Amirkabir International Journal of Modeling,Identification,Simulation and Control

Journal Information

Publisher: Tehran, Amirkabir University of Technology

ISSN: 2008-6067

Email:  journal@aut.ac.ir

Website:  miscj.aut.ac.ir

Number of Issues: 20

Number of Articles: 163

Update date:  2020/02/18

Journal Archive

Current Issue

Decentralized Model Reference Adaptive Control of Large Scale Interconnected Systems with Both State and Input Delays

S. H Hashemipour; N Vasegh; A Khaki Sedigh

Amirkabir International Journal of Modeling,Identification,Simulation and Control, Volume 50, Issue 1, Pages 3-12
DOI: 10.22060/miscj.2017.12239.5021

In this paper, the problem of decentralized Model Reference Adaptive Control (MRAC) for interconnected large scale systems associated with time varying delays in state and input is investigated. The upper bounds of the interconnection terms are considered to be unknown. Time varying delays in the nonlinear interconnection terms are bounded and non-negative continuous functions and their derivatives are not necessarily less than one. Moreover, a simple and practical method based on periodic characteristics of the reference model is established to predict the future states and input delay compensation. It is shown that the solution of uncertain large-scale time-delay interconnected system converges uniformly exponentially to inside of a desired small ball. Simulation results of a chemical reactor system and a numerical example illustrate effectiveness of the proposed methods.

Integration Scheme for SINS/GPS System Based on Vertical Channel Decomposition and In-Motion Alignment

H Nourmohammadi; J Keighobadi

Amirkabir International Journal of Modeling,Identification,Simulation and Control, Volume 50, Issue 1, Pages 13-22
DOI: 10.22060/miscj.2017.12483.5036

Accurate alignment and vertical channel instability play an important role in the strap-down inertial navigation system (SINS), especially in the case that precise navigation has to be achieved over long periods of time. Due to poor initialization and the cumulative errors of low-cost inertial measurement units (IMUs), initial alignment is insufficient to achieve required navigation accuracy. To tackle this problem, in this paper, misalignment error is dynamically modeled and in-motion alignment is provided based on position and velocity matching. It is revealed that using misalignment error, orientation estimation can be properly corrected. Moreover, to prevent the instability effects of the vertical channel, decomposed SINS error model is derived. In the decomposed SINS error model, the navigation states in the vertical channel are separated from those in the horizontal plane. Two-step estimation process is developed for integration of the aforementioned SINS error dynamics with the measurements provided by global positioning system (GPS), and fifteen-state SINS/GPS mechanization is presented. Assessment of the proposed approach is conducted in the airborne test.

Design of Observer-Based H∞ Controller for Robust Stabilization of Networked Systems Using Switched Lyapunov Functions

A Farnam; R Mahboobi Esfanjani

Amirkabir International Journal of Modeling,Identification,Simulation and Control, Volume 50, Issue 1, Pages 23-30
DOI: 10.22060/miscj.2017.12188.5013

In this paper, a H. controller is synthesized for networked systems subject to random transmission delays with known upper bound and different occurrence probabilities in both feedback (sensor to controller) and forward (controller to actuator) channels. A remote observer is employed to improve the performance of the system by computing non-delayed estimates of the states. The closed-loop system is described in the framework of switched systems; then, a switched Lyapunov function is utilized to obtain conditions to determine the gains of the observer and the controller such that robust asymptotic stability of the system is assured. Two illustrative examples are presented to demonstrate the real-world applicability and superiority of the proposed approach compared to rival ones in the literatue.

Robust Adaptive Control of Voltage Saturated Flexible Joint Robots with Experimental Evaluations

A Izadbakhsh

Amirkabir International Journal of Modeling,Identification,Simulation and Control, Volume 50, Issue 1, Pages 31-38
DOI: 10.22060/miscj.2017.12174.5008

This paper is concerned with the problem of designing and implementing a robust adaptive control strategy for the flexible joint electrically driven robots (FJEDR) while considering the constraints on the actuator voltage input. The control design procedure is based on the function approximation technique, to avoid saturation besides being robust against both structured and unstructured uncertainties associated with external disturbances and un-modeled dynamics. Stability proof of the overall closed-loop system is given via the Lyapunov direct method. The analytical studies as well as experimental results obtained using MATLAB/SIMULINK external mode control on a single-link flexible joint electrically driven robot, demonstrate a high performance of the proposed control schemes.

An Efficient Data Replication Strategy in Large-Scale Data Grid Environments Based on Availability and Popularity

N Mansouri; M.M Javidi

Amirkabir International Journal of Modeling,Identification,Simulation and Control, Volume 50, Issue 1, Pages 39-50
DOI: 10.22060/miscj.2017.12236.5020

The data grid technology, which uses the scale of the Internet to solve storage limitation for the huge amount of data, has become one of the hot research topics. Recently, data replication strategies have been widely employed in a distributed environment to copy frequently accessed data in suitable sites. The primary purposes are shortening distances of the file transmission and achieving files from nearby locations to requested sites so as to minimize retrieval time and bandwidth usage. In this paper, we propose a new replica selection strategy which is based on response time and security. However, replication should be used wisely because the storage size of each Data Grid site is limited. In addition, we propose a new replica replacement strategy that considers file availability, time of access, access frequency and size of file. The simulation results report that the proposed strategy can effectively improve mean job time, bandwidth consumption for data delivery, and data availability compared with those of the tested algorithms.

Dynamic Sliding Mode Control of Nonlinear Systems Using Neural Networks

A Karami-Mollaee; H Shanechi

Amirkabir International Journal of Modeling,Identification,Simulation and Control, Volume 50, Issue 1, Pages 51-60
DOI: 10.22060/miscj.2017.12805.5043

In this paper, dynamic sliding mode control (DSMC) of nonlinear systems using neural networks is proposed. In DSMC, the chattering is removed due to the integrator placed before the input control signal of the plant. However, in DSMC, the augmented system has higher order than the actual system, i.e. the states number of the augmented system is higher than the actual system and then to control of such a system, we must know and identify the new states, or the plant model should be completely known. To solve this problem, we suggest two online neural networks to identify and to obtain a model for the unknown nonlinear system. In the first approach, the neural network training law is based on the available system states and the bound of the observer error is not proved to converge to zero. The advantage of the second training law is only using the system’s output and the observer error converges to zero based on the Lyapunov stability theorem. To verify these approaches, Duffing-Holmes chaotic systems (DHC) are used.

Kinematic and Dynamic Analyses of Tripteron, an Over-Constrained 3-DOF Translational Parallel Manipulator, through Newton-Euler Approach

A Arian; B Danaei; M Tale Masouleh

Amirkabir International Journal of Modeling,Identification,Simulation and Control, Volume 50, Issue 1, Pages 61-70
DOI: 10.22060/miscj.2018.13020.5055

In this research, as the main contribution, a comprehensive study is carried out on the mathematical modeling and analysis of the inverse kinematics and dynamics of an over-constrained three translational degree-of-freedom parallel manipulator. Due to inconsistency between the number of equations and the unknowns, the problem of obtaining the constraint forces and torques of over-constraint manipulators does not admit solution, which can be regarded as one of the drawbacks of such mechanisms. In this paper, in order to overcome this problem and circumvent inconsistency between the number of equations and the unknowns, two of the revolute joints attached to the end-effector are changed into a universal and a spherical joint without changing the motion pattern of the manipulator under study. Then, the dynamical equations of the manipulator are obtained based on the Newton–Euler approach, and a simple and a compact formulations are provided. Then, all the joint forces and torques are presented. In order to evaluate accuracy of the obtained formulated model, a motion for the end-effector as a case study is performed, and it has been shown that the results of the analytical model are in a good agreement with those obtained from SimMechanics model. Finally, the Root Mean Square error is calculated between the analytical model and the results obtained from the simulation and experimental study.

Forecasting Gold Price Changes: Application of an Equipped Artificial Neural Network

R Hafezi; A Akhavan

Amirkabir International Journal of Modeling,Identification,Simulation and Control, Volume 50, Issue 1, Pages 71-82
DOI: 10.22060/miscj.2018.13508.5074

The forecast of fluctuations of prices is the major concern in financial markets. Thus, developing an accurate and robust forecasting decision model is critical for investors. As gold has shown a special capability to smooth inflation fluctuations, governors use gold as a price controlling lever. Thus, more information about future gold price trends will help make the firm decisions. This paper attempts to propose an intelligent model founded by artificial neural networks (ANNs) to project future prices of gold. The proposed intelligent network is equipped with a meta-heuristic algorithm called BAT algorithm to make ANN capable of following fluctuations. The designed model is compared to that of a published scientific paper and other competitive models such as Autoregressive Integrated Moving Average (ARIMA), ANN, Adaptive Neuro-Fuzzy Inference System (ANFIS), Multilayer Perceptron (MLP) Neural Network, Radial Basis Function (RBF) Neural Network and Generalized Regression Neural Networks (GRNN). In order to evaluate model performance, Root Mean Squared Error (RMSE) was employed as an error index. Results show that the proposed BAT-Neural Network (BNN) outperforms both conventional and modern forecasting models.

Presenting a Model for Multiple-Step-Ahead-Forecasting of Volatility and Conditional Value at Risk in Fossil Energy Markets

E Mohammadian Amiri; S. B Ebrahimi

Amirkabir International Journal of Modeling,Identification,Simulation and Control, Volume 50, Issue 1, Pages 83-94
DOI: 10.22060/miscj.2018.13473.5073

Fossil energy markets have always been known as strategic and important markets. They have a significant impact on the macro economy and financial markets of the world. The nature of these markets is accompanied by sudden shocks and volatility in the prices. Therefore, they must be controlled and forecasted using appropriate tools. This paper adopts the Generalized Auto Regressive Conditional Heteroskedasticity (GARCH)-type models, Exponential Smoothing (ES)-type models, and classic model in order to multiple-step-ahead forecast volatility, Value at Risk, and Conditional Value at Risk of Brent oil and natural gas in two different estimation window lengths, respectively. To evaluate the accuracy of the aforementioned models, eight different loss functions are utilized. There are a lot of financial terms in this the noted part. So, it’s comprehensible for financial person and etc. Therefore, the HWES model is proposed to multiple-step-ahead forecast functions as a verb.

Adaptive attitude controller of a reentry vehicles based on Back-stepping Dynamic inversion method

A Mohseni; F Fani Saberi; M Mortazavi

Amirkabir International Journal of Modeling,Identification,Simulation and Control, Volume 50, Issue 1, Pages 95-106
DOI: 10.22060/miscj.2017.12907.5048

This paper presents an attitude control algorithm for a Reusable Launch Vehicle (RLV) with a low lift/drag ratio (L/D 0.5), in presence of external disturbances, model uncertainties, control output constraints and the thruster model. The main novelty of the proposed control strategy is a new combination of the attitude control methods including backstepping, dynamic inversion, and adaptive control methods which will be called Backstepping-Dynamic inversion-Adaptive (B.D.A) method. In the proposed method, a single control variable is considered as the bank angle while the angle of the attack and the side slip angle will be stabilized in their inherent value. The purpose of this control is the attitude control of the vehicle to track the commanded bank angle and keep the vehicle in the desired trajectory. Lyapunov stability analysis of the closed-loop system will be performed to guaranty the stability of the vehicle in the presence of constraints. Performance of the controller will be evaluated based on six Degrees of Freedom (6-DOF) model of the re-entry capsule. Also, the results of the proposed control algorithm will be compared with the Backstepping Dynamic inversion (B.D) control method.

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