This paper proposes a new hybrid flight control strategy for a quadcopter with a slung load to achieve a user-defined trajectory following. The high-fidelity coupled nonlinear model involving the quadcopter and slung load is obtained by integrating the nonlinear slung load model with the nonlinear quadcopter model. The quadcopter’s nonlinear model is derived using the Euler-Newton method, whereas the slung load model is obtained using the Lagrangian methodology. The hybrid flight control strategy for this quadcopter with a slung load to accomplish trajectory tracking is developed using a conventional three-loop flight control structure. This control structure uses both linear proportional-integral-derivative (LPID) and nonlinear PID (NLPID) controllers. Here, NLPID controllers are employed in the outer loop for accomplishing trajectory tracking, whereas LPID controllers are used in the inner and middle loops to maintain stability and attitude tracking, respectively. Furthermore, the proposed technique uses a dedicated linear proportional-derivative controller to track the relative slung load positions with respect to the center of the quadcopter, thus reducing the adverse effect of the slung load oscillations. The effectiveness of the new hybrid flight control strategy is verified through different 8 degree-of-freedom (DOF) nonlinear numerical simulations.

This paper presents the nonlinear six degrees of freedom dynamic modeling of a fixed wing micro air vehicle. The static derivatives of the micro air vehicle are obtained through the wind tunnel testing. The propeller effects on the lift, drag, pitching moment and side force are quantified through wind tunnel testing. The dynamic derivatives are obtained through empirical relations available in the literature. The trim conditions are computed for a straight and constant altitude flight condition. The linearized longitudinal and lateral state space models are obtained about trim conditions. The variations in short period mode, phugoid mode, Dutch roll mode, roll subsidence mode and spiral mode with respect to different trim operating conditions is presented. A stabilizing static output feedback controller is designed using the obtained model. Successful closed loop flight trials are conducted with the static output feedback controller.

The development of a fixed-wing nano air vehicle that fits inside a cube of 75 mm is described in this paper. A novel aircraft configuration that satisfies the static and dynamic stability and the mission performance requirements is developed. A performance index is formulated to identify a suitable airfoil as there does not exist a single airfoil that achieves the mission required specifications. Eppler-61 is identified as the suitable airfoil since the value of its performance index is maximum among other airfoils selected for the analysis. The static stability of the nano air vehicle is analyzed using the aerodynamic characteristics obtained from the wind tunnel tests. The outcomes of this analysis indicate that the vehicle is statically stable. The analysis also indicates significant aerodynamic cross-coupling between longitudinal and lateral aerodynamic coefficients. The dynamic stability analysis indicates that the nano air vehicle is dynamically stable. Further, the dynamic stability of the vehicle is validated by flight test results.

The plants of nano air vehicles (NAVs) are generally unstable, adversely coupled, and uncertain. Besides, the autopilot hardware of a NAV has limited sensing and computational capabilities. Hence, these vehicles need a single controller referred to as robust simultaneously stabilizing decoupling (RSSD) output feedback controller that achieves simultaneous stabilization (SS), desired decoupling, robustness, and performance for a finite set of unstable multi-input-multioutput adversely coupled uncertain plants. To synthesize an RSSD output feedback controller, a new method that is based on a central plant is proposed in this article. Given a finite set of plants for SS, we considered a plant in this set that has the smallest maximum v-gap metric as the central plant. Following this, the sufficient condition for the existence of a simultaneous stabilizing controller associated with such a plant is described. The decoupling feature is then appended to this controller using the properties of the eigenstructure assignment method. Afterward, the sufficient conditions for the existence of an RSSD output feedback controller are obtained. Using these sufficient conditions, a new optimization problem for the synthesis of an RSSD output feedback controller is formulated. To solve this optimization problem, a new genetic algorithm-based offline iterative algorithm is developed. The effectiveness of this iterative algorithm is then demonstrated by generating an RSSD controller for a fixed-wing NAV. The performance of this controller is validated through numerical and hardware-in-the-loop simulations.

A solution to the waypoint navigation problem for fixed wing micro air vehicles (MAV) is addressed in this paper, in the framework of integrated guidance and control (IGC). IGC yields a single step solution to the waypoint navigation problem, unlike conventional multiple loop design. The pure proportional navigation (PPN) guidance law is integrated with the MAV dynamics. A multivariable static output feedback (SOF) controller is designed for the linear state space model formulated in the IGC framework. The waypoint navigation algorithm handles the minimum turn radius constraint of the MAV. The algorithm also evaluates the feasibility of reaching a waypoint. Extensive non-linear simulations are performed on high fidelity 150 mm wingspan MAV model to demonstrate the potential advantages of the proposed waypoint navigation algorithm.

In this paper, an algorithm for Unmanned Aircraft Systems Traffic Management (UTM) for a finite number of unmanned aerial vehicles (UAVs) is proposed. This algorithm is developed by combining the Rapidly-Exploring Random Trees (RRT) and Velocity Obstacle (VO) algorithms and is referred to as the RRT-VO UTM algorithm. Here, the RRT algorithm works offline to generate obstacle-free waypoints in a given environment with known static obstacles. The VO algorithm, on the other hand, operates online to avoid collisions with other UAVS and known static obstacles. The boundary of the static obstacles are approximated by small circles to facilitate the formulation of VO algorithm. The proposed algorithm's performance is evaluated using numerical simulation and then compared to the well-known artificial potential field (APF) algorithm for collision avoidance. The advantages of the proposed method are clearly shown in terms of lower path length and collision avoidance capabilities for a challenging scenario.