Control Design and Performance Analysis for Autonomous Formation Flight Experimentss

Autonomous Formation Flight is a key approach for reducing greenhouse gas emissions and managing traffic in future high density airspace. Unmanned Aerial Vehicles (UAV\u27s) have made it possible for the physical demonstration and validation of autonomous formation flight concepts inexpensively and eliminates the flight risk to human pilots. This thesis discusses the design, implementation, and flight testing of three different formation flight control methods, Proportional Integral and Derivative (PID); Fuzzy Logic (FL); and NonLinear Dynamic Inversion (NLDI), and their respective performance behavior. Experimental results show achievable autonomous formation flight and performance quality with a pair of low-cost unmanned research fixed wing aircraft and also with a solo vertical takeoff and landing (VTOL) quadrotor

Control Design and Performance Analysis for Autonomous Formation Flight Experiments

Autonomous Formation Flight is a key approach for reducing greenhouse gas emissions and managing traffic in future high density airspace. Unmanned Aerial Vehicles (UAV’s) have made it possible for the physical demonstration and validation of autonomous formation flight concepts inexpensively and eliminates the flight risk to human pilots. This thesis discusses the design, implementation, and flight testing of three different formation flight control methods, Proportional Integral and Derivative (PID); Fuzzy Logic (FL); and NonLinear Dynamic Inversion (NLDI), and their respective performance behavior. Experimental results show achievable autonomous formation flight and performance quality with a pair of low-cost unmanned research fixed wing aircraft and also with a solo vertical takeoff and landing (VTOL) quadrotor

Automated Carrier Landing of an Unmanned Combat Aerial Vehicle Using Dynamic Inversion

Dynamic Inversion (DI) is a powerful nonlinear control technique which has been applied to several modern flight control systems. This research utilized concepts of DI in order to develop a controller to land an Unmanned Combat Aerial Vehicle (UCAV) on an aircraft carrier. The Joint Unmanned Combat Air System (J-UCAS) Equivalent Model was used as the test aircraft. An inner-loop DI controller was developed to control the pitch, roll, and yaw rate dynamics of the aircraft, while an outer-loop DI controller was developed to provide flight path commands to the inner-loop. The controller design and simulation were conducted in the MATLAB/Simulink environment. Simulations were conducted for various starting positions near the carrier and for varying wind, wind turbulence, and sea state conditions. In the absence of wind and sea state turbulence, the controller performed well. After adding wind and sea state turbulence, the controller performance was degraded. Future work in this area should include a more robust disturbance rejection technique to compensate for wind turbulence effects and a method of carrier motion prediction to compensate for sea state effects

Development of Robust Control Laws for Disturbance Rejection in Rotorcraft UAVs

Inherent stability inside the flight envelope must be guaranteed in order to safely introduce private and commercial UAV systems into the national airspace. The rejection of unknown external wind disturbances offers a challenging task due to the limited available information about the unpredictable and turbulent characteristics of the wind. This thesis focuses on the design, development and implementation of robust control algorithms for disturbance rejection in rotorcraft UAVs. The main focus is the rejection of external disturbances caused by wind influences. Four control algorithms are developed in an effort to mitigate wind effects: baseline nonlinear dynamic inversion (NLDI), a wind rejection extension for the NLDI, NLDI with adaptive artificial neural networks (ANN) augmentation, and NLDI with L1 adaptive control augmentation. A simulation environment is applied to evaluate the performance of these control algorithms under external wind conditions using a Monte Carlo analysis. Outdoor flight test results are presented for the implementation of the baseline NLDI, NLDI augmented with adaptive ANN and NLDI augmented with L1 adaptive control algorithms in a DJI F330 Flamewheel quadrotor UAV system. A set of metrics is applied to compare and evaluate the overall performance of the developed control algorithms under external wind disturbances. The obtained results show that the extended NLDI exhibits undesired characteristics while the augmentation of the baseline NLDI control law with adaptive ANN and L1 output-feedback adaptive control improve the robustness of the translational and rotational dynamics of a rotorcraft UAV in the presence of wind disturbances

Nonlinear Control Concepts for a UA

A reconfigurable flight control method is developed to be implemented on an Unmanned Aircraft (UA), a thirty percent scale model of the Cessna 150. This paper presents the details of the UA platform, system identification, reconfigurable controller design, development, and implementation on the UA to analyze the performance metrics. A Crossbow Inertial Measurement Unit provides the roll, pitch, and yaw accelerations and rates along with the roll and pitch. The 100-400 mini-air data boom from SpaceAge Control provides the airspeed, altitude, angle of attack, and the side slip angles. System identification is accomplished by commanding preprogrammed inputs to the control surfaces and correlating the corresponding variations at the outputs. A Single Network Adaptive Critic, which is a neural network-based optimal controller, is developed as part of a nonlinear flight control system. An online learning neural network is augmented to form an outer loop to reconfigure and supplement the optimal controller to guarantee a practical stability for the airplane. This paper also presents some simulations from the hardware-in-the-loop testing and concludes with an analysis of the flight performance metrics for the controller under investigation

Development and Implementation of New Nonlinear Control Concepts for a UA

A reconfigurable flight control method is developed to be implemented on an Unmanned Aircraft (UA), a thirty percent scale model of the Cessna 150. This paper presents the details of the UAV platform, system identification, reconfigurable controller design, development, and implementation on the UA to analyze the performance metrics. A Crossbow Inertial Measurement Unit provides the roll, pitch and yaw accelerations and rates along with the roll and pitch. The 100400 mini-air data boom from spaceage control provides the airspeed, altitude, angle of attack and the side slip angles. System identification is accomplished by commanding preprogrammed inputs to the control surfaces and correlating the corresponding variations at the outputs. A Single Network Adaptive Critic, which is a neural network based optimal controller, is developed as part of a nonlinear flight control system. An online learning neural network is augmented to form an outer loop to reconfigure and supplement the optimal controller to guarantee a practical stability for the airplane. This paper also presents some simulations from the hardware-in-the-loop testing and concludes with an analysis of the flight performance metrics for the controller under investigation

Fault Tolerance Analysis of L1 Adaptive Control System for Unmanned Aerial Vehicles

Trajectory tracking is a critical element for the better functionality of autonomous vehicles. The main objective of this research study was to implement and analyze L1 adaptive control laws for autonomous flight under normal and upset flight conditions. The West Virginia University (WVU) Unmanned Aerial Vehicle flight simulation environment was used for this purpose. A comparison study between the L1 adaptive controller and a baseline conventional controller, which relies on position, proportional, and integral compensation, has been performed for a reduced size jet aircraft, the WVU YF-22. Special attention was given to the performance of the proposed control laws in the presence of abnormal conditions. The abnormal conditions considered are locked actuators (stabilator, aileron, and rudder) and excessive turbulence. Several levels of abnormal condition severity have been considered. The performance of the control laws was assessed over different-shape commanded trajectories. A set of comprehensive evaluation metrics was defined and used to analyze the performance of autonomous flight control laws in terms of control activity and trajectory tracking errors. The developed L1 adaptive control laws are supported by theoretical stability guarantees. The simulation results show that L1 adaptive output feedback controller achieves better trajectory tracking with lower level of control actuation as compared to the baseline linear controller under nominal and abnormal conditions