Development of a fault tolerant flight control system for a UAV

Abstract

It 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.