A system and method for determining the wind force along the planned trajectory of a projectile are disclosed herein. A drone is flown along the expected path of the trajectory along a set heading. The drone is programmed to maintain the heading. As wind forces act upon the drone during its flight, the drone's electronic stability system provides automatic power and directional control to one or more motors that control the rotors and propellers that keep the drone aloft. By monitoring the changes in motor or drone state information over time in response to wind forces, the wind can be determined at various locations along the flight path. This information can be provided to a ballistics calculator to determine the launch heading of the projectile.

A method of protecting a margin for controlling the yaw attitude of a hybrid helicopter that includes a lift rotor as well as at least one first propeller and at least one second propeller. A thrust control is configured to generate at least a first order issued to increase a first pitch of first blades of the first propeller and a second pitch of second blades of the second propeller. After a first order has been issued, the method includes an inhibition step for having a control computer inhibit the first order when a yaw attitude control margin, with regard to an envelope delimiting a flight control domain, is and/or will be less than or equal to a threshold.

Provided is a stable flight control method for a multi-rotor unmanned aerial vehicle based on finite-time neurodynamics, comprising the following implementation process: 1) acquiring real-time flight orientation and attitude data through airborne sensors, and analyzing and processing kinematic problems of the aerial vehicle through an airborne processor to establish a dynamics model of the aerial vehicle; 2) designing a finite-time varying-parameter convergence differential neural network solver according to a finite-time varying-parameter convergence differential neurodynamics design method; 3) solving output control parameters of motors of the aerial vehicle through the finite-time varying-parameter convergence differential neural network solver using the acquired real-time orientation and attitude data; and 4) transmitting results to speed regulators of the motors of the aerial vehicle to control the motion of the unmanned aerial vehicle. Based on the finite-time varying-parameter convergence differential neurodynamics method, the invention can approximate the correct solution of the problem in a quick, accurate and real-time way, and can well solve a variety of time-varying problems such as matrix, vector, algebra and optimization.

An aircraft can include a first wing and a second wing. The first wing can extend laterally from an aircraft body to a first tip, and the second wing can extend laterally from the aircraft body to a second tip. The aircraft can include a first end effector and a second end effector, each including a fore winglet and an aft winglet. The fore and aft winglets of the first end effector can be pivotably connected to the first tip. The fore and aft winglets of the second end effector can be pivotably connected to the second tip. The fore and aft winglets of the first and second end effectors can be independently operable. The first and second end effectors can be independently operable. A processor can be operatively connected to control movement of the fore and aft winglets of the first and second end effectors.

A system and method for distributed flight control configured for use in an electric vehicle wherein the system includes a flight control assembly which further includes at least a sensor electronically connected to the flight control assembly. The sensor is configured to capture at least an input datum, and at least a performance datum. The system further includes a plurality of modular flight controllers communicatively coupled to at least an actuator of a plurality of actuators, wherein each modular flight controller of the plurality of modular flight controllers is configured to the multitude of data from at least a sensor, generate an attitude control datum, determine at least an actuator instruction datum, and perform a control allocation configured for the at least a actuator from the plurality of actuators to follow as a function of the flight control assembly.

An aircraft motion observer configured for use in electric aircraft includes an actuator model configured to receive at least an aircraft command, wherein the aircraft command comprises a desired change in aircraft trajectory as a function of a plurality of flight components, generate a performance datum for the flight components as a function of the aircraft command. System includes a plant model configured to generate a predictive datum for the flight components as a function of the actuator model and the performance datum. System includes a sensor communicatively connected to the aircraft configured to detect a measured state datum. System includes a controller configured to compare the predictive datum and the measured state datum, generate an inconsistency datum wherein the inconsistency datum comprises a mathematical function to compensate for the difference between the predictive state datum and the measured state datum, and transmit the inconsistency datum to the plant model.

A method for controlling an overdetermined system with multiple actuators, for example an aircraft (1) with multiple propulsion units (3). The actuators perform at least one primary task and at least one non-primary task, including: a) determining a pseudo-control command u.sub.p ∈.sup.p′ based on a physical model of the system, which command represents the torques (L, M, N) and a total thrust force (F) acting on the system, b) determining a control matrix D, D∈.sup.p′×k according to u.sub.p=Du, where u.sub.1=D.sup.−1u.sub.pu.sub.1 ∈.sup.k represents a control command for the actuators to perform the primary task, c) projecting the non-primary task into the null space N(D) of the primary task, so that Du.sub.2=0 if u.sub.2u.sub.2 ∈.sup.k represents a control command for the actuators to perform the non-primary task, and d) providing the control commands from b) and c) to the actuators. In this way, the solution of the primary task is not adversely affected by the non-primary task or its solution.

Systems and method for controlling the attitude maneuvers of a spacecraft in space are provided. The method automatically generates optimal trajectories in real-time to guide a spacecraft, providing a much more robust and efficient method than predefined trajectories, to model errors or disturbances. These methods do not rely in predefined trajectories and their associated feed-forward term. The systems comprise sensors, attitude control mechanisms, and a control module to orient the spacecraft in real-time, such that the spacecraft reaches a desired target attitude following an optimal path in the state space and is locally and asymptotically stable.

A method for determining a maneuvering reserve in an aircraft having a number of propulsion units, preferably a multirotor VTOL aircraft, most preferably an aircraft with electrically operated drive units for the rotors, including the steps: a) Determining a control vector, τ, for the aircraft, τ=(L M N F).sup.T, the components of which represent control torques of the aircraft around the roll axis, L, the pitch axis, M, and the yaw axis, N, and a total thrust, F, b) Approximating an existing four-dimensional control volume, D, of the aircraft by a four-dimensional ellipsoid, E, the axes of which represent the control torques, L, M, N, of the aircraft and the total thrust, F, c) Determining a normalized control vector, τ.sub.ind=(L.sub.ind M.sub.ind N.sub.ind F.sub.ind).sup.T for the aircraft, using axis dimensions, L.sub.max, M.sub.max, N.sub.max, F.sub.max, of the ellipsoid, in particular semi-axis dimensions of the ellipsoid; and d) Outputting at least the normalized control vector, τ.sub.ind, for determining a permissible flight maneuver in at least one dimension of the four-dimensional control volume.

Physical and logical components of a long line loiter control system address control of a long line loiter maneuver conducted beneath a carrier, such as a fixed-wing aircraft. Control may comprise identifying, predicting, and reacting to estimated states and predicted states of the carrier, a suspended load control system, and a long line. Identifying, predicting, and reacting to estimated states and predicted states may comprise determining characteristics of state conditions over time as well as response time between state conditions. Reacting may comprise controlling a hoist of the carrier, controlling thrusters of the suspended load control system, and or controlling or issuing flight control instructions to the carrier so as not to increase the response time and or to avoid a hazard.