A new parameter estimation method is proposed in the framework of composite model reference adaptive control for improved parameter convergence without persistent excitation. The regressor filtering scheme is adopted to perform the parameter estimation with signals that can be obtained easily. A new framework for residual signal construction is proposed. The incoming data are first accumulated to build the information matrix, and then its quality is evaluated with respect to a chosen measure to select and store the best one. The information matrix is built to have full rank after sufficient but not persistent excitation. In this way, the exponential convergence of both tracking error and parameter estimation error can be guaranteed without persistent oscillation in the external command, which drives the system. Numerical simulations are performed to verify the theoretical findings and to demonstrate the advantages of the proposed adaptation law over the standard direct adaptation law.

A reactive three-dimensional maneuver strategy for a multirotor Unmanned Aerial Vehicle (UAV) is proposed based on the collision cone approach to avoid potential collision with a single moving obstacle detected by an onboard sensor. A Light Detection And Ranging (LiDAR) system is assumed to be mounted on a hexacopter to obtain the obstacle information from the collected point clouds. The collision cone approach is enhanced to appropriately deal with the moving obstacle with the help of a Kalman filter. The filter estimates the position, velocity, and acceleration of the obstacle by using the LiDAR data as the associated measurement. The obstacle state estimate is utilized to predict the future trajectories of the moving obstacle. The collision detection and obstacle avoidance maneuver decisions are made considering the predicted trajectory of the obstacle. Numerical simulations, including a Monte Carlo campaign, are conducted to verify the performance of the proposed collision avoidance algorithm.

A major challenge in homing guidance is that accurately predicting the time to go for missiles is generally difficult, which limits the practical application of a certain class of guidance laws with impact-time constraints or cooperative capabilities. To address this issue, a generalized method referred to as the time-to-go inversion guidance is presented, where the time to go is first specified as an explicit function and then the corresponding guidance law is inversely derived. By employing this new method, a variety of guidance laws and their globally accurate time-to-go solutions can be developed, regardless of whether the impact-angle constraint is considered or not. Different from all existing studies, the proposed method addresses the problem of time-to-go estimation without relying on any linearizing approximations or numerical iterations. To enhance the practicality of the proposed method, it is augmented with bias commands to result in either the impact time control guidance law or the impact time and angle control guidance law. Numerical simulation results demonstrate the higher accuracy, larger feasible solution region, and lower computational demand of the proposed guidance laws even for the missiles with nonconstant speeds.

To monitor large areas or simultaneously measure multiple points, multiple unmanned aerial vehicles (UAVs) must be flown in formation. To perform such flights, sensor information generated by each UAV should be shared via communications. Although a variety of studies have focused on the algorithms for formation flight, these studies have mainly demonstrated the performance of formation flight using numerical simulations or ground robots, which do not reflect the dynamic characteristics of UAVs. In this study, an onboard sensor information sharing system and formation flight algorithms for multiple UAVs are proposed. The communication delays of radiofrequency (RF) telemetry are analyzed to enable the implementation of the onboard sensor information sharing system. Using the sensor information sharing, the formation guidance law for multiple UAVs, which includes both a circular and close formation, is designed. The hardware system, which includes avionics and an airframe, is constructed for the proposed multi-UAV platform. A numerical simulation is performed to demonstrate the performance of the formation flight guidance and control system for multiple UAVs. Finally, a flight test is conducted to verify the proposed algorithm for the multi-UAV system.

This study presents a method to adjust the waypoints of an unpowered air vehicle to compensate for the influence of wind on the trajectory. A framework combining pre-flight waypoint planning and in-flight waypoint adjustment is proposed. In the offline planning phase, optimal trajectories under various wind profile combinations are generated by using a direct optimization method. Waypoints are extracted from the obtained trajectories for each wind condition. Then, deviations of each waypoint due to wind from the corresponding waypoint on the trajectory for a zero-wind case are obtained; these deviations are used to construct the models of waypoint deviation as functions of wind speed and direction via least-squares regression. In the online adjustment phase, the wind-compensated waypoint is computed using the waypoint deviation model and the estimated wind velocity. A nonlinear six degrees-of-freedom simulation, incorporating a guidance and control system and a realistic wind profile, is performed to demonstrate the effectiveness of the proposed waypoint management framework.

A reactive collision avoidance algorithm is proposed to enable safe autonomous flight in the presence of multiple dynamic obstacles. A position-controlled hexacopter equipped with a visual sensor obtaining obstacle information is considered as an Unmanned Aerial Vehicle (UAV) platform. The proposed method centers on the concept of bounding tube which intrinsically extends the static bounding box to incorporate forthcoming movement of the obstacles into the collision avoidance framework. The processing pipeline consists of separate components for each of the sequential tasks in obstacle sensing and tracking. Computation of a spherical bounding box for each obstacle is followed by discrete-time Kalman filtering for prediction of obstacle trajectory to detect potential collision. If the current course of UAV turns out highly likely to end in collision with any of the obstacles, the vehicle steers to an aiming point chosen from among the bundle of candidates produced by constructing a bounding tube that takes account of predicted obstacle motion. The bounding-tube-based aiming point generation extends seamlessly to the case with multiple moving obstacles through running in series with multi-obstacle track management that combines hierarchical clustering of sensory data points for obstacle identification and a simple geometric method for data association. Numerical simulations are conducted to verify the performance of the proposed collision avoidance algorithm.