Excellence in Research and Innovation for Humanity

International Science Index

Commenced in January 1999 Frequency: Monthly Edition: International Abstract Count: 48534

Electrical and Information Engineering

37
78538
Numerical Simulations on Feasibility of Stochastic Model Predictive Control for Linear Discrete-Time Systems with Random Dither Quantization
Abstract:
The random dither quantization method enables us to achieve much better performance than the simple uniform quantization method for the design of quantized control systems. Motivated by this fact, the stochastic model predictive control method in which a performance index is minimized subject to probabilistic constraints imposed on the state variables of systems has been proposed for linear feedback control systems with random dither quantization. In other words, a method for solving optimal control problems subject to probabilistic state constraints for linear discrete-time control systems with random dither quantization has been already established. To our best knowledge, however, the feasibility of such a kind of optimal control problems has not yet been studied. Our objective in this paper is to investigate the feasibility of stochastic model predictive control problems for linear discrete-time control systems with random dither quantization. To this end, we provide the results of numerical simulations that verify the feasibility of stochastic model predictive control problems for linear discrete-time control systems with random dither quantization.
Digital Article Identifier (DAI):
36
78360
Application of Artificial Neural Networks to Adaptive Speed Control under ARDUINO
Abstract:
Nowadays, adaptive control schemes are being used when model based control schemes are applied in presence of uncertainty and model mismatches. Artificial neural networks have been employed both in modelling and control of non-linear dynamic systems with unknown dynamics. In fact, these are powerful tools to solve this control problem when only input-output operational data are available. A neural network controller under SIMULINK together with the ARDUINO hardware platform has been used to perform real-time speed control of a computer case fan. Comparison of performance with a PID controller has also been presented in order to show the efficacy of neural control under different command signals tracking and also when disturbance signals are present in the speed control loops.
Digital Article Identifier (DAI):
35
78106
Stability Analysis of Hossack Suspension Systems in High Performance Motorcycles
Abstract:
A motorcycle's front end links the front wheel to the motorcycle's chassis and has two main functions: the front wheel suspension and the vehicle steering. Up to this date, several suspension systems have been developed in order to achieve the best possible front end behavior, being the telescopic fork the most common one and already subjected to several years of study in terms of its kinematics, dynamics, stability and control. A motorcycle telescopic fork suspension model consists of a couple of outer tubes which contain the suspension components (coil springs and dampers) internally and two inner tubes which slide into the outer ones allowing the suspension travel. The outer tubes are attached to the frame through two triple trees which connect the front end to the main frame through the steering bearings and allow the front wheel to turn about the steering axis. This system keeps the front wheel's displacement in a straight line parallel to the steering axis. However, there exist alternative suspension designs that allow different trajectories of the front wheel with the suspension travel. In this contribution, the authors investigate an alternative front suspension system (Hossack suspension) and its influence on the motorcycle nonlinear dynamics to identify and reduce stability risks that a new suspension systems may introduce in the motorcycle dynamics. Based on an existing high-fidelity motorcycle mathematical model, the front end geometry is modified to accommodate a Hossack suspension system. It is characterized by a double wishbone design that varies the front end geometry on certain maneuverings and, consequently, the machine's behavior/response. It consists of a double wishbone structure directly attached to the chassis. In here, the kinematics of this system and its impact on the motorcycle performance/stability are analyzed and compared to the well known telescopic fork suspension system. The framework of this research is the mathematical modelling and numerical simulation. Full stability analyses are performed in order to understand how the motorcycle dynamics may be affected by the newly introduced front end design. This study is carried out by a combination of nonlinear dynamical simulation and root-loci methods. A modal analysis is performed in order to get a deeper understanding of the different modes of oscillation and how the Hossack suspension system affects them. The results show that different kinematic designs of a double wishbone suspension systems do not modify the general motorcycle's stability. The normal modes properties remain unaffected by the new geometrical configurations. However, these normal modes differ from one suspension system to the other. It is seen that the normal modes behaviour depends on various important dynamic parameters, such as the front frame flexibility, the steering damping coefficient and the centre of mass location.
Digital Article Identifier (DAI):
34
76853
Attitude Stabilization of Satellites Using Random Dither Quantization
Abstract:
Recently, the effectiveness of random dither quantization method for linear feedback control systems has been shown in several papers. However, the random dither quantization method has not yet been applied to nonlinear feedback control systems. The objective of this paper is to verify the effectiveness of random dither quantization method for nonlinear feedback control systems. For this purpose, we consider the attitude stabilization problem of satellites using discrete-level actuators. Namely, this paper provides a control method based on the random dither quantization method for stabilizing the attitude of satellites using discrete-level actuators.
Digital Article Identifier (DAI):
33
76606
On Fault Diagnosis of Asynchronous Sequential Machines with Parallel Composition
Authors:
Abstract:
Fault diagnosis of composite asynchronous sequential machines with parallel composition is addressed in this paper. An adversarial input can infiltrate one of two submachines comprising the composite asynchronous machine, causing an unauthorized state transition. The objective is to characterize the condition under which the controller can diagnose any fault occurrence. Two control configurations, state feedback and output feedback, are considered in this paper. In the case of output feedback, the exact estimation of the state is impossible since the current state is inaccessible and the output feedback is given as the form of burst. A simple example is provided to demonstrate the proposed methodology.
Digital Article Identifier (DAI):
32
75656
Slip Suppression Sliding Mode Control with Various Chattering Functions
Abstract:
This study presents performance analysis results of SMC (Sliding mode control) with changing the chattering functions applied to slip suppression problem of electric vehicles (EVs). In SMC, chattering phenomenon always occurs through high frequency switching of the control inputs. It is undesirable phenomenon and degrade the control performance, since it causes the oscillations of the control inputs. Several studies have been conducted on this problem by introducing some general saturation function. However, study about whether saturation function was really best and the performance analysis when using the other functions, weren’t being done so much. Therefore, in this paper, several candidate functions for SMC are selected and control performance of candidate functions is analyzed. In the analysis, evaluation function based on the trade-off between slip suppression performance and chattering reduction performance is proposed. The analyses are conducted in several numerical simulations of slip suppression problem of EVs. Then, we can see that there is no difference of employed candidate functions in chattering reduction performance. On the other hand, in slip suppression performance, the saturation function is excellent overall. So, we conclude the saturation function is most suitable for slip suppression sliding mode control.
Digital Article Identifier (DAI):
31
75062
Using Optimal Control Method to Investigate the Stability and Transparency of a Nonlinear Teleoperation System with Time Varying Delay
Abstract:
In this paper, a new structure for teleoperation systems with time varying delay has been modeled and proposed. A random time varying the delay of up to 150 msec is simulated in teleoperation channel of both masters to slave and vice versa. The system stability and transparency have been investigated, comparing the result of a PID controller and an optimal controller on each master and slave sub-systems separately. The controllers have been designed in slave subsystem for reducing position errors between master and slave, and another controller has been designed in the master subsystem to establish stability, transparency and force tracking. Results have been compared together. The results showed PID controller is appropriate in position tracking, but force response oscillates in contact with the environment. We showed the optimal control established position tracking properly. Also, force tracking is achieved in this controller appropriately.
Digital Article Identifier (DAI):
30
74319
Number of Parametrization of Discrete-Time Systems without Unit-Delay Element: Single-Input Single-Output Case
Abstract:
In this paper, we consider the parametrization of the discrete-time systems without the unit-delay element within the framework of the factorization approach. In the parametrization, we investigate the number of required parameters. We consider single-input single-output systems in this paper. By the investigation, we find, on the discrete-time systems without the unit-delay element, three cases that are (1) there exist plants which require only one parameter and (2) two parameters, and (3) the number of parameters is at most three.
Digital Article Identifier (DAI):
29
72983
A Survey on Linear Time Invariant Multivariable Positive Real Systems
Abstract:
Positive realness as the most important property of driving point impedance of passive electrical networks appears in the control systems stability theory in 1960’s. There are three important subsets of positive real (PR) systems are introduced by researchers, that is, loos-less positive real (LLPR) systems, weakly strictly positive real (WSPR) systems and strictly positive real (SPR) systems. In this paper, definitions, properties, lemmas, and theorems related to family of positive real systems are summarized. Properties in both frequency domain and state space representation of system are explained. Also, several illustrative examples are presented.
Digital Article Identifier (DAI):
28
72768
A Survey on Routh-Hurwitz Stability Criterion
Abstract:
Routh-Hurwitz stability criterion is a powerful approach to determine stability of linear time invariant systems. On the other hand, applying this criterion to characteristic equation of a system, whose stability or marginal stability can be determined. Although the command roots (.) of MATLAB software can be easily used to determine the roots of a polynomial, the characteristic equation of closed loop system usually includes parameters, so software cannot handle it; however, Routh-Hurwitz stability criterion results the region of parameter changes where the stability is guaranteed. Moreover, this criterion has been extended to characterize the stability of interval polynomials as well as fractional-order polynomials. Furthermore, it can help us to design stable and minimum-phase controllers. In this paper, theory and application of this criterion will be reviewed. Also, several illustrative examples are given.
Digital Article Identifier (DAI):
27
71683
Sampled-Data Model Predictive Tracking Control for Mobile Robot
Abstract:
In this paper, a sampled-data model predictive tracking control method is presented for mobile robots which is modeled as constrained continuous-time linear parameter varying (LPV) systems. The presented sampled-data predictive controller is designed by linear matrix inequality approach. Based on the input delay approach, a controller design condition is derived by constructing a new Lyapunov function. Finally, a numerical example is given to demonstrate the effectiveness of the presented method.
Digital Article Identifier (DAI):
26
71394
Ziegler Nichols Based Integral Proportional Controller for Superheated Steam Temperature Control System
Abstract:
In this paper, Integral Proportional (I-P) controller is employed for superheated steam temperature control system. The Ziegler-Nichols (Z-N) method is used for the tuning of I-P controller. The performance analysis of Z-N based I-P controller is assessed on superheated steam system of 500-MW boiler. The comparison of transient response parameters such as rise time, settling time, and overshoot is made with Z-N based Proportional Integral (PI) controller. It is observed from the results that Z-N based I-P controller completely eliminates the overshoot in the output response.
Digital Article Identifier (DAI):
25
70632
Identification of Wiener Model Using Iterative Schemes
Abstract:
This paper presents the iterative schemes based on Least square, Hierarchical Least Square and Stochastic Approximation Gradient method for the Identification of Wiener model with parametric structure. A gradient method is presented for the parameter estimation of wiener model with noise conditions based on the stochastic approximation. Simulation results are presented for the Wiener model structure with different static non-linear elements in the presence of colored noise to show the comparative analysis of the iterative methods. The stochastic gradient method shows improvement in the estimation performance and provides fast convergence of the parameters estimates.
Digital Article Identifier (DAI):
24
67799
Object-Oriented Multivariate Proportional-Integral-Derivative Control of Hydraulic Systems
Abstract:
This paper presents and discusses the application of the object-oriented modelling software SIMSCAPE to hydraulic systems, with particular reference to multivariable proportional-integral-derivative (PID) control. As a result, a particular modelling approach of a double cylinder-piston coupled system is proposed and motivated, and the SIMULINK based PID tuning tool has also been used to select the proper controller parameters. The paper demonstrates the usefulness of the object-oriented approach when both physical modelling and control are tackled.
Digital Article Identifier (DAI):
23
66187
Application of Fractional Model Predictive Control to Thermal System
Abstract:
The article presents an application of Fractional Model Predictive Control (FMPC) to a fractional order thermal system using Controlled Auto Regressive Integrated Moving Average (CARIMA) model obtained by discretization of a continuous fractional differential equation. Moreover, the output deviation approach is exploited to design the K -step ahead output predictor, and the corresponding control law is obtained by solving a quadratic cost function. Experiment results onto a thermal system are presented to emphasize the performances and the effectiveness of the proposed predictive controller.
Digital Article Identifier (DAI):
22
63970
Stochastic Model Predictive Control for Linear Discrete-Time Systems with Random Dither Quantization
Abstract:
Recently, feedback control systems using random dither quantizers have been proposed for linear discrete-time systems. However, the constraints imposed on state and control variables have not yet been taken into account for the design of feedback control systems with random dither quantization. Model predictive control is a kind of optimal feedback control in which control performance over a finite future is optimized with a performance index that has a moving initial and terminal time. An important advantage of model predictive control is its ability to handle constraints imposed on state and control variables. Based on the model predictive control approach, the objective of this paper is to present a control method that satisfies probabilistic state constraints for linear discrete-time feedback control systems with random dither quantization. In other words, this paper provides a method for solving the optimal control problems subject to probabilistic state constraints for linear discrete-time feedback control systems with random dither quantization.
Digital Article Identifier (DAI):
21
57966
An Adaptive Controller Method Based on Full-State Linear Model of Variable Cycle Engine
Abstract:
Due to the more variable geometry parameters of VCE (variable cycle aircraft engine), presents an adaptive controller method based on the full-state linear model of VCE and has simulated to solve the multivariate controller design problem of the whole flight envelops. First, analyzes the static and dynamic performances of bypass ratio and other state parameters caused by variable geometric components, and develops nonlinear component model of VCE. Then based on the component model, through small deviation linearization of main fuel (Wf), the area of tail nozzle throat (A8) and the angle of rear bypass ejector (A163), setting up multiple linear model which variable geometric parameters can be inputs. Second, designs the adaptive controllers for VCE linear models of different nominal points. Among them, considering of modeling uncertainties and external disturbances, derives the adaptive law by lyapunov function. The simulation results showed that, the adaptive controller method based on full-state linear model used the angle of rear bypass ejector as input and effectively solved the multivariate control problems of VCE. The performance of all nominal points could track the desired closed-loop reference instructions. The adjust time was less than 1.2s, and the system overshoot was less than 1%, at the same time, the errors of steady states were less than 0.5% and the dynamic tracking errors were less than 1%. In addition, the designed controller could effectively suppress interference and reached the desired commands with different external random noise signals.
Digital Article Identifier (DAI):
20
57964
Parameterized Lyapunov Function Based Robust Diagonal Dominance Pre-Compensator Design for Linear Parameter Varying Model
Abstract:
For dynamic decoupling of linear parameter varying system, a robust dominance pre-compensator design method is given. The parameterized pre-compensator design problem is converted into optimal problem constrained with parameterized linear matrix inequalities (PLMI); To solve this problem, firstly, this optimization problem is equivalently transformed into a new form with elimination of coupling relationship between parameterized Lyapunov function (PLF) and pre-compensator. Then the problem was reduced to a normal convex optimization problem with normal linear matrix inequalities (LMI) constraints on a newly constructed convex polyhedron. Moreover, a parameter scheduling pre-compensator was achieved, which satisfies robust performance and decoupling performances. Finally, the feasibility and validity of the robust diagonal dominance pre-compensator design method are verified by the numerical simulation of a turbofan engine PLPV model.
Digital Article Identifier (DAI):
19
54706
Chemical Reaction Algorithm for Expectation Maximization Clustering
Abstract:
Clustering is an intensive research for some years because of its multifaceted applications, such as biology, information retrieval, medicine, business and so on. The expectation maximization (EM) is a kind of algorithm framework in clustering methods, one of the ten algorithms of machine learning. Traditionally, optimization of objective function has been the standard approach in EM. Hence, research has investigated the utility of evolutionary computing and related techniques in the regard. Chemical Reaction Optimization (CRO) is a recently established method. So the property embedded in CRO is used to solve optimization problems. This paper presents an algorithm framework (EM-CRO) with modified CRO operators based on EM cluster problems. The hybrid algorithm is mainly to solve the problem of initial value sensitivity of the objective function optimization clustering algorithm. Our experiments mainly take the EM classic algorithm:k-means and fuzzy k-means as an example, through the CRO algorithm to optimize its initial value, get K-means-CRO and FKM-CRO algorithm. The experimental results of them show that there is improved efficiency for solving objective function optimization clustering problems.
Digital Article Identifier (DAI):
18
54425
GPU Based Real-Time Floating Object Detection System
Abstract:
A GPU-based floating object detection scheme is presented in this paper which is designed for floating mine detection tasks. This system uses contrast and motion information to eliminate as many false positives as possible while avoiding false negatives. The GPU computation platform is deployed to allow detecting objects in real-time. From the experimental results, it is shown that with certain configuration, the GPU-based scheme can speed up the computation up to one thousand times compared to the CPU-based scheme.
Digital Article Identifier (DAI):
17
52967
Technical and Economic Analysis Effects of Various Parameters on the Performance of Heat Recovery System on Gas Complex Turbo Generators
Abstract:
This paper deals with the technical and economic effects of various parameters on the performance of heat recovery system on gas complex turbo generator. Given the importance of this issue, that is the main goal of economic efficiency and reduces costs; this project has been implemented similar plans in which the target is the implementation of specific patterns. The project will also help us in the process of gas refineries and the actual efficiency of the process after adding a system to analyze the turbine and predict potential problems and fix them and take appropriate measures according to the results of simulation analysis and results of the process gain. The results of modeling and the effect of different parameters on this line, have been done using Thermo Flow.
Digital Article Identifier (DAI):
16
52454
Detection of Abnormal Process Behavior in Copper Solvent Extraction by Principal Component Analysis
Abstract:
Frequent measurements of product steam quality create a data overload that becomes more and more difficult to handle. In the current study, plant history data with multiple variables was successfully treated by principal component analysis to detect abnormal process behavior, particularly, in copper solvent extraction. The multivariate model is based on the concentration levels of main process metals recorded by the industrial on-stream x-ray fluorescence analyzer. After mean-centering and normalization of concentration data set, two-dimensional multivariate model under principal component analysis algorithm was constructed. Normal operating conditions were defined through control limits that were assigned to squared score values on x-axis and to residual values on y-axis. 80 percent of the data set were taken as the training set and the multivariate model was tested with the remaining 20 percent of data. Model testing showed successful application of control limits to detect abnormal behavior of copper solvent extraction process as early warnings. Compared to the conventional techniques of analyzing one variable at a time, the proposed model allows to detect on-line a process failure using information from all process variables simultaneously. Complex industrial equipment combined with advanced mathematical tools may be used for on-line monitoring both of process streams’ composition and final product quality. Defining normal operating conditions of the process supports reliable decision making in a process control room. Thus, industrial x-ray fluorescence analyzers equipped with integrated data processing toolbox allows more flexibility in copper plant operation. The additional multivariate process control and monitoring procedures are recommended to apply separately for the major components and for the impurities. Principal component analysis may be utilized not only in control of major elements’ content in process streams, but also for continuous monitoring of plant feed. The proposed approach has a potential in on-line instrumentation providing fast, robust and cheap application with automation abilities.
Digital Article Identifier (DAI):
15
51420
Experimenting with Error Performance of Systems Employing Pulse Shaping Filters on a Software-Defined-Radio Platform
Authors:
Abstract:
This paper presents experimental results on testing the symbol-error-rate (SER) performance of quadrature amplitude modulation (QAM) systems employing symmetric pulse-shaping square-root (SR) filters designed by minimizing the roughness function and by minimizing the peak-to-average power ratio (PAR). The device used in the experiments is the 'bladeRF' software-defined-radio platform. PAR is a well-known measurement, whereas the roughness function is a concept for measuring the jitter-induced interference. The experimental results show that the system employing minimum-roughness pulse-shaping SR filters outperforms the system employing minimum-PAR pulse-shaping SR filters in the sense of SER performance.
Digital Article Identifier (DAI):
14
50850
Bifurcation and Chaos of the Memristor Circuit
Abstract:
In this paper, a magnetron memristor model based on hyperbolic sine function is presented and the correctness proved by studying the trajectory of its voltage and current phase, and then a memristor chaotic system with the memristor model is presented. The phase trajectories and the bifurcation diagrams and Lyapunov exponent spectrum of the magnetron memristor system are plotted by numerical simulation, and the chaotic evolution with changing the parameters of the system is also given. The paper includes numerical simulations and mathematical model, which confirming that the system, has a wealth of dynamic behavior.
Digital Article Identifier (DAI):
13
50847
Digital Control Algorithm Based on Delta-Operator for High-Frequency DC-DC Switching Converters
Abstract:
In this paper, a digital control algorithm based on delta-operator is presented for high-frequency digitally-controlled DC-DC switching converters. The stability and the controlling accuracy of the DC-DC switching converters are improved by using the digital control algorithm based on delta-operator without increasing the hardware circuit scale. The design method of voltage compensator in delta-domain using PID (Proportion-Integration- Differentiation) control is given in this paper, and the simulation results based on Simulink platform are provided, which have verified the theoretical analysis results very well. It can be concluded that, the presented control algorithm based on delta-operator has better stability and controlling accuracy, and easier hardware implementation than the existed control algorithms based on z-operator, therefore it can be used for the voltage compensator design in high-frequency digitally- controlled DC-DC switching converters.
Digital Article Identifier (DAI):
12
50826
A High Time Resolution Digital Pulse Width Modulator Based on Field Programmable Gate Array’s Phase Locked Loop Megafunction
Abstract:
The digital pulse width modulator (DPWM) is the crucial building block for digitally-controlled DC-DC switching converter, which converts the digital duty ratio signal into its analog counterpart to control the power MOSFET transistors on or off. With the increase of switching frequency of digitally-controlled DC-DC converter, the DPWM with higher time resolution is required. In this paper, a 15-bits DPWM with three-level hybrid structure is presented; the first level is composed of a7-bits counter and a comparator, the second one is a 5-bits delay line, and the third one is a 3-bits digital dither. The presented DPWM is designed and implemented using the PLL megafunction of FPGA (Field Programmable Gate Arrays), and the required frequency of clock signal is 128 times of switching frequency. The simulation results show that, for the switching frequency of 2 MHz, a DPWM which has the time resolution of 15 ps is achieved using a maximum clock frequency of 256MHz. The designed DPWM in this paper is especially useful for high-frequency digitally-controlled DC-DC switching converters.
Digital Article Identifier (DAI):
11
48722
Hybrid Subspace Approach for Time Delay Estimation in MIMO Systems
Abstract:
In this paper, we present a hybrid subspace approach for Time Delay Estimation (TDE) in multivariable systems. While several methods have been proposed for time delay estimation in SISO systems, delay estimation in MIMO systems were always a big challenge. In these systems the existing TDE methods have significant limitations because most of procedures are just based on system response estimation or correlation analysis. We introduce a new hybrid method for TDE in MIMO systems based on subspace identification and explicit output error method; and compare its performance with previously introduced procedures in presence of different noise levels and in a statistical manner. Then the best method is selected with multi objective decision making technique. It is shown that the performance of new approach is much better than the existing methods, even in low signal-to-noise conditions.
Digital Article Identifier (DAI):
10
46920
Assessing Effects of an Intervention on Bottle-Weaning and Reducing Daily Milk Intake from Bottles in Toddlers Using Two-Part Random Effects Models
Authors:
Abstract:
Two-part random effects models have been used to fit semi-continuous longitudinal data where the response variable has a point mass at 0 and a continuous right-skewed distribution for positive values. We review methods proposed in the literature for analyzing data with excess zeros. A two-part logit-log-normal random effects model, a two-part logit-truncated normal random effects model, a two-part logit-gamma random effects model, and a two-part logit-skew normal random effects model were used to examine effects of a bottle-weaning intervention on reducing bottle use and daily milk intake from bottles in toddlers aged 11 to 13 months in a randomized controlled trial. We show in all four two-part models that the intervention promoted bottle-weaning and reduced daily milk intake from bottles in toddlers drinking from a bottle. We also show that there are no differences in model fit using either the logit link function or the probit link function for modeling the probability of bottle-weaning in all four models. Furthermore, prediction accuracy of the logit or probit link function is not sensitive to the distribution assumption on daily milk intake from bottles in toddlers not off bottles.
Digital Article Identifier (DAI):
9
35692
A Novel Combination Method for Computing the Importance Map of Image
Abstract:
The importance map is an image-based measure and is a core part of the resizing algorithm. Importance measures include image gradients, saliency and entropy, as well as high level cues such as face detectors, motion detectors and more. In this work we proposed a new method to calculate the importance map, the importance map is generated automatically using a novel combination of image edge density and Harel saliency measurement. Experiments of different type images demonstrate that our method effectively detects prominent areas can be used in image resizing applications to aware important areas while preserving image quality.
Digital Article Identifier (DAI):
8
34308
Dynamic Fault Diagnosis for Semi-Batch Reactor Under Closed-Loop Control via Independent RBFNN
Abstract:
In this paper, a new robust fault detection and isolation (FDI) scheme is developed to monitor a multivariable nonlinear chemical process called the Chylla-Haase polymerization reactor when it is under the cascade PI control. The scheme employs a radial basis function neural network (RBFNN) in an independent mode to model the process dynamics and using the weighted sum-squared prediction error as the residual. The recursive orthogonal Least Squares algorithm (ROLS) is employed to train the model to overcome the training difficulty of the independent mode of the network. Then, another RBFNN is used as a fault classifier to isolate faults from different features involved in the residual vector. The several actuator and sensor faults are simulated in a nonlinear simulation of the reactor in Simulink. The scheme is used to detect and isolate the faults on-line. The simulation results show the effectiveness of the scheme even the process is subjected to disturbances and uncertainties including significant changes in the monomer feed rate, fouling factor, impurity factor, ambient temperature and measurement noise. The simulation results are presented to illustrate the effectiveness and robustness of the proposed method.
Digital Article Identifier (DAI):