Dead Time Model Research Articles

Paralyzable and non-paralyzable dead-time corrections for the neutron detectors of the KUCA facility driven by external neutron sources

ABSTRACT This study examines dead-time corrections for the neutron detectors of the Kyoto University Critical Assembly (KUCA) driven by a stationary or pulsed external neutron sources. For the stationary (californium) external neutron source, the MCNP simulations producing the detector timestamps and the MATLAB scripts correcting for the dead-time effect have been validated with analytical formulae from literature. For the pulsed external neutron source (produced by a proton accelerator), the experimental detector timestamps have been corrected for the dead-time effect using the aforementioned MATLAB scripts utilizing the backward extrapolation method and the paralyzable or non-paralyzable dead-time model. Then, the keff obtained from the dead-time corrected timestamps has been corrected for the detector position and type. Finally, the corrected experimental keff has been compared with the one obtained from MCNP criticality simulations.

Dead-time correction for spectroscopic photon-counting pixel detectors.

Modern photon-counting pixel detectors have enabled a revolution in applications at synchrotron light sources and beyond in the last decade. One of the limitations of the current detectors is their reduced counting linearity or even paralysis at high counting rates, due to dead-time which results in photon pile-up. Existing dead-time and pile-up models fail to reproduce the complexity of dead-time effects on photon-counting, resulting in empirical calibrations for particular detectors at best, imprecise linearization methods, or no linearization. This problem will increase in the future as many synchrotron light sources plan significant brilliance upgrades and free-electron lasers plan moving to a quasi-continuous operation mode. Presented here are the first models that use the actual behavior of the analog pre-amplifiers in spectroscopic photon-counting pixel detectors with constant current discharge (e.g. the Medipix and CPix families of detectors) to deduce more accurate analytical models and optimal linearization methods. In particular, for detectors with at least two counters per pixel, the need for calibration, or previous knowledge of the detector and beam parameters (dead-time, integration time, large sets of synchrotron filling patterns), is completely eliminated. This is summarized in several models of increasing complexity and accuracy. Finally, a general empirical approach is presented, applicable to any particular cases where the analytical approach is not sufficiently precise.

Parameter Identification of Delayed Under-Damped Systems Using On-Line Relay Autotuning

In this brief, an online relay autotuning scheme for parameter estimation of a class of time delayed systems is presented. The identification method comprises a relay and a proportional-derivative (PD) controller which helps in yielding sustained oscillation or limit cycles at the system output around the user defined setpoint. Along with the relay and controller parameters, the important limit cycle information are measured and further substituted in the derived set of mathematical expressions required for the estimation of system unknown dynamic in the form of under-damped second-order plus dead-time (SOPDT) model. Simulations of well known model dynamics from the literature are considered to illustrate the efficacy of the proposed scheme in presence of noise and static load disturbance. For the real-time validation of the proposed scheme, a pipe-line water pressure control system is identified as under-damped SOPDT transfer function model.

Observer-Based Predictive Vector-Resonant Current Control of Permanent Magnet Synchronous Machines

In this paper, an observer-based predictive vector-resonant current control strategy for permanent magnet synchronous machines is proposed, which can effectively improve system performance in case of disturbances caused by converter dead-time effect and model mismatch. Taking the external periodic, nonperiodic disturbances, and system delay into consideration, the system model in discrete-time domain is built. This model is then embedded with vector-resonant scheme with the aim of suppressing periodic disturbances resulting from converter dead-time effect and rotor flux harmonics. An extended-state observer is constructed in order to predict system behavior and to estimate the nonperiodic disturbances caused by parameter variations. The overall control law is then derived on basis of cost function minimization. With the proposed method adopted, the undesired anomalous peak around the resonant frequency in the frequency responses can be avoided. Moreover, enhanced transient performances and wider stability margin can also be obtained. The effectiveness and advantages of the proposed strategy are verified by frequency-domain analysis and comparative experimental studies.

Research on Improved Auto-Tuning of a PID Controller Based on Phase Angle Margin

In order to realize fast and efficient tuning of the proportional-integral-derivative (PID) controller parameters in thermal power plants, this paper thoroughly analyzes the problems existing in the relay characteristic method based on the phase angle margin auto-tuning method (PM method), and proposes an improved PM auto-tuning method for the most common first order plus dead time (FOPDT) model in practical engineering applications. The improved algorithm proposes the design of the target phase angle margin and the method of plant identification. Then, the optimization algorithm is used to calculate the correction coefficient of the setting formula to obtain the PID controller parameters that minimize the integrated time absolute error (ITAE) index of the control system. Finally, through the auto-tuning experiment on the generalized control model of the main steam temperature system in thermal power plants, it is verified that the improved algorithm is superior to the traditional PM method and Ziegler–Nichols method (Z-N method), and can obtain a fast and stable control performance.

Performance Evaluation of Nonlinear PI Controller on the Laboratory Type Spherical Tank Process

In this paper, the implementation of Nonlinear PI controller based on error square type is designed and adopted to control of level in a spherical tank process. By use of black box model the system is found as a First order plus Dead Time model (FOPDT). Then the controller tuning strategies has been adopted namely Direct synthesis (IMC), Skogestad (IMC PI), and Nonlinear PI (error square type) tuning. Among all the three controllers tuning the error square type based Nonlinear PI tuning method shows better control performance than the other two controller tuning in terms of performance indices like Integral Square Error (ISE), Integral Absolute Error (IAE) and Time domain specifications

Identification and control design for a class of non-minimum phase dead-time systems based on fractional-order Smith predictor and genetic algorithm technique

The design of Smith predictor (SP) controllers has been largely investigated in the literature to solve fractional-order dead-time systems control problems; this is due to its robustness in presence of modeling uncertainties and measurement noises. In this paper, a new SP control strategy is proposed for a special class of non-minimum phase dead-time systems. This controller can be designed only by imposing a new fractional-order model and new robust controller in the modeling and synthesis stages. A fractional multi-low-order dead-time (FMLODT) model is accordingly proposed in the SP modeling stage, where an actual process behavior is well ensured by high model accuracy. The proposed fractional model parameters are identified by an adequate optimization tool based genetic algorithm. Furthermore, a robust fractional order controller based FMLODT model is synthesized, its structure is analytically determined through a three terms fractional-order reference (TTFOR) model. The proposed fractional controller parameters are tuned according to some guidelines available in the literature. Thus, two main contributions are proposed in this work: the first one consists of introducing a general framework for the SP modeling scheme. This is ensured by the proposed FMLODT model, which enhances significantly the modeling accuracy compared to the existing models in the literature. The second innovation consists of proposing a general framework for the controller design step in the SP scheme. This is guaranteed by the proposed TTFOR model, which improves significantly the trade-off between the two contradictory goals: performance and robustness. To highlight the proposed strategy of good performances, both the new fractional order model and the classical controller are applied on the minimum phase dead-time systems. The obtained simulation results illustrate the performance and improvement of the proposed model comparatively to preceding works.

Model Based Analysis of Error Recursive Method for Self-Regulation Processes

Objectives: To improve the closed loop response by means of reducing the rise time, settling time and overshoots by implementing a suitable method. Methods/Statistical Analysis: Three different higher order transfer functions has been considered for analysis and are approximated to First Order Plus Dead Time models (FOPDT) using process reaction curve method by applying unit step change. Particle Swarm Optimization technique; and Internal Model Control (IMC) method are used to tune Proportional, Integral and Derivative (PID) control values. Findings: The new method, Error Recursive Reduction Computational (ERRC) method has been tested on higher order systems. The proposed method ERRC is merged with PSO based PID and IMC based PID controllers in hybrid form to form new control structure. Comparative analysis was carried out between with ERRC and without ERRC. Application/Improvements: It can be applied to feedback control system i.e., feedback control applications are highly influenced in chemical, manufacturing automobile industries and etc. Keywords: Higher Orders Systems, Modeling, PID Control, Recursive Analysis, Self Regulating Process

Sliding Mode Control Based on Internal Model for a Non-minimum phase Buck and Boost Converter

This work presents the application of different schemes to control a non-minimum phase Buck-Boost converter. Three control schemes are used. The first controller presented is a PI controller, the second one is Sliding Mode Control and the third one is a combination of two control schemes, Internal Model Control and Sliding Mode Control. The controllers are designed from a Right-Half Plane Zero (RHPZ) reduced order model. The RHPZ model is converted, using Taylor approximation, in a First Order Plus Dead Time (FOPDT) model and after that, the controllers are obtained. The performance of the SMC-IMC is compared against to a PI controller and a SMC. The simulation results show that SMC-IMC improves the converter response, reducing the chattering and presenting better robustness for load changes

Linear active disturbance rejection control for a raceway photobioreactor

In this paper, the problem of pH regulation in photobioreactors has been addressed by using a robust Proportional–Integral controller in combination with a linear active disturbance rejection approach. This formulation is based on the Generalized Proportional–Integral (GPI) observers design to estimate online external disturbances and non-modeled dynamics. The methodology uses a model reference optimization procedure with tracking and regulatory target closed-loop transfer functions for first-order plus dead-time models. The proposed controller is validated on a full-scale Raceway photobioreactor, whose results show a significant improvement in the accuracy of pH regulation and, consequently, a positive influence on biomass production. Moreover, a classical feedforward approach has been used for comparison purposes. The performance of the robust technique is evaluated with different indexes, whose results confirm the good performance of the proposed active disturbance rejection control scheme.

Model Reference Based Tuning for Fractional-Order 2DoF PI Controllers with a Robustness Consideration

Abstract The paper proposes the tuning of a two-degree-of-freedom (2-DoF) fractional-order PI (FOPI2) controller based on the Model Reference Robust Tuning (MoReRT) methodology presented by Alfaro and Vilanova (2012, 2016). The tuning rules are optimized for a first order plus dead time (FOPDT) model and for an over-damped servo and regulatory closed-loop target responses. Moreover, the design takes into account a robustness consideration, expressed as a maximum sensitivity target value of 2.0, dealing with the performance/robustness trade-off of the closed-loop control system. A set of controller tuning equations (four parameters) is provided for FOPDT models with normalized dead-times from 0.1 to 2.0. Also, an evaluation of the control system performance and robustness is presented, in order to show the effectiveness of the proposed tuning method, that is compared with other tuning rules.

A linear algebra controller based on reduced order models applied to trajectory tracking for mobile robots: an experimental validation

A linear algebra controller (LACr) based on an empirical linear model of the system is presented in this paper. The controller design is based on a first order plus dead time (FOPDT) model and can be tuned using the characteristic parameters obtained from the reaction curve. In previous studies, the versatility of this proposed controller was tested by simulations, proving be an alternative to control many kinds of processes. In this paper, the proposed controller is implemented for trajectory tracking using a real mobile robot platform. The performance results are compared against a PI controller using the ISE performance index to measure it.

Modelling and Nonlinear Model Predictive Control of a Twin Screw Feeder

In this work, a dynamic model of a twin screw feeder, for continuous tablet manufacturing, has been developed. In particular, a First Order Plus Dead Time (FOPDT) model has been suggested. The delayed dynamics depends on operating conditions, equipment design and physical properties of the bulk solid. Model parameters are estimated by fitting the model to experimental data. Due to the nonlinear input-output relationships and the time delays involved, a Nonlinear Model Predictive Control (NMPC) is investigated to maintain an accurate mass flow rate, with the ultimate goal to improve product homogeneity in an inherently complex process. The performance of the designed control system is found to be satisfactory in a wide operating range and its potential use in a continuous manufacturing process is worth being investigated in the future.

Design and experimental realization of a robust decentralized PI controller for a coupled tank system

This paper presents design and realization of a robust decentralized PI controller for regulating the level of a coupled tank system. The proposed controller is designed based on a predefined reference transfer function model in which we adopt a frequency matching of actual and reference models. Realization of control algorithms for a multivariable system is often complicated owing to uncertainties in the process dynamics. In this paper, initially a frequency response fitting model reduction technique is adopted to obtain a First Order Plus Dead Time (FOPDT) model of each higher order decoupled subsystem. Further, using the obtained reduced order model, the proposed robust decentralized PI controller is designed. The stability and performance of the proposed controller are verified by considering multiplicative input and output uncertainties. The performance of the proposed robust decentralized controller has been compared with that of a decentralized PI controller. To validate the performance of the proposed control approach, real-time experimentation is pursed on a Feedback Instrument manufactured coupled tank system.

Adopting pade approximation for first order plus dead time models for blending process

Dead-time is common to real time processes and occurs when the process variable doesn’t acknowledge to any changes in the set point. Existence of dead time in the systems poses a challenge to control and stabilize, especially in a control feedback loop. Padé approximation provides a determinate approximation of the dead time in the continuous process systems, which can be utilized in the further simulations of equivalent First Order plus Dead Time Models. However, the standard Padé approximation with the same numerator- denominator derivative power, exhibits a jolt at time t=0. This gives an inaccurate approximation of the dead time. To avoid this phenomenon, increasing orders of Padé approximation is applied. In the following manuscript, equivalent First Order plus Dead-Time models of two blending systems of orders four and seven are analysed for the same. As the orders of the Padé approximation increases, the accuracy of the response also increases. The oscillations are increased on a much smaller scale rather than having one big dip in the negative region (as observed in the first few orders of Padé approximation), and the approximation tries to synchronize with the desired response curve in the positive region. All the simulations are done in MATLAB.

Statistical Parameter Study of the Time Interval Distribution for Nonparalyzable, Paralyzable, and Hybrid Dead Time Models

A large dead time of a Geiger Mueller (GM) detector may cause a large count loss in radiation measurements and consequently may cause distortion of the Poisson statistic of radiation events into a new distribution. The new distribution will have different statistical parameters compared to the original distribution. Therefore, the variance, skewness, and excess kurtosis in association with the observed count rate of the time interval distribution for well-known nonparalyzable, paralyzable, and nonparalyzable-paralyzable hybrid dead time models of a Geiger Mueller detector were studied using Monte Carlo simulation (GMSIM). These parameters were then compared with the statistical parameters of a perfect detector to observe the change in the distribution. The results show that the behaviors of the statistical parameters for the three dead time models were different. The values of the skewness and the excess kurtosis of the nonparalyzable model are equal or very close to those of the perfect detector, which are ≅2 for skewness, and ≅6 for excess kurtosis, while the statistical parameters in the paralyzable and hybrid model obtain minimum values that occur around the maximum observed count rates. The different trends of the three models resulting from the GMSIM simulation can be used to distinguish the dead time behavior of a GM counter; i.e. whether the GM counter can be described best by using the nonparalyzable, paralyzable, or hybrid model. In a future study, these statistical parameters need to be analyzed further to determine the possibility of using them to determine a dead time for each model, particularly for paralyzable and hybrid models.

Multivariable model predictive control design of reactive distillation column for Dimethyl Ether production

Dimethyl ether (DME) as an alternative clean energy has attracted a growing attention in the recent years. DME production via reactive distillation has potential for capital cost and energy requirement savings. However, combination of reaction and distillation on a single column makes reactive distillation process a very complex multivariable system with high non-linearity of process and strong interaction between process variables. This study investigates a multivariable model predictive control (MPC) based on two-point temperature control strategy for the DME reactive distillation column to maintain the purities of both product streams. The process model is estimated by a first order plus dead time model. The DME and water purity is maintained by controlling a stage temperature in rectifying and stripping section, respectively. The result shows that the model predictive controller performed faster responses compared to conventional PI controller that are showed by the smaller ISE values. In addition, the MPC controller is able to handle the loop interactions well.

Model Predictive Control Based on System Re-Identification (MPC-SRI) to Control Bio-H2 Production from Biomass

Compressors and a steam reformer are the important units in biohydrogen from biomass plant. The compressors are useful for achieving high-pressure operating conditions while the steam reformer is the main process to produce H2 gas. To control them, in this research used a model predictive control (MPC) expected to have better controller performance than conventional controllers. Because of the explicit model empowerment in MPC, obtaining a better model is the main objective before employing MPC. The common way to get the empirical model is through the identification system, so that obtained a first-order plus dead-time (FOPDT) model. This study has already improved that way since used the system re-identification (SRI) based on closed loop mode. Based on this method the results of the compressor pressure control and temperature control of steam reformer were that MPC based on system re-identification (MPC-SRI) has better performance than MPC without system re-identification (MPCWSRI) and the proportional-integral (PI) controller, by % improvement of 73% against MPCWSRI and 75% against the PI controller.

Limits of a simplified controller design based on integral plus dead time models

This article presents design and evaluation of filtered proportional–integral controllers and filtered Smith predictor–inspired constrained dead time compensators. Both are based on the integral plus dead time and on the first-order time delayed plant models. They are compared as for tuning simplicity, robustness and noise attenuation. Such a comparison, which presents a robustness test regarding the importance of the internal plant feedback approximation, may be carried out by performance measures built on deviations of the input and output transient responses from their ideal shapes. When combined with integral of absolute error measures of both solution types with the disturbance responses set as nearly equivalent, we can see that the filtered Smith predictor setpoint responses may be significantly faster than the filtered proportional–integral controller responses, more robust and, using higher-order filters, also sufficiently smooth. Furthermore, tuning of the possibly higher-order filters for filtered Smith predictor is simpler. Its overall design is more transparent and straightforward with respect to the control constraints, where the filtered Smith predictor requires some additional anti-windup measures.

Energy-dependent dead-time correction in digital pulse processors applied to silicon drift detector's X-ray spectra.

Dead-time effects in X-ray spectra taken with a digital pulse processor and a silicon drift detector were investigated when the number of events at the low-energy end of the spectrum was more than half of the total, at counting rates up to 56 kHz. It was found that dead-time losses in the spectra are energy dependent and an analytical correction for this effect, which takes into account pulse pile-up, is proposed. This and the usual models have been applied to experimental measurements, evaluating the dead-time fraction either from the calculations or using the value given by the detector acquisition system. The energy-dependent dead-time model proposed fits accurately the experimental energy spectra in the range of counting rates explored in this work. A selection chart of the simplest mathematical model able to correct the pulse-height distribution according to counting rate and energy spectrum characteristics isincluded.