A guidance and control system is proposed for automatic carrier landing of an unmanned aerial vehicle. To this end, the position and attitude of the carrier is predicted by considering the sea state so that the UAV guidance loop can be compensated during the final approach phase. The landing kinematics and reference trajectory are determined using the predicted touchdown position. Nonlinear guidance and control laws are designed based on a sliding-mode control scheme to make the UAV robustly follow the reference trajectory

A nonlinear command and stability augmentation system is designed based on feedback linearization for a morphing aircraft in the presence of different variable-span configurations. A variable-span morphing aircraft model is obtained by modifying a conventional aircraft model. Feed-forward neural networks are trained to learn the inverse dynamics in various morphing configurations and flight conditions. An attitude orientation system is designed to stabilize the airframe and track the commanded angular rates. Finally, an online adaptive mechanism is employed to compensate for inversion error. Even when the aircraft is changing the morphing configuration while maneuvering, the proposed design satisfies the desired handling quality specifications. Consequentially, the proposed design provides another degree of freedom to manipulate the morphing configuration, and therefore agility or fuel efficiency can be improved. Numerical simulation is performed to demonstrate the effectiveness of the proposed scheme.

In this study a multi-level path planning system is proposed for indoor search and rescue operations. Requirements for the path planning system are derived based on the operational concept of the integrated indoor navigation system. Different aspects of various path planning algorithms are assessed, and their suitability to search and rescue operations in structured indoor environments is investigated. A five-step path planning system is proposed, which consists of map pre-processing, segment path planning, graph processing, route optimization, and path post-processing. The proposed method addresses a multi-goal path planning problem in a multi-story building in a computationally efficient way by adopting a graph-based approach while satisfying such requirements as clearance conditions in the pre- and post-processing steps. Furthermore, a multi-query approach is adopted to exploit the response time and earn flexibility with respect to environmental changes. The effectiveness of the proposed path planning system is demonstrated through numerical simulations.

A control design scheme that integrates policy gradient-based integral reinforcement learning with an extended state observer is proposed for a morphing aircraft. A general continuous-time nonaffine system, such as a morphing aircraft, in which the morphing parameters are considered as control effectors, can be addressed by the proposed control scheme. By combining a Q-function with the policy gradient method, the optimal control problems for completely unknown and nonaffine systems can be resolved. The actor-critic parameter estimation employs the integral reinforcement learning technique. The learning-based strategy enables the prediction of optimal morphing parameters, even in the absence of a complete aerodynamic model. To estimate the disturbances derived from morphing variations, an extended state observer is designed using the knowledge of the nominal model. This approach allows for the independent design of controllers for both morphing parameters and conventional control effectors. A stability analysis shows the asymptotic stability of the closed-loop system and the optimal convergence of the Q-function. To demonstrate the effectiveness of the proposed method, a numerical simulation is performed.