Provided in this disclosure is a system and methods for wind compensation of an electric aircraft. More specifically, provided in this disclosure is a controller of an aircraft configured to use a plant model for compensating for wind forces. The processor is configured to receive, from the sensor, at least a geographical datum of the electric aircraft.

An unmanned aerial vehicle (UAV) control system includes a first UAV, a second UAV that flies near the first UAV during a flight of the first UAV and is configured to obtain wind information about wind, and flight control means for controlling the flight of the first UAV based on the wind information obtained by the second UAV.

A stability control method and device based on particle active disturbance rejection are provided. The method includes: establishing an active disturbance rejection controller model based on a dynamic model and a speed loop control model of a tethered balloon system, where the speed loop control model is established through theoretical modeling of executive components of a control system of the tethered balloon system; and optimizing to-be-optimized parameters of the active disturbance rejection controller model using a particle swarm optimization algorithm, determining an optimal active disturbance rejection controller model, and using the optimal active disturbance rejection controller model to implement stability control of a photoelectric pod. An active disturbance rejection controller is optimized by using a particle swarm optimization algorithm, which can effectively isolate the internal and external disturbances of the photoelectric pod and improve the imaging stability of the photoelectric pod.

The present invention discloses an automatic control method and system for fixed-wing aircraft and autonomous driving vehicles, which pertains to the field of aircraft control technology. The method comprises the following steps: acquire actual measurement data and relevant data measured by a laser gyroscope system during the motion process of the fixed-wing aircraft/autonomous driving vehicles to calculate the force data acting on the aircraft/vehicles; establish a three-dimensional spatial model and construct a force control coordinate model within the three-dimensional spatial model based on the force data acting on the aircraft/vehicles and automatically control the operational state and position of the aircraft/vehicles based on the force control coordinate model. The present invention combines the principles of mechanics to simulate the fundamental logic of driver operation techniques, and combines real-time data to enable precise and effective automatic control of aircraft and vehicles.

A nonlinear dynamic control system is defined by a set of equations that include a state vector and one or more control inputs. Via a machine learning method, a sub-optimal controller is derived that stabilizes the nonlinear dynamic control system at an equilibrium point. The sub-optimal controller is retrained to be used as a stabilizing controller for the nonlinear dynamic control system under general operating conditions.

A computer accesses a first symbolic expression for an output matrix as a function of an input matrix at a computing device comprising processing circuitry and memory. The computer computes a first Jacobian of the input matrix with respect to an input tangent space. The computer computes a second Jacobian of the output matrix with respect to the input matrix. The computer computes a third Jacobian of an output tangent space with respect to the input matrix. The computer applies symbolic matrix multiplication to the first Jacobian, the second Jacobian, and the third Jacobian to obtain a second symbolic expression for the output tangent space with respect to the input tangent space. The computer provides a representation of the second symbolic expression, the second symbolic expression representing a computed tangent-space Jacobian.

A computer of an unmanned aerial vehicle (UAV) accesses, from a memory unit, a problem definition comprising cost functions associated with travel of the UAV. The computer causes movement of the UAV based on the cost functions. The computer adjusts one or more of the cost functions during a flight of the UAV. The computer causes further movement of the UAV based on the adjusted one or more of the cost functions.

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 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 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.