Hamid Khaloozadeh - Academia.edu (original) (raw)

Papers by Hamid Khaloozadeh

Research paper thumbnail of Identification and Fault Diagnosis of an Industrial Gas Turbine Using State-Space Methods

Advanced Materials Research, Nov 1, 2011

The objective of this paper is to identify, detect and isolate faults to an industrial gas turbin... more The objective of this paper is to identify, detect and isolate faults to an industrial gas turbine. The detection scheme is based on the generation of so-called "residuals" that are errors between estimated and measured variables of the process. A State-Space model is used for identification and some observer-based methods are used for residual generation, while for residual evaluation a neural network classifier for MLP is used. The proposed fault detection and isolation tool has been tested on a single-shaft industrial gas turbine simulator.

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Research paper thumbnail of Automotive radar data filtering approach for Adaptive Cruise Control systems

ABSTRACT

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Research paper thumbnail of Novel Approach for Nonlinear Maneuvering Target Tracking Based on Input Estimation

Applied Mechanics and Materials, Oct 1, 2011

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Research paper thumbnail of The tail mean–variance optimal portfolio selection under generalized skew-elliptical distribution

Insurance: Mathematics and Economics

Abstract In the insurance and financial markets, events of extreme losses happen in the tail of r... more Abstract In the insurance and financial markets, events of extreme losses happen in the tail of return distributions, and investors are sensitive to these losses. The Tail Mean–Variance (TMV) criterion focuses on the rare risk but large losses, and it has recently been used in financial management for portfolio selection. In this paper, the proposed TMV criterion is based on the two measures of risk, i.e., the Tail Conditional Expectation (TCE) and Tail Variance (TV) under Generalized Skew-Elliptical (GSE) distribution. We obtain an explicit solution with simple implementation and use a convex optimization approach for the TMV optimization problem under the GSE distribution. We also provide a practical example of a portfolio optimization problem using the proposed TMV criterion. The empirical results show that the optimal portfolio performance can be improved by controlling the tail variability of returns distribution.

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Research paper thumbnail of A Minimum Principle for Stochastic Optimal Control Problem with Interval Cost Function

Taiwanese Journal of Mathematics

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Research paper thumbnail of Modeling and Analysis of COVID-19 Spread: The Impacts of Nonpharmaceutical Protocols

Computational and Mathematical Methods in Medicine

In this study, the extended SEIR dynamical model is formulated to investigate the spread of coron... more In this study, the extended SEIR dynamical model is formulated to investigate the spread of coronavirus disease (COVID-19) via a special focus on contact with asymptomatic and self-isolated infected individuals. Furthermore, a mathematical analysis of the model, including positivity, boundedness, and local and global stability of the disease-free and endemic equilibrium points in terms of the basic reproduction number, is presented. The sensitivity analysis indicates that reducing the disease contact rate and the transmissibility factor related to asymptomatic individuals, along with increasing the quarantine/self-isolation rate and the contact-tracing process, from the view of flattening the curve for novel coronavirus, are crucial to the reduction in disease-related deaths.

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Research paper thumbnail of Stock Market Prediction-by-Prediction Based on Autoencoder Long Short-Term Memory Networks

2020 28th Iranian Conference on Electrical Engineering (ICEE)

This paper proposes a strategy for the stock market closing price prediction-by-prediction using ... more This paper proposes a strategy for the stock market closing price prediction-by-prediction using the autoencoder long short-term memory (AE-LSTM) networks. To integrate technical analysis with deep learning methods, technical indicators and oscillators are added to the raw dataset as features. The wavelet transformation is used as a noise-removal technique in the stock index. Anomaly detection in dataset is also performed through the z-score method. First, the autoencoder is trained to represent the data. Then, the encoder extracts feature and puts them into the LSTM network for predicting the closing price of the stock index. Afterwards, the system predicts subsequently based on the previous predictions. To evaluate the theoretical results, the proposed method is experimented on the standard and poor's 500 (S&P 500) stock market index through several simulation studies. To analyze the results, several performance criteria are used to compare the results with the generative adversarial network (GAN). The simulation studies are conducted to show the effectiveness of the proposed method in the Python environment, and the results show that the proposed prediction-by-prediction method outperforms GAN in terms of daily adjusted closing price prediction.

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Research paper thumbnail of Sensitivity analysis for evaluation of the effect of sensors error on the wind turbine variables using Monte Carlo simulation

IET Renewable Power Generation

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Research paper thumbnail of Mathematical Modeling of the Novel Coronavirus Pandemic in Iran: A Model With Vaccination

2022 8th International Conference on Control, Instrumentation and Automation (ICCIA), 2022

The novel coronavirus (COVID-19) is a major health threat caused by a virus called Severe Acute R... more The novel coronavirus (COVID-19) is a major health threat caused by a virus called Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). COVID-19 can cause acute respiratory illness, which is deadly in some people. Epidemio-logically, vaccination is the safest and most effective approach to gaining immunity and fighting against viral diseases. In this paper, the extended compartmental SEIR model, considering vaccination as a pharmaceutical intervention, to assess the behavior of the COVID-19 outbreak in Iran is proposed. Initially, some mathematical analysis of the model, including positivity, boundedness, the basic reproduction number, and herd immunity is investigated. In order to validate the proposed model, the biological parameters of the model are estimated based on real-confirmed cases in Iran at a specified interval of time. Finally, to examine the concept of flattening the curve of the disease outbreak, the impacts of physical distancing and vaccination on mitigating the burden of the epidemic are compared graphically.

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Research paper thumbnail of Suboptimal sliding manifold For nonlinear supply chain with time delay

Journal of Combinatorial Optimization, 2021

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Research paper thumbnail of Multi-Step-Ahead Prediction of Stock Price Using a New Architecture of Neural Networks

Modelling and forecasting Stock market is a challenging task for economists and engineers since i... more Modelling and forecasting Stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. This nonlinearity affects the efficiency of the price characteristics. Using an Artificial Neural Network (ANN) is a proper way to model this nonlinearity and it has been used successfully in onestep-ahead and multi-step-ahead prediction of different stocks prices. Several factors, such as input variables, preparing data sets, network architectures and training procedures, have huge impact on the accuracy of the neural network prediction. The purpose of this paper is to predict multi-step-ahead prices of the stock market and derive the method, based on Recurrent Neural Networks (RNN), Real-Time Recurrent Learning (RTRL) networks and Nonlinear Autoregressive model process with exogenous input (NARX). This model is trained and tested by Tehran Securities Exchange data.

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Research paper thumbnail of Distributed Estimation and Sensor Selection in Wireless Sensor Network in the Presence of State-Dependent Noise

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Research paper thumbnail of Forecasting the Iranian Tax Revenues: An Application of Nonlinear Models

Iranian National Tax Administration, 2008

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Research paper thumbnail of Analyzing the Advantage of Combination of Density Forecasts in Tehran Stock Exchange

Today, stock market plays a key role in the economy of any country and is considered as one of th... more Today, stock market plays a key role in the economy of any country and is considered as one of the growth indicators of any economy. Gaining the skills of gathering and analyzing data simultaneously, as well as using this knowledge in economic investigations, make time and precision factors to be the drawcard of any investor in competition with others. Therefore, having a predictive approach with the lowest degree of error will lead to smarter management of resources. Due to the complex and stochastic nature of the stock market, conventional forecasting approaches in this field have usually faced serious challenges, most notably losing the robustness when the data type changed over time. Moreover, by focusing on point forecasting, some useful statistical information about the objective random variable has been ignored inadvertently, undermining the prediction efficiency. The focus of this study is on density forecasting models which, unlike point forecasting, contain a description o...

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Research paper thumbnail of Unscented Kalman Filter in Gas pipeline leakage magnitude estimation and localization

2019 6th International Conference on Control, Instrumentation and Automation (ICCIA), 2019

A model-based real-time transient modeling estimation process is proposed in a gas transmission l... more A model-based real-time transient modeling estimation process is proposed in a gas transmission line with the purpose of leak detection. The objective of this paper is aimed at determining the effectiveness of filtering techniques in leak detection and localization in a pipeline network through a synthetic pipe simulation. A model for a straight pipeline without any branch has been derived from Momentum and continuity equations and discretized based on the method of characteristics. In the next step by assuming proper boundary conditions, state-space representation of the system has been developed and stability and observability conditions have been explored. The unscented Kalman Filter has been selected as a nonlinear state observer. Simulations have been done in OLGA and PVTsim as multiphase flow simulators. The pressure measurements at the discrete points have been used as input to the state estimation algorithm. Finally, by defining different scenarios the effect of leak magnitude, location and time duration on the performance of the proposed method has been evaluated.

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Research paper thumbnail of Sensitivity analysis of the bullwhip effect in supply chains with time delay

International Journal of Systems Science: Operations & Logistics, 2021

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Research paper thumbnail of Prediction‐discrepancy based on innovative particle filter for estimating UAV true position in the presence of the GPS spoofing attacks

IET Radar, Sonar & Navigation, 2020

In this paper, a novel prediction-discrepancy based on innovative particle filter (PDIPF) is prop... more In this paper, a novel prediction-discrepancy based on innovative particle filter (PDIPF) is proposed to solve the unmanned aerial vehicle (UAV) positioning problem in the presence of the global positioning system (GPS) spoofing attack, supposing that the GPS spoofing effects are in the form of unknown but bounded errors. To cope with the GPS spoofing attacks as unknown sudden changes of system state variables, the compensation of the GPS spoofing effects is adaptively done in two basic parts of PDIPF algorithm including particle weighting and covariance matrix adaption. In addition, a theorem is developed which verifies that the output estimation error is upper bounded by a given probability with the help of the adapted covariance matrix. Besides, the particle weight calculation in PDIPF is done with respect to the prediction discrepancy of generated particles from the GPS measurements. The proposed PDIPF is used to decrease the effects of any GPS spoofing errors with different probability density functions and estimate true position of UAV in the presence of the GPS spoofing attacks. The algorithm is applied to the inertial navigation system/GPS/Loran-C integration systems. Simulation results demonstrate the effectiveness of the proposed PDIPF algorithm in terms of accuracy and redundancy.

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Research paper thumbnail of Augmented input estimation in multiple maneuvering target tracking

Journal of Systems Engineering and Electronics, 2019

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Research paper thumbnail of Stabilization of switched systems with all unstable modes: application to the aircraft team problem

Journal of Systems Engineering and Electronics, 2019

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Research paper thumbnail of Design of state‐dependent impulsive observer for non‐linear time‐delay systems

IET Control Theory & Applications, 2019

In this study, a new state-dependent impulsive observer (SDIO) is proposed for a class of non-lin... more In this study, a new state-dependent impulsive observer (SDIO) is proposed for a class of non-linear time-delay systems. The proposed observer is based on the extended pseudo-linearisation technique that parameterises the non-linear time-delay system to a pseudo-linear structure with time delay and state-dependent coefficients. Applying this technique, the presented observer is utilised for non-linear systems with multiple, time-varying and distributed delays. Furthermore, the extended pseudo-linearisation technique simplifies the procedure of impulsive observer design for non-linear time-delay systems. The proposed SDIO is capable of continuously estimating system states using discrete samples of the system output that are available at discrete impulse times. The stability and convergence of the proposed observer are proven via a theorem utilising time-varying and delay-independent Lyapunov function and the comparison system theory of impulsive systems. It is guaranteed that the estimation error asymptotically converges to zero under well-defined and less-conservative sufficient conditions that are presented in terms of feasible linear matrix inequalities. In addition, the stability theorem specifies an upper bound on the time intervals between consecutive impulses. Results are simulated on Congo Ebola disease model which is an epidemic non-linear time-delay system. Simulation results confirm the effectiveness and performance of the proposed SDIO.

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Research paper thumbnail of Identification and Fault Diagnosis of an Industrial Gas Turbine Using State-Space Methods

Advanced Materials Research, Nov 1, 2011

The objective of this paper is to identify, detect and isolate faults to an industrial gas turbin... more The objective of this paper is to identify, detect and isolate faults to an industrial gas turbine. The detection scheme is based on the generation of so-called "residuals" that are errors between estimated and measured variables of the process. A State-Space model is used for identification and some observer-based methods are used for residual generation, while for residual evaluation a neural network classifier for MLP is used. The proposed fault detection and isolation tool has been tested on a single-shaft industrial gas turbine simulator.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Automotive radar data filtering approach for Adaptive Cruise Control systems

ABSTRACT

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Research paper thumbnail of Novel Approach for Nonlinear Maneuvering Target Tracking Based on Input Estimation

Applied Mechanics and Materials, Oct 1, 2011

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Research paper thumbnail of The tail mean–variance optimal portfolio selection under generalized skew-elliptical distribution

Insurance: Mathematics and Economics

Abstract In the insurance and financial markets, events of extreme losses happen in the tail of r... more Abstract In the insurance and financial markets, events of extreme losses happen in the tail of return distributions, and investors are sensitive to these losses. The Tail Mean–Variance (TMV) criterion focuses on the rare risk but large losses, and it has recently been used in financial management for portfolio selection. In this paper, the proposed TMV criterion is based on the two measures of risk, i.e., the Tail Conditional Expectation (TCE) and Tail Variance (TV) under Generalized Skew-Elliptical (GSE) distribution. We obtain an explicit solution with simple implementation and use a convex optimization approach for the TMV optimization problem under the GSE distribution. We also provide a practical example of a portfolio optimization problem using the proposed TMV criterion. The empirical results show that the optimal portfolio performance can be improved by controlling the tail variability of returns distribution.

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Research paper thumbnail of A Minimum Principle for Stochastic Optimal Control Problem with Interval Cost Function

Taiwanese Journal of Mathematics

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Research paper thumbnail of Modeling and Analysis of COVID-19 Spread: The Impacts of Nonpharmaceutical Protocols

Computational and Mathematical Methods in Medicine

In this study, the extended SEIR dynamical model is formulated to investigate the spread of coron... more In this study, the extended SEIR dynamical model is formulated to investigate the spread of coronavirus disease (COVID-19) via a special focus on contact with asymptomatic and self-isolated infected individuals. Furthermore, a mathematical analysis of the model, including positivity, boundedness, and local and global stability of the disease-free and endemic equilibrium points in terms of the basic reproduction number, is presented. The sensitivity analysis indicates that reducing the disease contact rate and the transmissibility factor related to asymptomatic individuals, along with increasing the quarantine/self-isolation rate and the contact-tracing process, from the view of flattening the curve for novel coronavirus, are crucial to the reduction in disease-related deaths.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Stock Market Prediction-by-Prediction Based on Autoencoder Long Short-Term Memory Networks

2020 28th Iranian Conference on Electrical Engineering (ICEE)

This paper proposes a strategy for the stock market closing price prediction-by-prediction using ... more This paper proposes a strategy for the stock market closing price prediction-by-prediction using the autoencoder long short-term memory (AE-LSTM) networks. To integrate technical analysis with deep learning methods, technical indicators and oscillators are added to the raw dataset as features. The wavelet transformation is used as a noise-removal technique in the stock index. Anomaly detection in dataset is also performed through the z-score method. First, the autoencoder is trained to represent the data. Then, the encoder extracts feature and puts them into the LSTM network for predicting the closing price of the stock index. Afterwards, the system predicts subsequently based on the previous predictions. To evaluate the theoretical results, the proposed method is experimented on the standard and poor's 500 (S&P 500) stock market index through several simulation studies. To analyze the results, several performance criteria are used to compare the results with the generative adversarial network (GAN). The simulation studies are conducted to show the effectiveness of the proposed method in the Python environment, and the results show that the proposed prediction-by-prediction method outperforms GAN in terms of daily adjusted closing price prediction.

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Research paper thumbnail of Sensitivity analysis for evaluation of the effect of sensors error on the wind turbine variables using Monte Carlo simulation

IET Renewable Power Generation

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Research paper thumbnail of Mathematical Modeling of the Novel Coronavirus Pandemic in Iran: A Model With Vaccination

2022 8th International Conference on Control, Instrumentation and Automation (ICCIA), 2022

The novel coronavirus (COVID-19) is a major health threat caused by a virus called Severe Acute R... more The novel coronavirus (COVID-19) is a major health threat caused by a virus called Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). COVID-19 can cause acute respiratory illness, which is deadly in some people. Epidemio-logically, vaccination is the safest and most effective approach to gaining immunity and fighting against viral diseases. In this paper, the extended compartmental SEIR model, considering vaccination as a pharmaceutical intervention, to assess the behavior of the COVID-19 outbreak in Iran is proposed. Initially, some mathematical analysis of the model, including positivity, boundedness, the basic reproduction number, and herd immunity is investigated. In order to validate the proposed model, the biological parameters of the model are estimated based on real-confirmed cases in Iran at a specified interval of time. Finally, to examine the concept of flattening the curve of the disease outbreak, the impacts of physical distancing and vaccination on mitigating the burden of the epidemic are compared graphically.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Suboptimal sliding manifold For nonlinear supply chain with time delay

Journal of Combinatorial Optimization, 2021

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Multi-Step-Ahead Prediction of Stock Price Using a New Architecture of Neural Networks

Modelling and forecasting Stock market is a challenging task for economists and engineers since i... more Modelling and forecasting Stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. This nonlinearity affects the efficiency of the price characteristics. Using an Artificial Neural Network (ANN) is a proper way to model this nonlinearity and it has been used successfully in onestep-ahead and multi-step-ahead prediction of different stocks prices. Several factors, such as input variables, preparing data sets, network architectures and training procedures, have huge impact on the accuracy of the neural network prediction. The purpose of this paper is to predict multi-step-ahead prices of the stock market and derive the method, based on Recurrent Neural Networks (RNN), Real-Time Recurrent Learning (RTRL) networks and Nonlinear Autoregressive model process with exogenous input (NARX). This model is trained and tested by Tehran Securities Exchange data.

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Research paper thumbnail of Distributed Estimation and Sensor Selection in Wireless Sensor Network in the Presence of State-Dependent Noise

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Forecasting the Iranian Tax Revenues: An Application of Nonlinear Models

Iranian National Tax Administration, 2008

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Analyzing the Advantage of Combination of Density Forecasts in Tehran Stock Exchange

Today, stock market plays a key role in the economy of any country and is considered as one of th... more Today, stock market plays a key role in the economy of any country and is considered as one of the growth indicators of any economy. Gaining the skills of gathering and analyzing data simultaneously, as well as using this knowledge in economic investigations, make time and precision factors to be the drawcard of any investor in competition with others. Therefore, having a predictive approach with the lowest degree of error will lead to smarter management of resources. Due to the complex and stochastic nature of the stock market, conventional forecasting approaches in this field have usually faced serious challenges, most notably losing the robustness when the data type changed over time. Moreover, by focusing on point forecasting, some useful statistical information about the objective random variable has been ignored inadvertently, undermining the prediction efficiency. The focus of this study is on density forecasting models which, unlike point forecasting, contain a description o...

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Research paper thumbnail of Unscented Kalman Filter in Gas pipeline leakage magnitude estimation and localization

2019 6th International Conference on Control, Instrumentation and Automation (ICCIA), 2019

A model-based real-time transient modeling estimation process is proposed in a gas transmission l... more A model-based real-time transient modeling estimation process is proposed in a gas transmission line with the purpose of leak detection. The objective of this paper is aimed at determining the effectiveness of filtering techniques in leak detection and localization in a pipeline network through a synthetic pipe simulation. A model for a straight pipeline without any branch has been derived from Momentum and continuity equations and discretized based on the method of characteristics. In the next step by assuming proper boundary conditions, state-space representation of the system has been developed and stability and observability conditions have been explored. The unscented Kalman Filter has been selected as a nonlinear state observer. Simulations have been done in OLGA and PVTsim as multiphase flow simulators. The pressure measurements at the discrete points have been used as input to the state estimation algorithm. Finally, by defining different scenarios the effect of leak magnitude, location and time duration on the performance of the proposed method has been evaluated.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Sensitivity analysis of the bullwhip effect in supply chains with time delay

International Journal of Systems Science: Operations & Logistics, 2021

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Prediction‐discrepancy based on innovative particle filter for estimating UAV true position in the presence of the GPS spoofing attacks

IET Radar, Sonar & Navigation, 2020

In this paper, a novel prediction-discrepancy based on innovative particle filter (PDIPF) is prop... more In this paper, a novel prediction-discrepancy based on innovative particle filter (PDIPF) is proposed to solve the unmanned aerial vehicle (UAV) positioning problem in the presence of the global positioning system (GPS) spoofing attack, supposing that the GPS spoofing effects are in the form of unknown but bounded errors. To cope with the GPS spoofing attacks as unknown sudden changes of system state variables, the compensation of the GPS spoofing effects is adaptively done in two basic parts of PDIPF algorithm including particle weighting and covariance matrix adaption. In addition, a theorem is developed which verifies that the output estimation error is upper bounded by a given probability with the help of the adapted covariance matrix. Besides, the particle weight calculation in PDIPF is done with respect to the prediction discrepancy of generated particles from the GPS measurements. The proposed PDIPF is used to decrease the effects of any GPS spoofing errors with different probability density functions and estimate true position of UAV in the presence of the GPS spoofing attacks. The algorithm is applied to the inertial navigation system/GPS/Loran-C integration systems. Simulation results demonstrate the effectiveness of the proposed PDIPF algorithm in terms of accuracy and redundancy.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Augmented input estimation in multiple maneuvering target tracking

Journal of Systems Engineering and Electronics, 2019

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Stabilization of switched systems with all unstable modes: application to the aircraft team problem

Journal of Systems Engineering and Electronics, 2019

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Design of state‐dependent impulsive observer for non‐linear time‐delay systems

IET Control Theory & Applications, 2019

In this study, a new state-dependent impulsive observer (SDIO) is proposed for a class of non-lin... more In this study, a new state-dependent impulsive observer (SDIO) is proposed for a class of non-linear time-delay systems. The proposed observer is based on the extended pseudo-linearisation technique that parameterises the non-linear time-delay system to a pseudo-linear structure with time delay and state-dependent coefficients. Applying this technique, the presented observer is utilised for non-linear systems with multiple, time-varying and distributed delays. Furthermore, the extended pseudo-linearisation technique simplifies the procedure of impulsive observer design for non-linear time-delay systems. The proposed SDIO is capable of continuously estimating system states using discrete samples of the system output that are available at discrete impulse times. The stability and convergence of the proposed observer are proven via a theorem utilising time-varying and delay-independent Lyapunov function and the comparison system theory of impulsive systems. It is guaranteed that the estimation error asymptotically converges to zero under well-defined and less-conservative sufficient conditions that are presented in terms of feasible linear matrix inequalities. In addition, the stability theorem specifies an upper bound on the time intervals between consecutive impulses. Results are simulated on Congo Ebola disease model which is an epidemic non-linear time-delay system. Simulation results confirm the effectiveness and performance of the proposed SDIO.

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