A method for controlling a hybrid helicopter having at least one lifting rotor, at least one forward-movement propeller and an empennage provided with at least one moveable empennage surface. The method includes the following steps: using a main sensor to determine a current value of a rotor parameter conditioning a current power drawn by the lifting rotor, using an estimator to determine a current setpoint of the rotor parameter, adjusting a position of the moveable empennage surface using a deflection controller as a function of the current value and of current setpoint.

A loss-of-control prevention and recovery automatic control system of an aircraft is provided having a plurality of flight control mode, including a nominal flight control mode, a loss-of-control prevention control mode, a loss-of-control arrest control mode, and a nominal flight restoration control mode, as well as a supervisory control system capable of monitoring the flight states and flight events of the aircraft and determining which flight control mode to activate.

The disclosure generally provides methods, systems and apparatus for networked drone systems. In an exemplary networked drone system, a plurality of smaller drones are attached to a fixed platform to increase delivery payload, distance, reliability and safety. As a drone nears charge depletion, it is replaced in-flight with a new drone. Thus, the networked drone system need not be grounded to replace the depleted drone. In another embodiment, flight efficiency is increased by providing collapsible wins to the networked drone system.

Methods, apparatus, systems, and articles of manufacture are disclosed for flight control prioritization. An example apparatus includes a thrust state determiner to determine a first thrust margin between a first limit of first available power for first rotors of a rotorcraft and a first thrust state associated with the first rotors, determine a second thrust margin between a second limit of second available power for second rotors of the rotorcraft and a second thrust state associated with the second rotors, and identify the first thrust margin or the second thrust margin as a selected thrust margin based on a vertical control profile of the rotorcraft, and a command generator to determine a first vertical control command based on the selected thrust margin and a second vertical control command, the second vertical control command being executed by the rotorcraft, and control the rotorcraft based on the first vertical control command.

A method of adjusting a center of gravity (CG) of an aircraft includes: inputting at least one load parameter into a flight computer, determining, via a processer of the flight computer, the CG of the aircraft. Responsive to a determination by the processor that the CG is outside of a CG envelope, moving at least a first movable ballast of a plurality of movable ballasts from a first ballast dock to a second ballast dock to move the CG toward the CG envelope. Each of the first and second ballast docks may include a housing and a first ballast tray secured within the housing and comprising a plurality of channels.

Embodiments of the present invention relates to a method and apparatus for yaw fusion and an aircraft. The method includes: acquiring magnetometer data, inertial measurement unit (IMU) data, and global positioning system (GPS) data; determining a yaw angular velocity correction amount according to the GPS data and the magnetometer data; determining a first yaw angular velocity error value according to the IMU acceleration information and the GPS acceleration information; determining an initial complementary fusion yaw angular velocity; determining a second yaw angular velocity error value; and determining a final complementary fusion yaw.

An anti-torque system for a rotorcraft includes a first tail fan assembly including a plurality of first fan blades and a second tail fan assembly including a plurality of second fan blades. The first tail fan assembly has a larger diameter than the second tail fan assembly. The first fan blades have a larger rotational inertia than the second fan blades such that the second fan blades experience a larger angular acceleration than the first fan blades in response to torque, thereby providing yaw control for the rotorcraft.

Embodiments of the present invention relate to the field of aerial photography technologies and disclose an aircraft control method and an aircraft. The aircraft control method is applicable to the aircraft, and the aircraft includes a flight control system (FCS) configured to control the aircraft and a gimbal control system (GCS) configured to control a gimbal. The GCS can obtain a yaw control instruction to be inputted into the aircraft and attitude angle information outputted by the gimbal and then control yawing of the gimbal according to the yaw control instruction to be inputted into the aircraft and the attitude angle information outputted by the gimbal, so as to implement high-precision control of aerial photography of the aircraft, thereby ensuring high quality of an aerial video and resolving video freezing during aerial photography at a low rotation speed.

According to one implementation, a rotorcraft includes rotors, a fuselage, at least three rods, at least one load sensor and a control device. The rotors obtain lift. The fuselage is coupled to the rotors. The at least three rods support the fuselage. The at least one load sensor detects loads applied on the at least three rods. The control device automatically controls the rotors so that measured values of the loads detected by the at least one load sensor are brought to targeted values of the loads.

Disclosed is a method for controlling stable flight of an unmanned aircraft, comprising the following steps: acquiring real-time flight operation data of the aircraft itself by means of an attitude sensor, a position sensor and an altitude sensor mounted to the unmanned aircraft, performing corresponding analysis on a kinematic problem of the aircraft by a processor mounted thereto, and establishing a dynamics model of the aircraft (S1); designing a controller of the unmanned aircraft according to a multi-layer zeroing neurodynamic method (S2); solving output control quantities of motors of the aircraft by the designed multi-layer zeroing neural network controller using the acquired real-time operation data of the aircraft and target attitude data (S3); and transferring solution results to a motor governor of the aircraft, and controlling powers of the motors according to a relationship between the control quantities solved by the controller and the powers of the motors of the multi-rotor unmanned aircraft, so as to control the motion of the unmanned aircraft (S4). Based on the multi-layer zeroing neurodynamic method, a correct solution to the problem can be approached rapidly, accurately and in real time, and a time-varying problem can be significantly solved.