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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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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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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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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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In this paper, a finite-time fault tolerant controller based on sliding mode algorithm, as a robust control method, is presented to control the attitude stabilization of the quadrotor system in the presence of actuator fault and uncertainty. The controller is designed based on the nonlinear model of a quadrotor and its stability analysis is performed according to the Lyapunov stability theorem. Also, regarding some weaknesses of MEMS sensors such as partly high noise and bias error, an extended Kalman filter is designed and implemented in order to merge sensors data and reduce the noise effect on the outputs. To validate the controller performance, the experimental tests is implemented on a full-scale quadrotor in real-time. The evaluation of the designed strategy is carried out in different scenarios, no fault in the actuators and a partial loss of effectiveness of an actuator as well as uncertainty in the quadrotor parameters. The experimental results reveal the superiority of the sliding mode tolerant strategy over feedback linearization in the presence of various faults and uncertainty effects.