Closed-loop identification with routine operating data: Effect of time delay and sampling time

Closed-Loop Identification using Routine Operating Data: the Effect of Time Delay

In a neuronal system, propagation speed of neuronal information is mainly determined by the length, the diameter, and the kind of the axons between the neurons. Thus, some communications between neurons are not instantaneous, and others are instantaneous or with some negligible delay. In the past years, effects of time delay on neuronal dynamics, such as synchronization, stochastic resonance, firing regularity, etc., have been investigated. For stochastic resonance, it has been reported recently that stochastic multi-resonance in a neuronal system can be induced by time delay. However, in these studies, time delay has been introduced to every connection of the neuronal system. As mentioned in the beginning, in a real neuronal system, communication between some neurons can be instantaneous or with some negligible delays. Thus, considering the effect of partial time delay (time delay is called as partial time delay if only part of connections are delayed) on neuronal dynamics could be more meaningful.In this paper, we focus on discussing effect of partial time delay on response amplitude of a Watts-Strogatz neuronal network which is locally modeled by Rulkov map. With the numerically obtained results, we can see that partial time delay can induce a stochastic multi-resonance which is indicated by the multi-peak characteristics in the variation of response amplitude with partial time delay. Namely, partial time delay could also induce stochastic multi-resonance in a neuronal system. Moreover, we also find that optimal response amplitude can be reached in much wider range of the partial time delay value when delayed connections are less (i.e., the partial time delay probability is small). This is different from the case in which all connections are delayed, where response amplitude become optimal only when time delay is nearly the multiples of external signal's period. But the range of the partial time delay value becomes narrower and narrower with the increasing of the partial time delay probability and when the partial time delay probability is large enough, response amplitude becomes optimal only when time delay is nearly the multiples of external signal period. It is similar to the case where all connections are delayed. Furthermore, effects of random rewiring probability and total link number in the neuronal network on partial time delay induced stochastic multi-resonance are also studied. It is found that partial time delay induced stochastic multi-resonance is robust to random rewiring probability but not robust to total link number. Stochastic resonance is a very important nonlinear phenomenon in neuroscience, thus, our obtained results could have some implications in this field.

Temperature control is desirable in heat exchanger system due to fact that chemical processing plants requires certain level of temperature for a given for a particular processing. This research aims at the development of a PID controller for outlet temperature control of a heat exchanger system. The proposed controller was developed based on MIGO (M-Constrained integral gain optimisation) tuning algorithm and simulated using MATLAB/Simulink. The system model is represented using discrete time domain approach at specific interval of sampling time, the results obtained showed that an increase in sampling time gives a resultant increase in settling time for all operating conditions considered. Similarly the values of rise time increases with the increase of sampling time at no disturbance condition but decreases during the application of disturbance. Also the occurrence of overshoot and undershoot decreases as the sampling time increases at no disturbance condition except for a sampling time of 1.0 second, but for the disturbed condition, the overshoot and undershoot are not experienced as the sampling time increase.

The objective of this paper is to design a controller for servo motor in discrete-time systems. The effect of sampling time on the proposed design will further discussed. Real-time data gained from the experimental is used to obtain the transfer function to design the PID controller. In order to get the optimal value of PID controller, ARX model structure was implemented. The effectiveness of the design is validated using MATLAB/Simulink. From the results obtained, it shows that the output for PID controller will gives the best performance of 87.79% at 55 ms sampling time.

The effect of pre-analytical handling on the stability of fractalkine, monocyte chemoattractant protein 1 (MCP1), interleukin 6 and interleukin 8 in samples of human cerebrospinal fluid

This study begins with a brief overview of the problem of harmonic distortion and its effect on the quality of electric power, how the active power filter mitigate this problems then investigate the effect of control circuit time delay on the performance of Active Power Filter (APF). Active filter is an electronic power system that absorbs harmonic currents in the network, generated by nonlinear loads. The nonlinear load seems the main source of harmonic distortion in the power system. A shunt active power filter with optimized PI discussed in this study. Delay times are inevitable in Digital Signal Processing (DSP) based controllers can significantly degrade the performance of active power filter. Active filter performance can be affected by the delay between the control and measurement of the output current of SAPF and load current harmonics. The proposed filter reduces harmonic distortion is within an acceptable range.

This study establishes a novel time-delay fractional SEIHR infectious disease model to investigate the effects of saturated incidence rates and time delays on different populations, including susceptibles, infected individuals, recovered individuals, and latent infected individuals. First, the existence and boundedness of the model’s solutions are verified, confirming its well-posedness. Subsequently, the existence of equilibria is analyzed, and the impact of parameter variations on the system is explored by examining the equilibria ϵ0 and ϵ∗, as well as the basic reproduction number R0. Additionally, the global dynamics of the equilibria are further analyzed using the Lyapunov method, while Hopf bifurcation theory is applied to explore the conditions under which the system shifts from stability to oscillatory behavior. Numerical simulations further validate the theoretical analysis, showing that time-delay effects significantly influence the system’s responsiveness and the rate of disease transmission. Moreover, when the time delay τ crosses the critical threshold τ0, the system exhibits periodic oscillations. By predicting periodic fluctuations and incorporating memory effects and persistent influences, we can better control epidemics, emphasizing the importance of time-delay adjustments and enhancing the system’s biological realism.

Effects of time and sampling location on concentrations of β-hydroxybutyric acid in dairy cows

A within host viral infection model with both virus to cell and cell to cell transmissions and three discrete delays using reverse transcriptase inhibitors is investigated. The effect of time delay on stability of the equilibria of the infection model has been studied. By choosing the immune delay as the bifurcation parameter, we establish the sufficient condition for local stability of chronic infection equilibrium, Hopf bifurcation occurs when the immune delay passes a critical value. The global stability analysis of the model is carried out in terms of the basic reproduction number $$R_0$$ . If $$R_0 < 1$$ , the infection-free (semi-trivial) equilibrium is globally stable for both ODE and DDE model; if $$R_0>1$$ , the chronic infection (positive) equilibrium exists and is globally stable for both ODE and DDE model. Numerical simulations are obtained to validate our analytical findings by varying the system parameter.

Telementoring allows a less experienced surgeon to benefit from an expert surgical consultation, reducing cost, travel, and the learning curve associated with new procedures. However, there are several technical limitations that affect practical applications. One potentially serious problem is the time delay that occurs any time data are transferred across long distances. To date, the effect of time delay on surgical performance has not been studied. A two-phase trial was designed to examine the effect of time delay on surgical performance. In the first phase, a series of tasks was performed, and the numbers of robotic movements required for completion was counted. Programmed incremental time delays were made in audiovisual acquisition and robotic controls. The number of errors made while performing each task at various time delay intervals was noted. In the second phase, a remote surgeon in Baltimore performed the tasks 9000 miles away in Singapore. The number of errors made was recorded. As the time delay increased, the number of operator errors increased. The accuracy needed to perform remote robotic procedures was diminished as the time delay increased. A learning curve did exist for each task, but as the time delay interval increased, it took longer to complete the task. Time delay does affect surgical performance. There is an acceptable delay of <700 msec in which surgeons can compensate for this phenomenon. Clinical studies will be needed to evaluate the true impact of time delay.

The hardware used for software implementation on a physical system introduces uncertainty into the controller. If neglected during design, this uncertainty can lead to poor controller performance, resulting in significant design and verification iterations. In this work, the effect of sampling time, quantization, and fixed-point computation are directly accounted for in the control design. Sampling time is compensated for by a discrete-time controller. A generic methodology is developed for modeling the worst-case scenario effect of quantization and fixed-point computation on the control commands. The cold-start emission control problem is used as a case study, and a discrete-time sliding surface controller is developed. Verification is performed to ensure the estimated worst-case scenario uncertainty bounds are accurate. The bounds are incorporated into a modified version of the control laws. During simulation the modified controller demonstrates significant reduction in tracking error in the presence of hardware imprecisions.

Seasonal variation in testis size, gonadotrophin secretion and pituitary responsiveness to GnRH in rams of two breeds differing in time of onset of the breeding season

In this paper, a general nonlinear Chikungunya virus (CHIKV) dynamics model is formulated and analyzed. We assume that, the production, removal and proliferation rates of all compartments as well as the incidence rate of infection are modeled by general nonlinear functions that satisfy a set of reasonable conditions. The model is incorporated by two types of discrete time delays. We prove the nonnegativity and boundedness of the solutions of the model. We established the global stability of the steady states of the model by constructing suitable Lyapunov functionals. The numerical simulations are performed to illustrate our theoretical results. The effect of time delay on the virus dynamics is studied.

Variability of biofouling communities on fish cage nets: A 1-year field study at a Norwegian salmon farm

Synchronization and firing patterns of neurons play a significant role in neural signal encoding and transduction of information processing of nervous systems. For real neurons, the signal transmission delay is inherent due to finite propagation speed of action potentials through axons, as well as both dendritic and synaptic processing. Time delay is universal and inevitable, and results in complex dynamical behavior, such as multistability and bifurcations leading to chaos. In this chapter, the authors provide a survey of some recent developments on the effects of time delay on synchronization and firing patterns in coupled neuronal systems. Basic concepts of firing patterns of neurons and complete and phase synchronization of oscillators are introduced. Synchronization and firing patterns in electrically coupled neurons as well as inhibitory and excitatory delayed synapses with time delay are presented. Delay effects on spatiotemporal dynamics of coupled neuronal activities are discussed. Some well-known neuron models are also included for benefit of the readers.