approach that utilizes deep learning-based techniques to estimate time delay for leak localization. The research findings reveal that while the Res1D-CNN model demonstrates inferior performance compared to the GCC-SCOT and BCC under high signal-to-noise ratio (SNR) conditions, it exhibits robust capabilities and higher accuracy in low SNR scenarios. The proposed method's efficacy was empirically validated through field measurements. This advancement in acoustic leak localization holds the potential to significantly improve fault diagnosis and maintenance systems, thereby enabling efficient management of WDNs.

An Adaptive Sliding Mode Control Using a Novel Adaptive Law Based on Quasi-Convex Functions and Average Sliding Variables for Robot Manipulators

This paper proposes a novel adaptive law that uses a quasi-convex function and a novel sliding variable in an adaptive sliding mode control (ASMC) scheme for robot manipulators. Since the dynamic equations of robot manipulators inevitably include model uncertainties and disturbances, time-delay estimation (TDE) errors occur when using the time-delay control (TDC) approach. Further, the ASMC method used to compensate for TDE errors naturally causes a chattering phenomenon. To improve tracking performance while reducing or maintaining chattering, this paper proposes an adaptive law based on a quasi-convex function that is convex at the origin and concave at the gain switching point, respectively. We also adopt a novel sliding variable that uses previously sampled tracking errors and their time derivatives. Further, this paper proves that the sliding variable of the robot manipulator controlled by the proposed ASMC satisfies uniformly ultimately bounded stability. The simulation and experimental results illustrate the effectiveness of the proposed methods in terms of tracking performance.

Frequency identification method based on active aliasing of biprobes blade tip timing without once-per-revolution

Rotary blades are the core components of aeroengines, gas turbines and centrifugal compressors. However, due to the complexity of their work environment, monitoring their health is necessary. Blade tip timing (BTT) is an effective non-contact monitoring method for rotating blades. However, conventional BTT rely heavily on once-per-revolution (OPR) signals, which cannot be accurately obtained in complex environments. At the same time, there is a serious under-sampled problem in the BTT signal. Consequently, the extraction of blade vibration parameters from under-sampled BTT signals without relying on OPR signals remains a research hotspot. In this paper, an active aliasing method based on the vibration difference of the blade is proposed to extract the vibration frequency of the blade. In the proposed method, two BTT probes are used to calculate the blade vibration difference, and time delay estimation is used to estimate the approximate value of the natural frequency at small angle delays. Then, a series of aliasing frequencies are generated under various large angle delays. Next, the estimated natural frequency and aliasing frequency are combined to solve the exact natural frequency value. In addition, numerical simulations demonstrate the effectiveness and robustness of the method. Finally, experimental research was conducted on the BTT experimental platform. As a result, this method can accurately identify the inherent frequency of asynchronous vibration of the blades.

Collaborative control of circumferential yarn implantation system for large-scale 3D braiding equipment using fast nonsingular terminal sliding mode

To ensure high performance collaborative control of the circumferential yarn implantation system for a specific large-scale 3D braiding equipment, a multi-motor fast nonsingular terminal sliding mode (FNTSM) controller is proposed using time-delay estimation (TDE) and deviation coupling control (DCC). Then, comparative experiments were conducted among our proposed TDE-based FNTSM controller and PD controller and TDE-based PD controller. As shown in the experimental results, the root-mean-square error (RMSE) and maximum absolute error (MAE) ensured by our FNTSM controller tracking sine signal have decreased by 89.4% and 67.1% compared with the ones by PD controller; while the RMSE of our FNTSM controller tracking slope signal is ensured within 0.03°. Meanwhile, the RMSE and MAE of our FNTSM controller tracking sine signals has been reduced by 48.2% and 56.9% due to DCC, respectively. Afterwards, less than 3% error fluctuation has been observed with load using our FNTSM controller. High control performance and strong robustness have been experimentally observed.

Enhanced tracking control for n-DOF robotic manipulators: A fixed-time terminal sliding mode approach with time delay estimation

This work proposes a fixed-time terminal sliding mode control scheme with time delay estimation (FxTSM-TDE) for unknown nonlinear robotic systems under uncertainties and unknown bounded disturbances. The idea of the fixed-time terminal sliding mode control method (FxTSM) is thoroughly explained and analyzed. The FxTSM approach has been used to achieve nonsingular fixed-time control, non-chatter control inputs, and superior trajectory tracking performance. Then, the proposed approach for estimating the unknown robot dynamics utilizes the TDE approach. The Lyapunov analysis is used to establish the fixed-time stability of the system. The effectiveness of the suggested approach is evaluated, demonstrated, and contrasted with the existing control schemes by illustrating the results of the proposed method's enhanced performance in tracking accuracy, robustness, and convergence speed when implemented in robotic manipulator dynamics. The results indicate that the existing scheme has corresponding root mean square (RMS) error values of 0.0235 and 0.0283, while the proposed scheme has values of 0.0178 and 0.0188, respectively.

Fractional‐order fixed‐time sliding mode control of robotic manipulators with robust exact differentiator

AbstractThis paper introduces a robust position tracking control structure of exoskeleton robots (ERs). An open environment and body flexibility are the main challenges in ER control system design. First, a fractional‐order nonsingular terminal sliding mode (FONTSM) controller is proposed for a robotic manipulator with uncertainty, where lumped unknown dynamics are computed by time‐delay estimation (TDE). Secondly, a robust exact differentiator (RED) is employed as the output feedback observer for the velocity sensorless robotic system. Thirdly, the stability of the FONTSM control system is demonstrated. It is proved that the system error can move to the specified value along the sliding mode surface at a fixed time and can reach the sliding mode surface after a finite time. Finally, simulations on a 2‐DOF robotic manipulator show the effectiveness of the fixed‐time control strategies. When a combined trigonometric function disturbance is applied, the joint angle error is reduced to 1% by the model‐free controller based on output feedback within approximately 1 s.

A high-order time-delay difference estimation method for signal enhancement in the distorted towed hydrophone array.

In passive sonar systems, deviations from an ideal linear configuration can significantly impair the beamforming performance of towed hydrophone arrays. This paper presents a method aimed at improving the underwater acoustic signals in the presence of array distortion. The method is centered on a high-order time-delay difference estimation technique utilizing time-frequency autofocus. Initially, a detailed signal model is established that captures the distinctive characteristics of distorted arrays. Subsequently, an algorithm is introduced for high-order time-delay difference estimation to enhance signal fidelity by leveraging phase information within narrowband components originating from incidental acoustic sources. Additionally, a quality metric to evaluate these components is introduced, facilitating the practical implementation of the method. The effectiveness of the proposed approach is validated through both simulation and experimental results, demonstrating its superiority over existing techniques. Importantly, this method does not require prior knowledge of the distortion pattern, making it adaptable to various non-linear array configurations.

A new time-delay estimation: phase difference-reassigned transform

A new time-delay estimation: phase difference-reassigned transform

Model-free composite sliding mode adaptive control for 4-DOF tower crane

For the 4-DOF tower crane system, the lack of actuated number brings great challenges to the realization of accurate positioning and effective swing suppression, particularly with system uncertainties and disturbances. To address this, a model-free composite sliding mode (CSM) adaptive control method for 4-DOF tower crane system with multiple types of uncertainties is proposed. Specifically, the CSM surfaces are constructed by coupling the driving and non-driving variables to enhance the ability to suppress swing. Based on CSM surfaces, not only the asymptotic stability of the system equilibrium point is proved, but also the continuity of the designed control law is ensured. In addition, according to time delay estimation (TDE) technique, system uncertainties and disturbances are dealt with, and achieving model-free control. Finally, the controller is implemented on a 4-DOF tower crane experiment platform, and the effectiveness of the proposed control strategy is verified by four sets of hardware experiments.

Spatiotemporal discoordination of brain spontaneous activity in major depressive disorder

BackgroundMajor depressive disorder (MDD) is a widespread mental health issue, impacting spatial and temporal aspects of brain activity. The neural mechanisms behind MDD remain unclear. To address this gap, we introduce a novel measure, spatiotemporal topology (SPT), capturing both the hierarchy and dynamic attributes of brain activity in depressive disorder patients. MethodsWe analyzed fMRI data from 285 MDD inpatients and 141 healthy controls (HC). SPT was assessed by coupling brain gradient measurement and time delay estimation. A nested machine learning process distinguished between MDD and HC using SPT. Person's correlation tested the link between SPT's and symptom severity, and another machine learning method predicted the gap between patients' chronological and brain age. ResultsSPT demonstrated significant differences between patients and healthy controls (F = 2.944, p < 0.001). Machine learning approaches revealed SPT's ability to discriminate between patients and healthy controls (Accuracy = 0.65, Sensitivity = 0.67, Specificity = 0.64). Moreover, SPT correlated with the severity of depression symptom (r = 0.32. pFDR = 0.045) and predicted the gap between patients' chronological age and brain age (r = 0.756, p < 0.001). LimitationsEvaluation of brain dynamics was constrained by MRI temporal resolution. ConclusionsOur study introduces SPT as a promising metric to characterize the spatiotemporal signature of brain function, providing insights into deviant brain activity associated with depressive disorders and advancing our understanding of their psychopathological mechanisms.

Optimal control for linear cyber physical systems under replay attacks via time delay estimation approach

We mainly study the optimal control problem for linear cyber physical systems under replay attacks in this paper. The methodological research mainly relies on the following two starting points: (1) Promptly detect and estimate the malicious replay attack behaviours hampering normal system procedures; (2) Design a resilient optimal control strategy to ensure the system regularly works as much as possible. Specifically, the linear cyber physical system becomes a time delay system after the replay attack, and then an algorithm is designed to transform the system into a delay-free system and ensure its stability. Finally, a numerical simulation is employed to demonstrate the practicality and efficacy of the proposed approach.

On the efficiency of misspecified Gaussian inference in nonlinear regression: Application to time-delay and Doppler estimation

Nonlinear regression plays a crucial role in various engineering applications. For the sake of mathematical tractability and ease of implementation, most of the existing inference procedures are derived under the assumption of independent and identically distributed (i.i.d.) Gaussian-distributed data. However, real-world situations often deviate from this assumption, with the true data generating process being a correlated, heavy-tailed and non-Gaussian one. The paper aims at providing the Misspecified Cramér–Rao Bound (MCRB) on the Mean Squared Error (MSE) of any unbiased (in a proper sense) estimator of the parameters of a nonlinear regression model derived under the i.i.d. Gaussian assumption in the place of the actual correlated, non-Gaussian data generating process. As a special case, the MCRB for an uncorrelated, i.i.d. Complex Elliptically Symmetric (CES) data generating process under Gaussian assumption is also provided. Consistency and asymptotic normality of the related Mismatched Maximum Likelihood Estimator (MMLE) will be discussed along with its connection with the Nonlinear Least Square Estimator (NLLSE) inherent to the nonlinear regression model. Finally, the derived theoretical findings will be applied in the well-known problem of time-delay and Doppler estimation for GNSS.

Rapid characterisation of mixtures of hydrogen and natural gas by means of ultrasonic time-delay estimation

Rapid characterisation of mixtures of hydrogen and natural gas by means of ultrasonic time-delay estimation

Modeling of drinking and driving behaviors among adolescents and young adults in the United States: Complexities and Intervention outcomes

Alcohol-impaired driving is a formidable public health problem in the United States, claiming the lives of 37 individuals daily in alcohol-related crashes. Alcohol-impaired driving is affected by a multitude of interconnected factors, coupled with long delays between stakeholders’ actions and their impacts, which not only complicate policy-making but also increase the likelihood of unintended consequences. We developed a system dynamics simulation model of drinking and driving behaviors among adolescents and young adults. This was achieved through group model building sessions with a team of multidisciplinary subject matter experts, and a focused literature review. The model was calibrated with data series from multiple sources and replicated the historical trends for male and female individuals aged 15 to 24 from 1982 to 2020. We simulated the model under different scenarios to examine the impact of a wide range of interventions on alcohol-related crash fatalities. We found that interventions vary in terms of their effectiveness in reducing alcohol-related crash fatalities. In addition, although some interventions reduce alcohol-related crash fatalities, some may increase the number of drinkers who drive after drinking. Based on insights from simulation experiments, we combined three interventions and found that the combined strategy may reduce alcohol-related crash fatalities significantly without increasing the number of alcohol-impaired drivers on US roads. Nevertheless, related fatalities plateau over time despite the combined interventions, underscoring the need for new interventions for a sustained decline in alcohol-related crash deaths beyond a few decades. Finally, through model calibration we estimated time delays between actions and their consequences in the system which provide insights for policymakers and activists when designing strategies to reduce alcohol-related crash fatalities.

Time Delay Estimation for Acoustic Temperature Measurement of Loose Coal Based on Quadratic Correlation PHAT-β Algorithm

The acoustic temperature measurement method has a broad application prospect due to its advantages of high precision, non-contact, etc. It is expected to become a new method for hidden fire source detection in mines. The acoustic time of flight (TOF) can directly affect the accuracy of acoustic temperature measurement. We proposed a quadratic correlation-based phase transform weighting (PHAT-β) algorithm for estimating the time delay of the acoustic temperature measurement of a loose coal. Validation was performed using an independently built experimental system for acoustic temperature measurement of loose coals under multi-factor coupling. The results show that the PHAT-β algorithm estimated acoustic TOF values closest to the reference line as the sound travelling distance increased. The results of coal temperature inversion experiments show that the absolute error of the PHAT-β algorithm never exceeds 1 °C, with a maximum value of 0.862 °C. Using the ROTH weighted error maximum, when the particle of the coal samples is 3.0–5.0 cm, the absolute error maximum is 4.896 °C, which is a difference of 3.693 °C from the error minimum of 1.203 °C in this particle size interval. The accuracy of six algorithms was ranked as PHAT-β &gt; GCC &gt; PHAT &gt; SCOT &gt; HB &gt; ROTH, further validating the accuracy and reliability of the PHAT-β algorithm.

Localization of Speaker using Fusion Techniques and Neural Network Algorithms

ABSTACT Sound source localization especially speech and speaker is sole of the most significant techniques recently because used in various applications like smart environments, industry, robots, and audio conferences. So, the usage of these techniques needs more accuracy. In this paper, a speaker localization proposed it depends on the speech signals in closed spaces by employing fusion techniques and neural networks (NN) algorithms to get more accuracy. The proposed work included finding the classification of the speaker signals, which included three phases: the preprocessing phase, the phase of the feature extraction and classification phase. Data Fusion technique used to generate the dataset of speakers. In feature extraction phase features fusion technique was used for constructing a feature vector by using Generalized Cross Correlation (GCC) for time delay estimation, Root_MUSIC, and Minimum Variance Distortion Less (MVDR) for a direction of arrival for the signal source. In the classification stage two NN algorithms used, Restricted Boltzmann Machine (RBM), which implemented using Tensor flow library and Long Short-Term Memory (LSTM), which implemented using Keras library. The experiments results shows that the accuracy of the two methods was 99.84%, 99.15% for RBM, and LSTM respectively.

Path-following control of an underactuated stratospheric airship with unknown dynamics and external disturbances using time-delay control

In this paper, the path-following problem for an underactuated stratospheric airship with unknown dynamics and external disturbances has been studied. Vector field guidance is employed to fix the underactuated issue and generate desired heading. Time-delay control (TDC) is utilised to track desired states and estimate the model uncertain and disturbances using simple delayed states. To compensate time-delay estimation (TDE) errors, terminal sliding-mode is utilised and combined with TDC. The stability of the proposed controller is provided by using Lyapunov function. A numerical simulation is demonstrated to prove the performance of the proposed control algorithm.

Adaptive Fuzzy Integral Sliding Mode Cooperative Control Based on Time-Delay Estimation for Free-Floating Close-Chain Manipulators.

Space manipulators are expected to perform more challenging missions in on-orbit service (OOS) systems, but there are some unique characteristics that are not found on ground-based robots, such as dynamic coupling between space bases and manipulators, limited fuel supply, and working with unfixed bases. This paper focuses on trajectory-tracking control and internal force control for free-floating close-chain manipulators. First, the kinematics and dynamics of free-floating close-chain manipulators are given using the momentum conservation and spatial operator algebra (SOA) methodologies, respectively. Furthermore, an adaptive fuzzy integral sliding mode controller (AFISMC) based on time delay estimation (TDE) was designed for trajectory-tracking control, and a proportional-integral (PI) control strategy was adopted for internal force control. The global asymptotic stability of the proposed controller was proven by using the Lyapunov methodology. Three cases were conducted to verify the efficiency of the controller by using numerical simulations on two six-link manipulators with a free-floating base. The controller presents the desired tracking capability.