The paper addresses the problem of transforming single-output nonlinear state equations affine in disturbance into an extended observer form whose nonlinear injection part depends additionally on the derivatives of the output up to a finite order. Moreover, the considered form is affine in disturbance. Based on the earlier results a detailed algorithm is given and applied to the model of a synchronous machine connected to an infinite bus. For the system in the extended observer form a reduced-order Kalman-filter based observer can be designed such that its error dynamics is exponentially attractive and it is stable in the input-to-state sense with respect to the disturbance.

UAVs are becoming versatile and valuable platforms for various applications. However, the main limitation is their flying time. We present BEAVIS, a novel aerial robotic platform striking an unparalleled trade-off between the maneuverability of drones and the long-lasting capacity of blimps. BEAVIS scores highly in applications where drones enjoy unconstrained mobility yet suffer from limited lifetime. A nonlinear flight controller exploiting novel, unexplored, aerodynamic phenomena to regulate the ambient pressure and enable all translational and yaw degrees of freedom is proposed without direct actuation in the vertical direction. BEAVIS has built-in rotor fault detection and tolerance. We explain the design and the necessary background in detail. We verify the dynamics of BEAVIS and demonstrate its distinct advantages, such as agility, over existing platforms including the degrees of freedom akin to a drone with 11.36× increased lifetime. We exemplify the potential of BEAVIS to become an invaluable platform for many applications.

In recent times, Unmanned Aerial Vehicles (UAVs) have been intensively studied for a wide range of military and civil applications such as surveillance and reconnaissance missions, agriculture, traffic monitoring, pollution control, meteorological data collection, pipeline survey, early fire detection, rescue missions, wildlife population monitoring, etc. The above tasks can be carried out faster and more efficiently by a group of UAVs flying in formation. This paper presents the application of two stage switching controller in autonomous formation flight of UAVs based on the leader follower configuration. The constructed UAV airframe, as well as the autopilot, formation flight control unit and wireless communication link for data sharing between UAVs were presented and described. The advantages of the switched control were demonstrated in terms of a significantly larger region of stability in maintaining formation, as opposed to the standard, one stage control laws with classical compensators. Hardware In The Loop (HIL) tests of proposed formation flight control were performed, during which the inertial states and flight parameters were logged. The HIL test rig enabled us to verify the formation flight control performance which was further substantiated by actual flight tests.

This paper provides a real time on-board algorithm for a biplane-quadrotor to iteratively learn a forward transition maneuver via repeated flight trials. The maneuver is controlled by regulating the pitch angle and propeller thrust according to feedforward control laws that are parameterized by polynomials. Based on a nominal model with simplified aerodynamics, the optimal coefficients of the polynomials are chosen through simulation such that the maneuver is completed with specified terminal conditions on altitude and air speed. In order to compensate for modeling errors, repeated flight trials are performed by updating the feedforward control parameters according to an iterative learning algorithm until the maneuver is perfected. A geometric attitude controller, valid for all flight modes is employed in order to track the pitch angle according to the feedforward law. Further, a high-fidelity thrust model of the propeller for varying advance-ratio and orientation angle is obtained from wind tunnel data which is captured using a neural network model. This facilitates accurate application of feedforward thrust for varying flow conditions during transition. Experimental flight trials are performed to demonstrate the robustness and rapid convergence of the proposed learning algorithm.

Compliant fish-like robots are being developed as efficient and dependable underwater observation platforms with low impact on the observed environment. Orientation control is an essential building block to achieve autonomy for those vehicles. So far, the major focus has been on rigid tails or on flexible tails with a high degree of actuation. We present a novel control strategy for an underactuated robotic fish with a flexible tail optimized for cruising. The basis for our approach is the generation of asymmetric velocity profiles of the robot's tail beats. To achieve such velocity profiles, the usual sinusoidal tail actuation is replaced with skewed triangle waves. We provide a simple formulation for such waves, where their skew is dependent on only one variable which we define as skew factor. Furthermore, a nonlinear control law is derived to achieve the desired turning motions. We implement the controller on a compliant fish-like robot with a simple actuation mechanism. The control scheme is experimentally validated, and its robustness is tested in field trials.