LEE- Uçak ve Uzay Mühendisliği-Doktora
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Yazar "Hacızade, Cengiz" ile LEE- Uçak ve Uzay Mühendisliği-Doktora'a göz atma
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ÖgeDevelopment of a fault tolerant flight control system for a UAV(Graduate School, 2022-08-12) Vural, Sıtkı Yenal ; Hacızade, Cengiz ; 511082105 ; Aeronautics and Astronautics EngineeringIt is important for the unmanned aerial vehicles that are used for various purposes including military missions, surveillance, security, atmospheric data gathering etc. to be autonomous and control systems that can work flawless even when faults are present are needed for such systems. The methods for achieving fault tolerant control are under development and are still not used much in practical applications however developing a fault tolerant control system for all types of applications including aerial vehicle control systems seems to be an ultimate control aim. In this thesis, developing a fault tolerant control system for a UAV is aimed mainly for the given reasons. In the study, active and passive control methods and Kalman filter based fault detection and isolation techniques are used together to build a fault tolerant controller for an unmanned aerial vehicle. Also, a hybrid controller including both active and passive fault tolerant controllers is developed in order to benefit from their different characteristics in dealing with faults. Kalman filter based fault detection and isolation algorithm which can be used to detect and isolate the faults in sensors and actuators , to determine the source of the fault and to find the unbiased sensor measurements is developed in the study and its effectiveness is shown through simulations. To detect and isolate the faults occuring in sensors/actuators Kalman filter innovation sequence analysis is used. On the other hand, to determine the source of the fault, Doyle-Stein method based Kalman filter and to rectify the biased sensor measurements Kalman filter insensitive to measurement failures are employed in the study. Unmanned aerial vehicle model is used to simulate the fault cases and to show the successfulness of the built system. One of the methods used in the thesis to build a fault tolerant controller is the active fault tolerant control method. In this method, fault detection and isolation technique is used to determine the faults occuring in the system and to find the severity of the fault and this info is used to reconfigure the controller. The actuators would not work effectively if hydraulic pressure decrease, partial blockage of a control valve, voltage reduction in electrical servosystems etc. occur in the system. In these cases, the effectiveness of the actuators decrease. The change in the mentioned actuator effectiveness can be represented in the system as the control effectiveness factor related with the actuator. In the study, two-stage Kalman filter is used to estimate the changes occurring in actuator control effectiveness factors which corresponds to the faults occurring in actuators. With the help of two-stage Kalman filter in which a second bias-estimation filter is used, the bias in the system can be estimated and the best state estimates can still be found. This type of filter,different than the augmented state filter in which all parameters are estimated in one stage, has the advantage of reducing calculation burden and thus giving results in small time period. In short, two-stage Kalman filter consists of a bias-free state estimator that estimates the states, a bias estimator to estimate the bias, the residual vector and the covariance matrix calculation equations and coupling equations that are used to relate the filters and update the bias free state estimator. In the simulations, the faults in actuators are modelled as changes in control distribution matrix B and these changes are tried to be estimated using two-stage Kalman filter and the reconfiguration of the controller is done using the determined new B matrix. In this method, one needs to determine when the fault occurs in the system and to decide when to reconfigure the controller. To that end, to determine the fault occuring in the system, weighted sum-squared bias estimate – WSSBE- fault detection algortihm is used. This algorithm uses the statistical variables that are based on bias- control effectiveness factor- estimates. The ratio of the square of the bias estimate to its covariance matrix is summed in a predetermined window length which corresponds to an iteration period. The resultant value should be between determined theoretical values if there is no fault in the system. In the decision-gain update algorithm, convergence of the control effectiveness factors is important and mean value of the estimates can be used for this purpose. In the study, using the mentioned methods, control of heading and altitude in cases where actuator faults are present in aileron and elevators are realized. It is shown through simulations that the unmanned aerial vehicle can be effectively controlled using the active fault tolerant controller despite the decrease in actuator control effectiveness factors which corresponds to effectivity loss in actuator controls. Another method used in the thesis to design a fault tolerant controller is passive fault tolerant control method. In this method, the pre-designed controller is relied upon in dealing with the faults occuring in the system. Thus, fault detection and isolation and controller reconfiguration are not needed in this scheme. Dynamic inversion technique is used together with robust integral of the signum of the error- RISE- method to design an asymptotic tracking passive fault tolerant controller that has the capability to cope with faults. The faults occuring in actuators are modelled as parametric uncertainties in control distribution matrix B. To build an asymptotic model following passive controller, control inputs that decrease the difference between model and system should be found. For this purpose, Lyapunov type functions are used and controller constants are determined as done in similar studies. In simulations, in longitudinal model, forward velocity, pitch rate and in lateral model, yaw rate and roll rate are the main states that are controlled. Using asymptotic tracking controller system that helps in maintaining control of mentioned states, an outer loop is also built which aranges heading and altitude changes by tuning reference model inputs using fedback state values from the main system. Simulations done using both longitudinal and lateral models show that the designed passive controller is effective in controlling the unmanned aerial vehicle at times when faults are present in actuators. Hybrid control method is also used in the study to build a fault tolerant controller. This method uses active and passive controllers at different times to achieve fault tolerant control. This way, at times when the fault detection and isolation algortihm based on two-stage Kalman filter determines the fault but still needs time to find the severity of the fault, passive fault tolerant controller can be used and the system can be kept under control continously. Reconfiguration can later be done after the fault severity is determined. As passive and active controllers are shown to be effective in controlling the unmanned aerial vehicle, hybrid control can be used for controlling faulty plants continously. In simulations done using lateral model of the unmanned aerial vehicle,it is shown that the hybrid controller is successful in keeping the vehicle under control and tracking the heading inputs at times when actuator faults are present. To achieve this result, active and passive controllers are used at different times after fault occurrence. In conclusion, a fault tolerant controller is designed for the unmanned aerial vehicle and it is shown that it can be effectively used when actuator faults – actuator control effectiveness loss cases corresponding to the problems in actuators- and/or sensor faults are present in the study.
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ÖgeDevelopment of single-frame methods aided kalman-type filtering algorithms for attitude estimation of nano-satellites(Graduate School, 2021-08-20) Çilden Güler, Demet ; Hacızade, Cengiz ; Kaymaz, Zerefşan ; 511162104 ; Aeronautics and Astronautics Engineering ; Uçak ve Uzay MühendisliğiThere is a growing demand for the development of highly accurate attitude estimation algorithms even for small satellite e.g. nanosatellites with attitude sensors that are typically cheap, simple, and light because, in order to control the orientation of a satellite or its instrument, it is important to estimate the attitude accurately. Here, the estimation is especially important in nanosatellites, whose sensors are usually low-cost and have higher noise levels than high-end sensors. The algorithms should also be able to run on systems with very restricted computer power. One of the aims of the thesis is to develop attitude estimation filters that improve the estimation accuracy while not increasing the computational burden too much. For this purpose, Kalman filter extensions are examined for attitude estimation with a 3-axis magnetometer and sun sensor measurements. In the first part of this research, the performance of the developed extensions for the state of art attitude estimation filters is evaluated by taking into consideration both accuracy and computational complexity. Here, single-frame method-aided attitude estimation algorithms are introduced. As the single-frame method, singular value decomposition (SVD) is used that aided extended Kalman filter (EKF) and unscented Kalman filter (UKF) for nanosatellite's attitude estimation. The development of the system model of the filter, and the measurement models of the sun sensors and the magnetometers, which are used to generate vector observations is presented. Vector observations are used in SVD for satellite attitude determination purposes. In the presented method, filtering stage inputs are coming from SVD as the linear measurements of attitude and their error covariance relations. In this step, UD is also introduced for EKF that factorizes the attitude angles error covariance with forming the measurements in order to obtain the appropriate inputs for the filtering stage. The necessity of the sub-step, called UD factorization on the measurement covariance is discussed. The accuracy of the estimation results of the SVD-aided EKF with and without UD factorization is compared for the estimation performance. Then, a case including an eclipse period is considered and possible switching rules are discussed especially for the eclipse period, when the sun sensor measurements are not available. There are also other attitude estimation algorithms that have strengths in coping well with nonlinear problems or working well with heavy-tailed noise. Therefore, different types of filters are also tested to see what kind of filter provides the largest improvements in the estimation accuracy. Kalman-type filter extensions correspond to different ways of approximating the models. In that sense, a filter takes the non-Gaussianity into account and updates the measurement noise covariance whereas another one minimizes the nonlinearity. Various other algorithms can be used for adapting the Kalman filter by scaling or updating the covariance of the filter. The filtering extensions are developed so that each of them is designed to mitigate different types of error sources for the Kalman filter that is used as the baseline. The distribution of the magnetometer noises for a better model is also investigated using sensor flight data. The filters are tested for the measurement noise with the best fitting distribution. The responses of the filters are performed under different operation modes such as nominal mode, recovery from incorrect initial state, short and long-term sensor faults. Another aspect of the thesis is to investigate two major environmental disturbances on the spacecraft close enough to a planet: the external magnetic field and the planet's albedo. As magnetometers and sun sensors are widely used attitude sensors, external magnetic field and albedo models have an important role in the accuracy of the attitude estimation. The magnetometers implemented on a spacecraft measure the internal geomagnetic field sources caused by the planet's dynamo and crust as well as the external sources such as solar wind and interplanetary magnetic field. However, the models that include only the internal field are frequently used, which might remain incapable when geomagnetic activities occur causing an error in the magnetic field model in comparison with the sensor measurements. Here, the external field variations caused by the solar wind, magnetic storms, and magnetospheric substorms are generally treated as bias on the measurements and removed from the measurements by estimating them in the augmented states. The measurement, in this case, diverges from the real case after the elimination. Another approach can be proposed to consider the external field in the model and not treat it as an error source. In this way, the model can represent the magnetic field closer to reality. If a magnetic field model used for the spacecraft attitude control does not consider the external fields, it can misevaluate that there is more noise on the sensor, while the variations are caused by a physical phenomenon (e.g. a magnetospheric substorm event), and not the sensor itself. Different geomagnetic field models are compared to study the errors resulting from the representation of magnetic fields that affect the satellite attitude determination system. For this purpose, we used magnetometer data from low Earth-orbiting spacecraft and the geomagnetic models, IGRF and T89 to study the differences between the magnetic field components, strength, and the angle between the predicted and observed vector magnetic fields. The comparisons are made during geomagnetically active and quiet days to see the effects of the geomagnetic storms and sub-storms on the predicted and observed magnetic fields and angles. The angles, in turn, are used to estimate the spacecraft attitude, and hence, the differences between model and observations as well as between two models become important to determine and reduce the errors associated with the models under different space environment conditions. It is shown that the models differ from the observations even during the geomagnetically quiet times but the associated errors during the geomagnetically active times increase more. It is found that the T89 model gives closer predictions to the observations, especially during active times and the errors are smaller compared to the IGRF model. The magnitude of the error in the angle under both environmental conditions is found to be less than 1 degree. The effects of magnetic disturbances resulting from geospace storms on the satellite attitudes estimated by EKF are also examined. The increasing levels of geomagnetic activity affect geomagnetic field vectors predicted by IGRF and T89 models. Various sensor combinations including magnetometer, gyroscope, and sun sensor are evaluated for magnetically quiet and active times. Errors are calculated for estimated attitude angles and differences are discussed. This portion of the study emphasizes the importance of environmental factors on the satellite attitude determination systems. Since the sun sensors are frequently used in both planet-orbiting satellites and interplanetary spacecraft missions in the solar system, a spacecraft close enough to the sun and a planet is also considered. The spacecraft receives electromagnetic radiation of direct solar flux, reflected radiation namely albedo, and emitted radiation of that planet. The albedo is the fraction of sunlight incident and reflected light from the planet. Spacecraft can be exposed to albedo when it sees the sunlit part of the planet. The albedo values vary depending on the seasonal, geographical, diurnal changes as well as the cloud coverage. The sun sensor not only measures the light from the sun but also the albedo of the planet. So, a planet's albedo interference can cause anomalous sun sensor readings. This can be eliminated by filtering the sun sensors to be insensitive to albedo. However, in most of the nanosatellites, coarse sun sensors are used and they are sensitive to albedo. Besides, some critical components and spacecraft systems e.g. optical sensors, thermal and power subsystems have to take the light reflectance into account. This makes the albedo estimations a significant factor in their analysis as well. Therefore, in this research, the purpose is to estimate the planet's albedo using a simple model with less parameter dependency than any albedo models and to estimate the attitude by comprising the corrected sun sensor measurements. A three-axis attitude estimation scheme is presented using a set of Earth's albedo interfered coarse sun sensors (CSSs), which are inexpensive, small in size, and light in power consumption. For modeling the interference, a two-stage albedo estimation algorithm based on an autoregressive (AR) model is proposed. The algorithm does not require any data such as albedo coefficients, spacecraft position, sky condition, or ground coverage, other than albedo measurements. The results are compared with different albedo models based on the reference conditions. The models are obtained using either a data-driven or estimated approach. The proposed estimated albedo is fed to the CSS measurements for correction. The corrected CSS measurements are processed under various estimation techniques with different sensor configurations. The relative performance of the attitude estimation schemes when using different albedo models is examined. In summary, the effects of two main space environment disturbances on the satellite's attitude estimation are studied with a comprehensive analysis with different types of spacecraft trajectories under various environmental conditions. The performance analyses are expected to be of interest to the aerospace community as they can be reproducible for the applications of spacecraft systems or aerial vehicles.