Adaptive Time Delay Research Articles

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.

Adaptive time delay estimation algorithm for indoor near‐field electromagnetic ranging

SummaryThe near‐field electromagnetic ranging technology exploits the near‐field behavior of radio signals to measure distance in indoor real‐time location system. In this paper, we propose a new adaptive time‐delay estimation algorithm based on maximum correntropy criterion (MCC) and received signal strength indication (RSSI), abbreviated as RSSIMCC. The proposed RSSIMCC algorithm estimates the time delay between the electric and magnetic components of low‐frequency signals in the near field instead of detecting the phase difference. An inaccurate time‐delay estimation value obtained according to the RSSI is used as the initial time delay of adaptive time‐delay estimator, which is not far from the true time delay and can improve the convergence speed. Through the analysis of simulation and experiment results, we conclude that the proposed RSSIMCC algorithm can estimate the time delay with small steady‐state error in near‐field electromagnetic ranging application. Then the time delay estimated by RSSIMCC algorithm can be exploited based on the near‐field behavior of radio signals for ranging in an indoor environment. Copyright © 2016 John Wiley & Sons, Ltd.

Explicit time-delay compensation in teleoperation: An adaptive control approach

Summary This paper proposes a control framework that addresses the destabilizing effect of communication time delays and system uncertainties in telerobotics, in the presence of force feedback. Force feedback is necessary to obtain transparency, which is providing the human operator as close a feel as possible of the environment where the slave robot is operating. Achieving stability and providing transparency are conflicting goals. This is the major reason why, currently, a very few, if at all, fully operational force feedback teleoperation devices exist except for research environments. The proposed framework handles system uncertainty with adaptation and communication time delays with explicit delay compensation. The technology that allows this explicit adaptive time-delay compensation is inspired by Massachusetts Institute of Technology (MIT)'s Adaptive Posicast Controller. We provide simulation results that demonstrate stable explicit adaptive delay compensation in a force-reflecting teleoperation set up. Copyright © 2016 John Wiley & Sons, Ltd.

Application of Adaptive Time Delay Model in Optimal Control of a Hydropower Cascade

Small hydropower plants (SHP) are increasingly constructed in recent years as a substitution of the use of conventional energy materials such as wood and fossil fuel in remote rural regions. Besides new constructed plants, upgrading operational strategies of existed systems for increasing electricity productivity is also significant. Motivated by this issue, a combination of two techniques, simulation and optimization based on models is frequently used to improve the operation regimes of coordinated reservoir cascades. However, the application of a complex hydraulic model consumes a huge computation time. Hence, this paper proposes a replacement for the complex hydraulic model by an adaptive time delay (ATD) model. The cutting- edge point is that the ATD model is able to quickly predict the system dynamics both in simulation and optimization. This ATD model consists of only two parameters: time constant and time delay which are functions of unsteady flow and can be easily derived from complex hydraulic models (HECRAS, MIKE11), or from physical parameters of rivers (flow rate, roughness, bed slope, cross section). The integration of the ATD model into the simulation and optimization techniques will be demonstrated by a case study of a cascade of SHPs. In terms of optimization, a non-linear constrainted optimization algorithm is applied to improve electricity production to meet the scheduled demand.

A Robust and Adaptive Line Echo Canceller

In the actual communication system, the line echo is a frequent problem, so the line echo canceller is necessary. Traditionally, the cancellers are designed according to line noises based on the Gaussian noise model. However, the noises do not obey the Gaussian distribution; therefore the performances of algorithms decrease, even cannot be used. In order to solve this problem, this paper proposes a line echo canceller based on alpha stable distribution model. The Method in this line echo canceller has Robust and Adaptive. Alpha stable distribution modeling impulse noise, and under the rule of minimum average <i>p</i> norm, adaptive time delay estimator offsets the echo. This method can not only effectively offset the line echo with the Gaussian noise, but also to have the effect of good to echo with impulse noise. Theoretical analysis and computer simulation experiments show that it is very kind of the line echo canceller. It is robust to impulse noises and adaptive to echo cancellation.

Reduction of clinically irrelevant alarms in patient monitoring by adaptive time delays.

The problem of high rates of false alarms in patient monitoring in anesthesiology and intensive care medicine is well known but remains unsolved. False alarms desensitize the medical staff, leading to ignored true alarms and reduced quality of patient care. A database of intra-operative monitoring data was analyzed to find characteristic alarm patterns. The original data were re-evaluated to find relevant events and to rate the severity of these events. Based on this analysis an adaptive time delay was developed that individually delays the alarms depending on the grade of threshold deviation. The conventional threshold algorithm led to 4893 alarms. 3515 (71.84%) of these alarms were annotated as clinically irrelevant. In total 81.0% of all clinically irrelevant alarms were caused by only mild and/or brief threshold violations. We implemented the new algorithm for selected parameters. These parameters equipped with adaptive validation delays led to 1729 alarms. 931 (53.85%) alarms were annotated as clinically irrelevant. 632 alarms indicated the 645 clinically relevant events. The positive predictive value of occurring alarms improved from 28.16% (conventional algorithm) to 46.15% (new algorithm). 13 events were missed. The false positive alarm reduction rate of the algorithm ranged from 33 to 86.75%. The overall reduction was 73.51%. The implementation of this algorithm may be able to suppress a large percentage of false alarms. The effect of this approach has not been demonstrated but shows promise for reducing alarm fatigue. Its safety needs to be proven in a prospective study.

Adaptive noise cancellation based on EMD in water-supply pipeline leak detection

In water-supply pipeline leak detection and location, both the leak signals and blurred noises are closely related to the pipeline states and surroundings and most of the conventional noise-cancellation methods have to depend on the empirical parameters of either signals or noises. EMD (Empirical Mode Decomposition) is an adaptive signal decomposition method and is exclusive of base functions. A signal is decomposed into several IMFs (Intrinsic Mode Functions) in EMD, then the noise in a signal can be cancelled through removing uncorrelated IMFs. The existing EMD noise cancellation methods need to know the characteristics of either the wanted signal or the noise for rebuilding the noise-removed signal. However the characteristics of leak signals and noises are not fixed in various pipeline conditions, so the existing EMD noise cancellation methods can’t be directly applied in water-supply pipeline leak detection. This paper proposes an adaptive noise cancellation method based on EMD, in which the IMFs that don’t or less contain the components related to the leak can be removed through the cross-correlation between the IMFs and another signal collected at the either side of a suspect leak. In simulation analysis, the adaptive noise cancellation method can increase the SNRs (Signal to Noise Ratios) of leak signals as high as 16dB. In processing practical pipeline vibro-acoustic signals, with the proposed method the peak of adaptive time delay estimate of leak signals, which determines the location of a leakage, becomes more distinguished, and thus the error of leakage location is improved.

Adaptive Time Delay Compensator (ATDC) Design for Wide-Area Power System Stabilizer

Wide-area power system stabilizer (WPSS) is one of the most potentially effective approaches to damp interarea low frequency oscillations in power systems. When WPSS is implemented in a real project, emphasis should be put on feedback signal delay problem. In order to intensively study the delay of phasor measurement unit data, field tests have been carried out in Guizhou power grid (GZPG), and results show that, in the long-term, the delay might change remarkably under some circumstances, such as when route switches or communication load increases. Therefore, this paper proposes an adaptive time delay compensation method to deal with such kind of delay. Bounded random delay is divided into several intervals and compensators are designed for each delay interval. Then appropriate compensators will be selected according to the delay measured online, based on the switching rules that is also demonstrated in this paper. The delay used here is the average delay before the compensator switching. The proposed compensator is demonstrated in a two area power system. Numerical simulation results show the effectiveness and feasibility of the proposed adaptive compensator. A comparison with conventional methods is also presented. Finally, the proposed method is validated on GZPG based on real-time digital simulations.

A novel online adaptive time delay identification technique

Time delay is a phenomenon which is common in signal processing, communication, control applications, etc. The special feature of time delay that makes it attractive is that it is a commonly faced problem in many systems. A literature search on time-delay identification highlights the fact that most studies focused on numerical solutions. In this study, a novel online adaptive time-delay identification technique is proposed. This technique is based on an adaptive update law through a minimum–maximum strategy which is firstly applied to time-delay identification. In the design of the adaptive identification law, Lyapunov-based stability analysis techniques are utilised. Several numerical simulations were conducted with Matlab/Simulink to evaluate the performance of the proposed technique. It is numerically demonstrated that the proposed technique works efficiently in identifying both constant and disturbed time delays, and is also robust to measurement noise.

An Adaptive Time Delay Alignment Technique Based on Equalizer Taps in a Sensor Array

In the wireless sensor network, every node is energy and resource limited and the received signal is weak, which requires less complexity and low computation signal combination algorithm to improve the signal-to-noise ratio (SNR) of the combined output signal. Considering the computation and time-varying delay, this paper proposes an adaptive feedback time-delay alignment method based on equalizer taps to decrease the SNR loss caused by time-delay difference. The relationship between the equalizer taps and time-delay difference is derived according to the Nyquist first criterion and the gradient descent algorithm is adopted to iteratively adjust the time delay of one antenna signal. The numerical results show that the algorithm can almost eliminate the time difference and the SNR loss decreases. The bit error rate (BER) performance is obviously improved.

Manipulation of visual biofeedback during gait with a time delayed adaptive Virtual Mirror Box.

BackgroundA mirror placed in the mid-sagittal plane of the body has been used to reduce phantom limb pain and improve movement function in medical conditions characterised by asymmetrical movement control. The mirrored illusion of unimpaired limb movement during gait might enhance the effect, but a physical mirror is only capable of showing parallel movement of limbs in real time typically while sitting. We aimed to overcome the limitations of physical mirrors by developing and evaluating a Virtual Mirror Box which delays the mirrored image of limbs during gait to ensure temporal congruency with the impaired physical limb.MethodsAn application was developed in the CAREN system’s D-Flow software which mirrors selected limbs recorded by real-time motion capture to the contralateral side. To achieve phase shifted movement of limbs during gait, the mirrored virtual limbs are also delayed by a continuously calculated amount derived from past gait events. In order to accommodate non-normal proportions and offsets of pathological gait, the movements are morphed so that the physical and virtual contact events match on the mirrored side. Our method was tested with a trans-femoral amputee walking on a treadmill using his artificial limb. Joint angles of the elbow and knee were compared between the intact and mirrored side using cross correlation, root mean squared difference and correlation coefficients.ResultsThe time delayed adaptive virtual mirror box produced a symmetrical looking gait of the avatar coupled with a reduction of the difference between the intact and virtual knee and elbow angles (10.86° and 5.34° reduced to 4.99° and 2.54° respectively). Dynamic morphing of the delay caused a non-significant change of toe-off events when compared to delaying by 50% of the previous gait cycle, as opposed to the initial contact events which showed a practically negligible but statistically significant increase (p < 0.05).ConclusionsAdding an adaptive time delay to the Virtual Mirror Box has extended its use to treadmill gait, for the first time. Dynamic morphing resulted in a compromise between mirrored movement of the intact side and gait events of the virtual limbs matched with physical events of the impaired side. Asymmetrical but repeatable gait is expected to provide even more faithful mirroring.

Role of myelin plasticity in oscillations and synchrony of neuronal activity

Conduction time is typically ignored in computational models of neural network function. Here we consider the effects of conduction delays on the synchrony of neuronal activity and neural oscillators, and evaluate the consequences of allowing conduction velocity (CV) to be regulated adaptively. We propose that CV variation, mediated by myelin, could provide an important mechanism of activity-dependent nervous system plasticity. Even small changes in CV, resulting from small changes in myelin thickness or nodal structure, could have profound effects on neuronal network function in terms of spike-time arrival, oscillation frequency, oscillator coupling, and propagation of brain waves. For example, a conduction delay of 5ms could change interactions of two coupled oscillators at the upper end of the gamma frequency range (∼100Hz) from constructive to destructive interference; delays smaller than 1ms could change the phase by 30°, significantly affecting signal amplitude. Myelin plasticity, as another form of activity-dependent plasticity, is relevant not only to nervous system development but also to complex information processing tasks that involve coupling and synchrony among different brain rhythms. We use coupled oscillator models with time delays to explore the importance of adaptive time delays and adaptive synaptic strengths. The impairment of activity-dependent myelination and the loss of adaptive time delays may contribute to disorders where hyper- and hypo-synchrony of neuronal firing leads to dysfunction (e.g., dyslexia, schizophrenia, epilepsy).

Adaptive Time Delay Estimation Using Filter Length Constraints for Source Localization in Reverberant Acoustic Environments

Adaptive time delay estimation based on blind system identification (BSI) focuses on the impulse responses between a source and a microphone to estimate the time difference of arrival (TDOA) in reverberant environments. In this letter, we consider the adaptive eigenvalue decomposition (AED) BSI method based on the normalized multichannel frequency-domain least mean square (NMCFLMS) algorithm. We show that the use of filter length constraints (FLC) based on the maximum TDOA between microphones improves the performance of the NMCFLMS filter for the localization of different sound types in highly reverberant environments. The experimental results demonstrate the improvement of the proposed method for reverberation times (RT60) of up to 2 s. Applications for this method include teleconferencing systems, musical interfaces, videogames, and monitoring systems.

Online time delay identification and control for general classes of nonlinear systems

In this study, online identification of state delays is discussed. First, a novel adaptive time delay identification technique is proposed for general classes of nonlinear systems subject to state delays. The stability of the time delay identification algorithm is analyzed via Lyapunov-based techniques. In this work, we consider the time delay as a nonlinear parameter effecting the system which is a seemingly novel departure from the existing literature. As an extension, this technique is modified to design a tracking controller for general classes of nonlinear systems subject to state delays. The main novelty of this controller is that identification of unknown state delays are ensured while output tracking objective is satisfied. Numerical simulations are conducted that demonstrate the efficiency of the time delay identification algorithm and the tracking controller.

Neural Network and Observer-Based Compensation of Networked Control Systems with Packet Dropout

This paper investigates a new alternative approach to compensate for the effects of packet dropouts (data do not arrive at destination) on a dynamic networked control system (NCS). The approach uses an observer-based control computation using time-delay predicted values as the sampling times of the NCS. To obtain the predicted values of time delays we propose the use a one-step-ahead and multi-step prediction algorithms based on an adaptive time delay neural network (TDNN). The control gains are calculated using optimal control.

Adaptive Time Delay Estimation Based on Robust Cyclic Correlation Estimator

Based on the proposed efficient cyclic correlation estimator by exploiting normalized infinite norm, this paper develops a robust adaptive time delay estimation method which provides additional robustness against heavy-tailed noise and when the noise statistics are not fully known. Simulations demonstrate the reliable and robust performance of the proposed cyclic correlation estimator and its corresponding time delay estimation algorithm.

Adaptive fuzzy H ∞ tracking design of SISO uncertain nonlinear fractional order time-delay systems

In this paper, a fuzzy logic controller equipped with training algorithms is developed such that the H∞ tracking performance should be satisfied for a model-free nonlinear fractional order time delay system which is infinite dimensional in nature and time delay is a source of instability. In order to deal with the linguistic uncertainties caused from delay terms, the adaptive time delay fuzzy logic system is constructed to approximate the unknown time delay system functions. By incorporating Lyapunov stability criterion with H∞ tracking design technique, the free parameters of the adaptive fuzzy controller can be tuned on line by output feedback control law and adaptive law. Moreover, the tracking error and external disturbance can be attenuated to arbitrary desired level. The numerical results show the effectiveness of the proposed adaptive H∞ tracking scheme.

Variable sampling approach to mitigate instability in networked control systems with delays.

This paper analyzes a new alternative approach to compensate for the effects of time delays on a dynamic networked control system (NCS). The approach is based on the use of time-delay-predicted values as the sampling times of the NCS. We use a one-step-ahead prediction algorithm based on an adaptive time delay neural network. The application of pole placement and linear quadratic regulator methods to compute the feedback gains taking into account the estimated time delays is investigated.

Estimação de frequência usando sensores de erro com atrasos adaptativos

Neste trabalho é proposta uma solução para a melhoria no desempenho dos Early Error Sensing (EES) Adaptive Time Delay Tanlock Loops (ATDTL) de 1ª ordem apresentados em (Al-Zaabi et al., 2005), quanto a estimação de frequência e tempo de captura. Os EES-ATDTL são Phase-Locked Loops (PLL) digitais utilizados para implementações em hardware, devido à simplicidade da sua estrutura. Teoremas de ponto fixo são usados para se determinar condições sob as quais é possível obter uma convergência rápida no processo de captura e é obtida uma estimativa da frequência de entrada, utilizando um filtro de tipo Gaussiano para melhorar a adaptação do ganho. Os modelos matemáticos usados são os apresentados em (Al-Araji et al., 2006). São feitas simulações para avaliar os resultados teóricos.

Imposition of Sparse Priors in Adaptive Time Delay Estimation for Speaker Localization in Reverberant Environments

In this letter, we describe a method to estimate the time delay for speaker localization in reverberant environments. Based on an adaptive eigenvalue decomposition (AED) algorithm, the method takes the reverberation fully into account by estimating channel impulse responses from a speaker to sensors directly, but it may suffer from whitening effects for temporally correlated natural sounds. Imposing sparse priors on the responses can reduce the temporal whitening and provide a more accurate and robust time delay of speaker location. Experiments demonstrate that the proposed method can efficiently estimate the time delay for speaker localization in reverberant environments.