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Papers by Seid H. Pourtakdoust

Research paper thumbnail of Muscles force patterns prediction and joint reactions determination during 3D spine movements by means of optimal control theory

2008 International Conference on Control, Automation and Systems, 2008

ABSTRACT Modeling the spine behavior plays a key role in understanding mechanisms leading to spin... more ABSTRACT Modeling the spine behavior plays a key role in understanding mechanisms leading to spinal disorders and injuries. The goal of this article is to develop a new model to obtain 3D spine movement patterns. High degrees of freedom of the system, a great many of muscles involved in the spine movements and its unstable intrinsic behavior make the control problem more difficult. New theories in computational motor control suggest optimal control as a useful tool for modeling Central Nervous System (CNS) to provide appropriate control signals while it computes the neural interactions with biological sensors. Therefore, CNS is modeled as an optimal controller. Our simulations illustrate the movement behavior and muscles force patterns which may be used to relate to the risk of injury in both joints and muscles.

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Research paper thumbnail of Flexural Vibration of Atomic Force Microscope Cantilever with Dimensional Effects

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Research paper thumbnail of Radiation based satellite attitude and thermal parameters estimation considering conduction effect

Advances in Space Research

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Research paper thumbnail of Modeling Halo Orbits and the Associated Manifolds in the Restricted Three Body Problem

ABSTRACT Development of halo orbits and their associated invariant manifolds are investigated. Ha... more ABSTRACT Development of halo orbits and their associated invariant manifolds are investigated. Halo orbits play a fundamental role in complex space mission designs. In essence, halo orbits are periodic solutions of the restricted three body problem (R3BP) determined under specific initial conditions. In this paper, the symmetric property of the nonlinear R3BP governing differential equations is utilized in order to obtain the desired initial conditions. In this regard the differential correction technique and the state transition matrix are used to generate the halo orbits. The differential correction technique, based on the Newton method, is an effective tool for solving two point boundary value problems. In addition to generate the stable and unstable manifolds, the initial conditions are perturbed in the direction of Eigenvectors and the equations of motion are integrated for an arbitrary time interval.

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Research paper thumbnail of Solution of FPK equation for stochastic dynamics subjected to additive Gaussian noise via deep learning approach

Structural Safety

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Research paper thumbnail of New Results - Multisensor data fusion for navigation

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Research paper thumbnail of Aeroelastic analysis of guided hypersonic launch vehicles

Scientia Iranica, 2004

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Research paper thumbnail of Time Optimal Closed-Loop Fuzzy-Control Strategy for Nonlinear Lunar Lander Mission

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Research paper thumbnail of Trim and maneuverability analysis of a UAV using a new constrained PSO approach

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Research paper thumbnail of Advanced fault detection and diagnosis in spacecraft attitude control systems: Current state and challenges

Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering

A review of advanced fault detection and diagnosis (FDD) techniques in attitude control systems (... more A review of advanced fault detection and diagnosis (FDD) techniques in attitude control systems (ACSs) of spacecraft is presented. In the first part of the paper, several types of ACS failure scenarios with their practical solutions are presented. Next, the existing approaches to FDD are considered and classified based on different criteria, including applications and design techniques. The literature of this part showed that to enhance ACS operational safety, predictability of failure of an ACS and/or of its components as well as reducing the possibility of failure occurrence is imperative. In addition, fast FDD of various kinds of failures is necessary to guarantee the required reliability of an ACS. The second part of this study highlights challenges involved with different FDD approaches, emphasizing their practical applicability. Current research gaps in FDD techniques such as insensitive residual signal, process monitoring methods, accurate plant model design, easy-to-use soft...

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Research paper thumbnail of Model-based microburst identification using a hybridized extended Kalman filter with genetic algorithm

Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering

Microburst (MB) wind shear is one of the most important meteorological dangers threatening the ai... more Microburst (MB) wind shear is one of the most important meteorological dangers threatening the aircraft (AC) safety and the life of passengers. Though there are some ground-based 3D Lidar systems to detect low-level MB wind shears to alert the pilots, there have been fewer scientific attempts to identify model-based MB parameters via AC onboard air and position data. The latter refers to the development and identification of an acceptable MB model upon which an automatic flight control (AFC) system can be designed to control the AC through wind shear microburst. In essence, accurate knowledge of MB model is an essential prerequisite for design and analysis of AFC systems that can safely fly the AC against microbursts, especially in crucial phases of flight such as takeoff and landing. The present study focuses on online estimation of MB parameters whose results pave the way for effective MB autopilot designs for safe flights through MB. The proposed task is accomplished via a model-...

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Research paper thumbnail of Trim and Maneuverability Analysis Using a New Constrained PSO Approach of a UAV

Performance characteristic of an Unmanned Air Vehicle (UAV) is investigated using a newly develop... more Performance characteristic of an Unmanned Air Vehicle (UAV) is investigated using a newly developed heuristic approach. Almost all flight phases of any air vehicle can be categorized into trim and maneuvering flights. In this paper, a new envelope called trim-ability envelope, is introduced and sketched within the conventional flight envelope for a small UAV. Optimal maneuverability of the intended UAV is evaluated for minimum time pull-up and turn maneuvers. For both the trim and the maneuver problems, the nonlinear 6DOF dynamic models as well as the vehicle constraints are considered. A heuristic based constrained optimization approach is developed to solve both the trim and maneuver problems. Several interesting performance characteristics are extracted. The results are indicative of a good potential for the proposed algorithm to handle complex constrained optimization problems in aerospace engineering .

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Research paper thumbnail of A Maneuver-Based Motion Planning Approach for Uavs by Particle Swarm Optimization Method

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Research paper thumbnail of Development of a Mushy State Simulated Annealing Algorithm for the Design of an Optimal Path Regulator Autopilot System

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Research paper thumbnail of Evaluation of flapping wing propulsion based on a new experimentally validated aeroelastic model

Scientia Iranica, 2012

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Research paper thumbnail of Spacecraft Attitude and System Identification via Marginal Modified UnscentedKalman Filter Utilizing the Sun and Calibrated Three-Axis-Magnetometer Sensors

Scientia Iranica, 2014

This paper deals with attitude determination, parameter identification and reference sensor calib... more This paper deals with attitude determination, parameter identification and reference sensor calibration simultaneously. A LEO satellite’s attitude, inertia tensor as well as calibration parameters of Three-Axis-Magnetometer (TAM) including scale factors, misalignments and biases along three body axes are estimated during a maneuver designed to satisfy the condition of persistency of excitation. The advanced nonlinear estimation algorithm of Unscented Kalman Filter (UKF) is a good choice for nonlinear estimation problem of attitude determination, but its computational cost is considerably larger than the widespread low accurate Extended Kalman Filter. Reduced Sigma Point Filters provide good solutions and also decrease the run time of the UKF. However, in contrast to nonlinear problem of attitude determination, parameter identification and sensor calibration have linear dynamics. Therefore, a new Marginal UKF is proposed that combines utility of Kalman Filter with Modified UKF (MUKF)...

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Research paper thumbnail of Thrust - Limited Optimal Three - Dimensional Spacecraft Trajectories

International Journal of Engineering, Feb 1, 2001

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Research paper thumbnail of Nonlinear Aerothermoelastic Behavior of Skin Panel with Wall Shear Stress Effect

Journal of Thermal Stresses, 2005

... DOI: 10.1080/014957390523714. Affiliations: 1: Department of Aerospace Engineering, SharifUni... more ... DOI: 10.1080/014957390523714. Affiliations: 1: Department of Aerospace Engineering, SharifUniversity of Technology, Tehran, Iran 2: Department of Mechanical Engineering, School ofEngineering, Shiraz University, Shiraz, Iran. Publication date: 2005-02-01. ...

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Research paper thumbnail of A deep learning approach for the solution of probability density evolution of stochastic systems

Structural Safety

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Research paper thumbnail of An Improved Multi-State Constraint Kalman Filter for Visual-Inertial Odometry

Cornell University - arXiv, Oct 4, 2022

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Research paper thumbnail of Muscles force patterns prediction and joint reactions determination during 3D spine movements by means of optimal control theory

2008 International Conference on Control, Automation and Systems, 2008

ABSTRACT Modeling the spine behavior plays a key role in understanding mechanisms leading to spin... more ABSTRACT Modeling the spine behavior plays a key role in understanding mechanisms leading to spinal disorders and injuries. The goal of this article is to develop a new model to obtain 3D spine movement patterns. High degrees of freedom of the system, a great many of muscles involved in the spine movements and its unstable intrinsic behavior make the control problem more difficult. New theories in computational motor control suggest optimal control as a useful tool for modeling Central Nervous System (CNS) to provide appropriate control signals while it computes the neural interactions with biological sensors. Therefore, CNS is modeled as an optimal controller. Our simulations illustrate the movement behavior and muscles force patterns which may be used to relate to the risk of injury in both joints and muscles.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Flexural Vibration of Atomic Force Microscope Cantilever with Dimensional Effects

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Radiation based satellite attitude and thermal parameters estimation considering conduction effect

Advances in Space Research

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Modeling Halo Orbits and the Associated Manifolds in the Restricted Three Body Problem

ABSTRACT Development of halo orbits and their associated invariant manifolds are investigated. Ha... more ABSTRACT Development of halo orbits and their associated invariant manifolds are investigated. Halo orbits play a fundamental role in complex space mission designs. In essence, halo orbits are periodic solutions of the restricted three body problem (R3BP) determined under specific initial conditions. In this paper, the symmetric property of the nonlinear R3BP governing differential equations is utilized in order to obtain the desired initial conditions. In this regard the differential correction technique and the state transition matrix are used to generate the halo orbits. The differential correction technique, based on the Newton method, is an effective tool for solving two point boundary value problems. In addition to generate the stable and unstable manifolds, the initial conditions are perturbed in the direction of Eigenvectors and the equations of motion are integrated for an arbitrary time interval.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Solution of FPK equation for stochastic dynamics subjected to additive Gaussian noise via deep learning approach

Structural Safety

Bookmarks Related papers MentionsView impact

Research paper thumbnail of New Results - Multisensor data fusion for navigation

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Aeroelastic analysis of guided hypersonic launch vehicles

Scientia Iranica, 2004

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Time Optimal Closed-Loop Fuzzy-Control Strategy for Nonlinear Lunar Lander Mission

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Trim and maneuverability analysis of a UAV using a new constrained PSO approach

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Advanced fault detection and diagnosis in spacecraft attitude control systems: Current state and challenges

Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering

A review of advanced fault detection and diagnosis (FDD) techniques in attitude control systems (... more A review of advanced fault detection and diagnosis (FDD) techniques in attitude control systems (ACSs) of spacecraft is presented. In the first part of the paper, several types of ACS failure scenarios with their practical solutions are presented. Next, the existing approaches to FDD are considered and classified based on different criteria, including applications and design techniques. The literature of this part showed that to enhance ACS operational safety, predictability of failure of an ACS and/or of its components as well as reducing the possibility of failure occurrence is imperative. In addition, fast FDD of various kinds of failures is necessary to guarantee the required reliability of an ACS. The second part of this study highlights challenges involved with different FDD approaches, emphasizing their practical applicability. Current research gaps in FDD techniques such as insensitive residual signal, process monitoring methods, accurate plant model design, easy-to-use soft...

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Model-based microburst identification using a hybridized extended Kalman filter with genetic algorithm

Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering

Microburst (MB) wind shear is one of the most important meteorological dangers threatening the ai... more Microburst (MB) wind shear is one of the most important meteorological dangers threatening the aircraft (AC) safety and the life of passengers. Though there are some ground-based 3D Lidar systems to detect low-level MB wind shears to alert the pilots, there have been fewer scientific attempts to identify model-based MB parameters via AC onboard air and position data. The latter refers to the development and identification of an acceptable MB model upon which an automatic flight control (AFC) system can be designed to control the AC through wind shear microburst. In essence, accurate knowledge of MB model is an essential prerequisite for design and analysis of AFC systems that can safely fly the AC against microbursts, especially in crucial phases of flight such as takeoff and landing. The present study focuses on online estimation of MB parameters whose results pave the way for effective MB autopilot designs for safe flights through MB. The proposed task is accomplished via a model-...

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Trim and Maneuverability Analysis Using a New Constrained PSO Approach of a UAV

Performance characteristic of an Unmanned Air Vehicle (UAV) is investigated using a newly develop... more Performance characteristic of an Unmanned Air Vehicle (UAV) is investigated using a newly developed heuristic approach. Almost all flight phases of any air vehicle can be categorized into trim and maneuvering flights. In this paper, a new envelope called trim-ability envelope, is introduced and sketched within the conventional flight envelope for a small UAV. Optimal maneuverability of the intended UAV is evaluated for minimum time pull-up and turn maneuvers. For both the trim and the maneuver problems, the nonlinear 6DOF dynamic models as well as the vehicle constraints are considered. A heuristic based constrained optimization approach is developed to solve both the trim and maneuver problems. Several interesting performance characteristics are extracted. The results are indicative of a good potential for the proposed algorithm to handle complex constrained optimization problems in aerospace engineering .

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Maneuver-Based Motion Planning Approach for Uavs by Particle Swarm Optimization Method

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Development of a Mushy State Simulated Annealing Algorithm for the Design of an Optimal Path Regulator Autopilot System

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Evaluation of flapping wing propulsion based on a new experimentally validated aeroelastic model

Scientia Iranica, 2012

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Spacecraft Attitude and System Identification via Marginal Modified UnscentedKalman Filter Utilizing the Sun and Calibrated Three-Axis-Magnetometer Sensors

Scientia Iranica, 2014

This paper deals with attitude determination, parameter identification and reference sensor calib... more This paper deals with attitude determination, parameter identification and reference sensor calibration simultaneously. A LEO satellite’s attitude, inertia tensor as well as calibration parameters of Three-Axis-Magnetometer (TAM) including scale factors, misalignments and biases along three body axes are estimated during a maneuver designed to satisfy the condition of persistency of excitation. The advanced nonlinear estimation algorithm of Unscented Kalman Filter (UKF) is a good choice for nonlinear estimation problem of attitude determination, but its computational cost is considerably larger than the widespread low accurate Extended Kalman Filter. Reduced Sigma Point Filters provide good solutions and also decrease the run time of the UKF. However, in contrast to nonlinear problem of attitude determination, parameter identification and sensor calibration have linear dynamics. Therefore, a new Marginal UKF is proposed that combines utility of Kalman Filter with Modified UKF (MUKF)...

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Thrust - Limited Optimal Three - Dimensional Spacecraft Trajectories

International Journal of Engineering, Feb 1, 2001

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Nonlinear Aerothermoelastic Behavior of Skin Panel with Wall Shear Stress Effect

Journal of Thermal Stresses, 2005

... DOI: 10.1080/014957390523714. Affiliations: 1: Department of Aerospace Engineering, SharifUni... more ... DOI: 10.1080/014957390523714. Affiliations: 1: Department of Aerospace Engineering, SharifUniversity of Technology, Tehran, Iran 2: Department of Mechanical Engineering, School ofEngineering, Shiraz University, Shiraz, Iran. Publication date: 2005-02-01. ...

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A deep learning approach for the solution of probability density evolution of stochastic systems

Structural Safety

Bookmarks Related papers MentionsView impact

Research paper thumbnail of An Improved Multi-State Constraint Kalman Filter for Visual-Inertial Odometry

Cornell University - arXiv, Oct 4, 2022

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A deep learning approach for the solution of probability density evolution of stochastic systems

A deep learning approach for the solution of probability density evolution of stochastic systems, 2022

Derivation of the probability density evolution provides invaluable insight into the behavior of ... more Derivation of the probability density evolution provides invaluable insight into the behavior of many stochastic systems and their performance. However, for most real-time applications, numerical determination of the probability density evolution is a formidable task. The latter is due to the required temporal and spatial discretization schemes that render most computational solutions prohibitive and impractical. In this respect, the development of an efficient computational surrogate model is of paramount importance. Recent studies on the physics-constrained networks show that a suitable surrogate can be achieved by encoding the physical insight into a deep neural network. To this aim, the present work introduces DeepPDEM which utilizes the concept of physics-informed networks to solve the evolution of the probability density via proposing a deep learning method. DeepPDEM learns the General Density Evolution Equation (GDEE) of stochastic structures. This approach paves the way for a mesh-free learning method that can solve the density evolution problem without prior simulation data. Moreover, it can also serve as an efficient surrogate for the solution at any other spatiotemporal points within optimization schemes or real-time applications. To demonstrate the potential applicability of the proposed framework, two network architectures with different activation functions as well as two optimizers are investigated. Numerical implementation on three different problems verifies the accuracy and efficacy of the proposed method.

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Research paper thumbnail of Solution of FPK equation for stochastic dynamics subjected to additive Gaussian noise via deep learning approach

Solution of FPK equation for stochastic dynamics subjected to additive Gaussian noise via deep learning approach, 2024

The Fokker-Plank-Kolmogorov (FPK) equation is an idealized model representing many stochastic sys... more The Fokker-Plank-Kolmogorov (FPK) equation is an idealized model representing many stochastic systems commonly encountered in the analysis of stochastic structures as well as many other applications. Its solution thus provides an invaluable insight into the performance of many engineering systems. Despite its great importance, the solution of the FPK equation is still extremely challenging. For systems of practical significance, the FPK equation is usually high dimensional, rendering most of the numerical methods ineffective. In this respect, the present work introduces the FPK-DP Net as a physics-informed network that encodes the physical insights, i.e. the governing constrained differential equations emanated out of physical laws, into a deep neural network. FPK-DP Net is a mesh-free learning method that can solve the density evolution of stochastic dynamics subjected to additive white Gaussian noise without any prior simulation data and can be used as an efficient surrogate model afterward. FPK-DP Net uses the dimension-reduced FPK equation. Therefore, it can be used to address high-dimensional practical problems as well. To demonstrate the potential applicability of the proposed framework, and to study its accuracy and efficacy, numerical implementations on five different benchmark problems are investigated.

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