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The error of inertial navigation systems increase versus time, therefore for achieving higher accuracy specially in long time navigations we have to use an aiding system. Global positioning system is the best aiding system in this case. In this paper we first simulate a GPS and INS; Then simulate tightly integration and finally review adaptation method of Kalman Filtering a Fuzzy adaptive Kalman filter is proposed in which adaptation is accomplished by adaptive tuning of covariance matrix of measurement noise (R) and process noise (Q). We have achieved adaptive tuning using Fuzzy systems and Covariance - Matching techniques .The results show that the adaptive fuzzy integration of GPS and INS would lead to better performance comparing to the usual methods of integration in which both R and Q matrices are constant.

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Today, policymakers and economists use widely rational expectations (RE) in monetary, financial and regulatory policies to improve their country economic performance. In some of the pertinent models to these policies, expectations have been formed by assuming rationality and full information on economics. Indeed, economic agents have no perfect information about some parameters of these models. These unknown parameters can be estimated in the form rational expectations during learning process. In this research, the impact of government policies on the inflation rate has been modeled on the basis of rational expectations under learning process. Data has been gathered from Central Bank of Iran (CBI) and Iran’s economic development plans over the period 1989-2009. Results show that current inflation in the country originates mainly from economy structure and government policies, so share of public inflationary expectations is negligible. In addition, the learning process in Iran will converge to rational expectations, thus government policies for reducing inflation and increasing employment are inefficient. It is recommended that government adopt unanticipated and sudden policies to be effective its plans.

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This paper proposes a new hierarchical identification method for fractional-order systems. In this method, a SISO (single input, single output) state space model has been considered in which parameters and also state variables should be estimated.  By using a linear transformation and a shift operator, the system will be transformed into a form appropriate for identification of a fractional-order system. Then, the unknown parameters will be identified through a recursive least squares method and the states will be estimated using a fractional order Kalman filter. This identification method is based on the hierarchical identification principle that reduces the computational burden and is easy to implement on computer. The promising performance of the proposed method is verified using two stable fractional-order systems.    

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In this paper, LQG/LTR controller is designed for attitude control of the geostationary satellite at nominal mode. Usage actuator in this paper is the reaction wheel and control torque is determined by the LQR regulator. Usage sensors in this article are sun and earth sensors and EKF are used for estimation of noisy states. LQR controller signal has good performance, if all system's states are considered in system output feedback. But this method is ideal and does not include model noise and sensors noise. Therefore, LQG and LQG/LTR controllers are designed based on the estimated states, and are compared with LQR controller. Controllers gain coefficients are obtained based on linearization about working point. It caused to robustness and similarity of LQG and LQG/LTR response. The results show that control overshoot of LQR is greater than the others.

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In rocket systems, the re-entry speed to atmosphere is very high which leads to compression of air molecules and appearance of strong bow shock waves in the leading edge; consequently, this yields aerodynamic heating. Using ablating-dispensing materials on the leading edge surfaces, it is important to accurately determine heat flux on these moving boundaries. Measuring heat flux directly is very difficult or impossible in some situations. In the present study, the online Kalman filtering is used to determine heat flux accurately. Since the heat flux is estimated in online (non-iterative) fashion, the optimum location of temperature sensors can be effectively determined. In addition, the results of this study can be used to design heat flux sensors. In this paper, the optimum locations of three temperature sensors are calculated on the basis that the disturbances occur due to burning of sensors are reduced. More robust solutions are obtained for heat flux on the ablating surfaces.

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Dynamic model identification and state variables estimation from the corrupted measurement data have been attracted much research efforts during the recent years. In this way, Kalman and H-infinity filters have been increasingly used to estimate the parameters individually. In this paper, a mixed kalman-H_∞ filter is designed in an innovative approach using a multi-objective optimization method. It is desired to simultaneously employ the advantages of both filters to minimize both the root-mean squared errors and the upper bounds limit of estimation errors associated with Kalman and H-infinity filters, respectively. Some Pareto optimum design points are presented for two case studies from which trade-off optimum design points can be simply selected.

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This paper presents the inverted PID control of a quadrotor based on the experimentally measured sensors and actuators’ specifications. The main goal is the control and closed loop simulation of a quadrotor using inverted PID algorithm. First, a nonlinear model of quadrotor is derived using Newton-Euler equations. To have a more realistic simulation a setup were designed and developed to measure the sensors noise performance as well as the actuators’ dynamics. The setup involves a platform that two brushless motors mounted at the ends and rotates on a shaft. The platform attitude is measured using the MEMS sensors attached to it. A Kalman filter was used to reduce the sensors noises effect. Results demonstrate good performance for Kalman filter and the controller.

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The accurate, correct, and quick calculation of vehicle longitudinal velocity during braking plays a vital role in the precise operation of Anti-lock Brake System (ABS). Therefore, different researches have been conducted in the field of vehicle longitudinal velocity estimation. But, most these researches have been faced with a problem so called using braking torque as a known input to an estimator. These researches have addressed the issue while measuring the braking torque is not easy and needs expensive and additional sensors which causes the increase of costs and also requires more attention to maintenance and repair problems. In this paper, two approaches, Unknown Input Iterated Extended Kalman Filter (UIIEKF) and Modified Nonlinear Adaptive Filter (MANF) are proposed in order to estimate vehicle longitudinal velocity so that they do not need a braking torque and both methods have acceptable accuracy. The main difference between these two approaches is that the UIIEKF requires the dynamic model of vehicle motion during the braking process to estimate the longitudinal velocity while the MANF is model-free. Different aspects of both methods are analyzed by experimental tests on the vehicle and finally advantages and disadvantages of the both methods are compared.

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The Stewart platform with six degree of freedom (three translational and three rotational motions) consists of two rigid bodies, lower plate (base) and upper one (mobile). These two bodies are connected together by six extensible legs between three pairs of joints on each of the bodies. This platform can be used to isolate the top plate of the platform and its payload from the applied motions to the base. Since the passive isolation methods are not effective in elimination of the high amplitude (and usually) low frequency motions, this paper practically investigates the possibility of using the 6DOF Stewart platform as an active vibration isolator. In this study, a Stewart platform was designed and constructed based on electric actuators (servo-motors). And then it was practically utilized to isolate its top plate from the applied pitch and roll rotations to the base plate. MEMS sensors including two accelerometers and one rate gyro along with Kalman filter and kinematic relations were utilized for measuring the pitch and roll motions. A PI controller was implemented to keep the top plate at level position using the MEMS sensors installed on the bottom plate. The experimental results indicated that the platform can effectively isolate the pitch and roll motions while the frequency of these motions is in the working speed range of the electric actuators.

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The Unscented Kalman filter (UKF) is the popular approach to estimate the recursive parameter of nonlinear dynamical system corrupted with Gaussian and white noises. Also, it has been applied to train the weights of the multi-layered neural network (MNN) models. The Group method of data handling (GMDH)-type neural network is one of the most widely used neural networks which has high capacity in modeling of the complex data. In many researches, different approaches are used in training of neural networks in terms of associated weights or coefficients, such as singular value decomposition, and genetic algorithms. In this paper, the unscented Kalman filter is used to train the parameters of GMDH-type neural network when the experimental data are deterministic. The effectiveness of GMDH-type neural network with UKF algorithm is demonstrated by the modeling of the using a table of the multi input-single output experimental data. The simulation result shows that the UKF-based GMDH algorithm perform well in modeling of nonlinear systems in comparison with the results of using traditional GMDH-type neural network and is more robust against the model and measurement uncertainty.

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Nowadays, different methods are used to calculate the capital stock in Iran. For example, the Central Bank uses permanent inventory method (PIM) to calculate the capital stock, however the researchers have used the production function method or the method of the ratio of capital to production in different periods. The present study aims at estimating the capital stock in Iran’s economic sectors. By comparing the results of permanent inventory method (PIM) and production function method, it is observed that the estimations by production function method are more suitable due to the data limitations. The same method was applied for the U.S. regarding data limitations and estimations were compared with the U.S capital stock; and once again the production function method was selected. It is found that the depreciation rates in agriculture, industry and mining, building, services, oil and gas, and water and electricity are 5.8, 4.9, 7.8, 3.5, 6.3, and 4.1 per cent, respectively. In order to make the depreciation rate more reliable, these rates were compared with the similar researches in Iran and a close relationship was found among them  

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In this paper, an Extended Kalman Filter (EKF) and a model-dependent nonlinear controller over network using the separation principle for Low Earth Orbit (LEO) satellite Attitude Determination and Control Subsystem (ADCS) have been designed. In this context, according to the satellites development trend, ADCS architecture for a broad class of LEO satellites is proposed to stabilize and achieve mission objectives such as precision attitude determination and pointing. This architecture is a Networked Control System (NCS) used to establish connection and communication among control components including sensors, actuators and onboard processors, as well as to share data with other subsystems. Then, by modeling all components of the system, and considering the network effects as a bounded disturbance, the control system is designed to compensate of these effects. For this purpose, estimation and control algorithms including EKF and a model-dependent nonlinear controller is designed such that in addition to achieve desired system performance, the stability of each of them is guaranteed. Afterwards, the nonlinear dynamics model of the satellite in terms of quaternion parameters and angular velocities is presented, and by expression of the separation principle for nonlinear observer and controller design, their convergence and exponential stability conditions based on linearized model of satellite are derived. Proof of theorem shows that the closed-loop system continuously maintained satellite attitude in the specified accuracy range. Finally, simulation results obtained from applying the designed observer and controller on the active satellite in orbit demonstrates the efficiency of the proposed design.

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The purpose of this paper is design, construction and the control of a two-wheel self-balancing robot. For this purpose firstly, a literature study is carried out on the history of manufactured self-balancing robots and the researches which have been done so far in this area are reported. In addition, the robot chassis with consideration of the size and material is analyzed; and the dynamic equations of the robot are computed according to the designed chassis. Then, the robot inertial parameters are measured through different experimental tests and these parameters are used in the equations. Also, the derived equations are simplified and the transfer functions are evaluated for considering the stability of the robot. In this self-balancing robot, the simplified Kalman and complementary filters are used for identifying of the bias angle from the vertical position by combination of data obtained from accelerometer and gyroscope sensors. The PID controller and the robot transfer functions are simulated in MATLAB software. Then, the controller gains are obtained for the stability of the constructed robot. These gains are computed by PID tuning toolbox of MATLAB software as well as theoretically, and the results in each method have been compared with each other. Finally, the robot control electronic circuit is designed for analyzing the results through AVR microcontroller, while angle identification sensor is used.

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The short–term inflation dynamics and its cyclical interactions with real economic variables are basic issues in the context of monetary policies analysis. This study investigates and estimates the hybrid new Keynesian Phillips curve for Economy of Iran during 1971-2008. On this curve, the effective variables on current inflation would be future inflation, lagged inflation and GDP gap. This paper makes use of three Kalman, Hodrick- Prescott and band-pass filters to estimate GDP gap. There is a structural break in 1979 due to victory of the Islamic Revolution in Iran. Findings indicate that GDP gap has a significant positive impact on current inflation, which means the effectiveness of real variables, besides monetary policies, on inflation in the long-run. Our findings are consistent with other Phillips curve models, which confirm the effective role of output gap on current inflation. In addition, the coefficients of the expected inflation and lagged inflation variables are statistically significant, which indicate that firms look forward and backward in setting prices, but coefficient of expected inflation variable is higher than that of lagged inflation, means that firms pay more attention to the expected inflation in setting current prices. The evaluation tests indicate the accuracy and reliability of models.  

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This paper presents a completely practical control approach for quadrotor drone. Quadrotor is modelled using Euler-Newton equations. For stabilization and control of quadrotor a classic PID controller has been designed and implemented on the plant and a fuzzy controller is used to adjust the controller parameters. Considering that quadrotor is a nonlinear system, using classic controllers for the plant is not effective enough. Therefor using fuzzy system which is a nonlinear controller is effective for the nonlinear plant. According to the desire set point, fuzzy system adjusts the controller gain values to improve the performance of quadrotor and it leads to better results than classical PID controller. To study the performance of fuzzy PID controller on attitude control of the system, a quadrotor is installed to the designed stand. The system consists of accelerometer and gyroscope sensors and a microcontroller which is used to design fuzzy PID attitude controller for the quadrotor. Considering that the experimental data has lots of errors and noises, Kalman filter is used to reduce the noises. Finally using the Kalman filter leads to better estimation of the quadrotor angle position and the fuzzy PID controller performs the desired motions successfully.

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This paper presents the control of a quadrotor using nonlinear approaches based on the experimentally measured sensors data. The main goal is the control and closed loop simulation of a quadrotor using feedback linearization and sliding mode algorithms. First, a nonlinear model of quadrotor is derived using Newton-Euler equations. To have a more realistic simulation the sensors noise performance were measured using a setup. sensors data was measured under on engines. Since the experimental data for sensor had error and noise, a Kalman filter was used to reduce sensors noise effect. Results demonstrate good performance for Kalman filter and controllers. Results showed that feedback linearization and sliding mode controllers performance was good but angles changes were smoother on feedback linearization controller. With increasing uncertainty, feedback linearization performance was away desired mode from this aspect The time to reach the goal situation while increasing uncertainty was no significant impact on the performance of sliding mode controller.Thus feedback linearization controller added PID is Appropriate to Maintain the quadrotor attitude while sliding mode controller has better performance to angles change and transient situations.

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In this paper, a modified linear-quadratic-Gaussian (MLQG) optimal control algorithm is proposed for controlling the seismic response of frame structures. Environmental loads (e.g., earthquakes) at the moment of calculation and exertion of control forces to structures, can not be measured. So these loads are not included in the conventional control algorithms, such as linear quadratic regulator and linear-quadratic-Gaussian control. Therefore the command of LQG optimal controller is merely a proportional feedback of estimated state of structure at the moment of exertion. This state approximation is performed by optimal state stimator or Kalman filter. In the proposed control algorithm, using a new variable, including control force andearthquake force, acceleration of gound motion, which is non-measurable duting exertion of control force, is considered in the state space equation of motion and also in both of Kalman Filter estimator and the optimal regulator. According to the proposed control algorithm, two ways are selected. So first command control are sum of the control force and ratios of the estimated state and measurement output of sensors, which are obtained and used in previous time step. The estimated state of system, used in the first command control, is calculated by the conventional and knownKalman Filter. but in second strategy of control, First, the Kalman Filter estimator is modified based on new state space equations, and then the estimated state of structure obtained from it, is used for calculation of command control. Numerical simulation of a seven-storey structure with active control system under several far-fault and near-fault earthquakes are performed to show effectiveness of two proposed controls on mitigation of structural responses and compare to those of a uncontrolled structure and a structure controlled with conventional control. Also sensitivity of some perforemance measures for controllers are investigated against changes of some controlling and perturbation parameters of systems or uncertainties. The alalysis results demonstrate that control performance of the proposed controllers, specially the second one, are better and also stable and robust under variations of uncertainties. So that the greatest reduction in maximum displacement (even up to 80 percent) compared to uncontrolled displacement of structure and meanwhile, very low energy consumption are attained by the second proposed control strategy.but in second strategy of control, First, the Kalman Filter estimator is modified based on new state space equations, and then the estimated state of structure obtained from it, is used for calculation of command control. Numerical simulation of a seven-storey structure with active control system under several far-fault and near-fault earthquakes are performed to show effectiveness of two proposed controls on mitigation of structural responses and compare to those of a uncontrolled structure and a structure controlled with conventional control. Also sensitivity of some perforemance measures for controllers are investigated against changes of some controlling and perturbation parameters of systems or uncertainties. The alalysis results demonstrate that control performance of the proposed controllers, specially the second one, are better and also stable and robust under variations of uncertainties. So that the greatest reduction in maximum displacement (even up to 80 percent) compared to uncontrolled displacement of structure and meanwhile, very low energy consumption are attained by the second proposed control strategy.

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In this paper, we used a non-singular backstepping terminal sliding mode control approach to the unmanned aerial vehicle (quadrotor). In the first step, the governing dynamical equations were obtained based on the quadrotor considering all the effective parameters. The controller objective is limited to obtaining proper tracking of the desired positions (x, y, z) and the yaw angle (ψ), as well as maintaining the stability of the roll and pitch angles despite the presence of external disturbances. Controlling methods require complete information about system states that may be limited in practice. Even if all system conditions are available, it is interfered by noise, and also large number of applier sensors to measure states, makes the entire system more complex and costly. For this purpose, the Extended Kalman Filter (EKF) has been used as an observer. The extended Kalman filter is used as a speed observer and estimator of external disturbances such as wind force. Therefore, the use of a controller-observer is suggested to estimate the effects of external disturbances in order to compensate for them. The design method is based on the stability of Lyapunov. Simulation results show the promising performance and suitability of the observer-controller.

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In order to use and control Shape Memory Alloy (SMA) actuators, it is essential to measure its state variables to be used as the feedback in the control loop. The wire temperature is one of critical state variables need to be fed back. However, measuring this variable is difficult and usually contains some noises and delay. Therefore, it is desirable to estimate this variable instead of measuring it. Thermoelectric model is one of the most common models used to estimate the SMA wire temperature. This model calculates the SMA wire temperature based on its input electric current. In this paper, first three unknown parameters of thermoelectric model are estimated using Extended Kalman filter (EKF) and the wire temperature is calculated based on the identified model. The parameter estimation and temperature calculation are performed on a practical SMA actuator. Then, in order to eliminate the effects of environmental disturbances and the thermoelectric model inaccuracies, the temperature is estimated using EKF. In this method, all measurable data such as the input current, the strain and stress of the SMA wire are used in the temperature estimation. The estimator combines the information obtained from both thermoelectric and Brinson models and the measurement data. This method is used for online temperature estimation of the SMA wire on a practical SMA actuator. The results show that the estimated temperature matches the actual wire temperature with high precision. Furthermore, the temperature estimation using EKF is more accurate than the estimates of the thermoelectric model.

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