478 results about "Aircraft flight control system" patented technology

The invention relates to the technical field of unmanned aerial vehicles, in particular to a mooring unmanned aerial vehicle safety management system which comprises a multi-source data acquisition module, a data fusion and analysis module, an intelligent risk assessment module, a control instruction generation module and an execution mechanism module. The multi-source data acquisition module acquires flight and mooring cable data of the unmanned aerial vehicle by using various sensors, and transmits the flight and mooring cable data to the data fusion and analysis module through a time division multiple access protocol with low packet loss rate and low delay; the intelligent risk assessment module judges a risk level by combining a risk assessment model based on a support vector machine and a Bayesian network algorithm according to the information, and generates prompt information if the risk level exceeds a preset range; the control instruction generation module generates instructions according to a risk prompt and a preset strategy, ranks the instructions and sends the instructions to the execution mechanism module; a flight control system in the execution mechanism module cooperates with a mooring cable electric take-up and pay-off device and the like, real-time adjustment and control over the flight attitude of the unmanned aerial vehicle and the cable state are achieved, and it is ensured that tasks are safely and stably executed in the complex environment.

The invention relates to the technical field of unmanned aerial vehicle clusters, in particular to an unmanned aerial vehicle cluster formation method. Comprising the following steps: system initialization and configuration, cluster communication and data exchange, collaborative algorithm calculation and control input, positioning and navigation assistance, global and dynamic task planning setting, energy management and optimization strategy implementation, and biological enlightenment formation control application. Compared with a traditional formation operation method, the unmanned aerial vehicle cluster formation method is higher in autonomy, and the unmanned aerial vehicle cluster formation is more accurate through an advanced autonomous flight control system and a sensor technology. Information sharing and cooperative actions among the unmanned aerial vehicles in the cluster formation are realized through an efficient communication technology, and even if part of the unmanned aerial vehicles fail, other unmanned aerial vehicles can still continue to execute tasks.

The invention belongs to the field of electromechanical control, relates to an electric pump work control technology, and discloses an electric pump work comprehensive control method and system.The work comprehensive control method comprises the steps that onboard state signals, flight control system state signals and hydraulic pressure of each sub hydraulic system in a hydraulic system are collected in real time; starting and stopping of an electric pump connected to the hydraulic sub-system are controlled through a first electric pump control logic and an anti-error working mechanism according to the onboard state signal; and according to the flight control system state signal, the hydraulic pressure and other electric pump control instructions, the electric pump connected to each sub hydraulic system is automatically controlled through second electric pump control logic, an anti-error working mechanism and a time-staggered working mechanism. The working accuracy of the electric pump can be ensured, meanwhile, system damage caused by simultaneous starting of the electric pump is avoided, and the system safety is improved.

The invention discloses a four-rotor unmanned aerial vehicle model prediction control method based on a deep Q-network (DQN). The method comprises the following steps: firstly, establishing an inertial coordinate system and a body coordinate system, and establishing a kinematic equation and a kinetic equation of the four-rotor unmanned aerial vehicle based on a Newton second law and an Euler-Lagrange equation; according to the method, an unmanned aerial vehicle robust controller based on model predictive control (MPC) is designed, optimal control input is generated through dynamic optimization, and the trajectory tracking performance of the unmanned aerial vehicle is improved. For various disturbances existing in the whole tracking process, a DQN reinforcement learning algorithm and an MPC method are combined to design a flight control system, control rate errors caused by the disturbances are compensated, the stability of attitude control is enhanced, and the tracking precision and the anti-interference capability are improved. And finally, verifying the robust performance of the flight control system through a simulation experiment.

The invention belongs to the field of electromechanical control, and discloses a redundancy control method and system for an electric pump and a transmission system.The redundancy control method comprises the steps that onboard state signals, flight control system state signals and first hydraulic pressure and second hydraulic pressure of each hydraulic sub-system in a hydraulic system are collected in real time; an electric pump closing instruction and a hydraulic pressure logic judgment instruction are output through an on-board state threshold value and first electric pump control logic according to the on-board state signal, and the electric pump is closed according to the electric pump closing instruction; and a flight control system state signal, the first hydraulic pressure and the second hydraulic pressure are obtained, and each electric pump is automatically controlled through a low-pressure threshold value and a pressure safety threshold value by adopting second electric pump control logic, a hydraulic pipeline protection mechanism and an electric pump protection mechanism. According to the invention, the starting of each electric pump can be automatically controlled, a hydraulic passage can be protected, and the flight safety is ensured.

The invention provides an unmanned aerial vehicle intelligent power line inspection system, and relates to the technical field of power line inspection. The unmanned aerial vehicle intelligent power line inspection system comprises an unmanned aerial vehicle platform which comprises an unmanned aerial vehicle body, a flight control system, a power system and a navigation system; the sensor module comprises a high-definition camera, an infrared camera, a laser radar and an ultrasonic sensor; the data processing module comprises an embedded processor, a data storage unit and a data transmission module and is used for processing and analyzing the data acquired by the sensor; the intelligent algorithm module is used for intelligently analyzing and judging the state of the power line; and the communication module comprises a wireless communication module and a satellite communication module and is used for realizing data transmission and remote control between the unmanned aerial vehicle and a ground control center. The unmanned aerial vehicle is used for replacing manual inspection, so that the work of personnel in dangerous environments such as high altitude and complex terrains is reduced, and the personnel risk is reduced.

The invention discloses an aircraft voice instruction security analysis method and system based on natural language processing, and aims to realize intelligent identification and execution control of aircraft operation instructions. The method comprises the steps of receiving a voice instruction audio input by an aircraft operator, and performing preprocessing to generate a standardized voice segment; converting the standardized voice segment into text content, and extracting structured semantic information related to the instruction; calculating a semantic intention confidence score of the instruction in combination with the structured semantic information, the aircraft operation state, the historical instruction and the current task mode; according to the confidence score and the security policy rule, jointly evaluating the security of the instruction and outputting an executable state identifier; when the state identification is executable, calculating a flight control command and submitting the flight control command to a flight control system for execution; and when the state identifier is non-executable, the interception operation is automatically executed, and abnormal prompt information is fed back to an operator through the man-machine interaction module.

The invention relates to a four-axis unmanned aerial vehicle PID parameter setting method based on a whale optimization algorithm, and belongs to the technical field of unmanned aerial vehicle control. The method aims at solving the problems that in the traditional PID parameter setting process, experience is relied on, time is consumed, and optimal parameters are difficult to obtain. According to the method, the whale optimization algorithm is introduced, and the characteristics of high global search capability and high convergence speed are utilized, so that the parameters of the four-axis unmanned aerial vehicle PID controller are automatically optimized. The method comprises the steps that firstly, a kinetic model of the four-axis unmanned aerial vehicle is established, a control target of a PID controller is determined, PID parameters serve as optimization variables, and a fitness function is defined to evaluate control performance; then, performing global search in a parameter space by using a whale optimization algorithm, and gradually optimizing PID parameters by simulating a whale predation behavior; the optimized PID parameters are applied to a flight control system of the four-axis unmanned aerial vehicle, and stable attitude, height and position control is achieved. The method has the advantages that PID parameters are automatically set through the whale optimization algorithm, the low efficiency and subjectivity of a traditional trial and error method are avoided, and the parameter setting efficiency and precision are remarkably improved; meanwhile, for an unmanned aerial vehicle control system with high nonlinearity and complexity, the WOA can adjust parameters based on actual feedback under the condition that accurate modeling of the system is not needed, and dependence on system modeling is reduced.

The invention belongs to an electric pump work control technology in the aviation field, and discloses an anti-error work control method and system for a multi-redundancy automatic electric pump, and the method comprises the steps: collecting an onboard state signal, a flight control system state signal and two paths of hydraulic pressure of each sub-hydraulic system in a hydraulic system in real time; according to the onboard state signal, a hydraulic pressure logic judgment instruction is output through the first control logic or an electric pump mistake proofing mechanism is started, and an electric pump connected to the sub hydraulic system is controlled to be closed through the electric pump mistake proofing mechanism; a flight control system state signal and two-way hydraulic pressure are obtained according to the pressure logic judgment instruction, and an electric pump connected to each hydraulic sub-system is automatically controlled through second control logic and an electric pump mistake proofing mechanism. Through the design of the first control logic, the second control logic and the electric pump mistake proofing mechanism, the working accuracy of the multi-redundancy automatic electric pump can be ensured, and the system safety is improved.

The invention discloses a distributed electric propulsion box type wing ground effect aircraft which comprises a main fuselage, the two sides of the main fuselage are connected with one ends of sweepback wing main wings, one ends of two empennages above the tail of the main fuselage are connected with the main fuselage through vertical tails, and the other ends of the two empennages are connected with the other ends of the two main wings through end plates respectively. The empennage is connected with a plurality of high ducted fans directly driven by a motor, an elevator and an aileron are arranged at the rear part of the empennage, a rudder is arranged at the rear part of the vertical fin, and the elevator, the aileron and the rudder are controlled by a flight control system and are mutually independent. The ground-effect aircraft provided by the invention adopts a box-type wing structure formed by combining the sweepback main wing and the sweepforward empennage, has better aerodynamic performance in a take-off and climbing low-speed flight state, and can provide larger lift force and better maneuvering characteristics.

The invention provides an aerial photovoltaic system and a high-efficiency energy management method thereof, and the system comprises a self-adaptive floating platform, a high-efficiency light photovoltaic module, a linear energy transmission system and an intelligent attitude regulation and control and flight control system, and is based on an AI meteorological data prediction algorithm and in combination with a GPS / Beidou positioning module. A servo motor, a telescopic cable and an electric propulsion fan are driven to adjust the orientation of the photovoltaic panel and the height of the platform; according to the energy storage and management system, a solid-state lithium battery and a hydrogen fuel battery are combined, electric energy distribution is optimized, and dynamic balance of photovoltaic power generation, energy storage and wireless transmission is achieved. Through dynamic adjustment of the suspension height, intelligent illumination tracking and efficient wireless power transmission, the problems that traditional ground photovoltaic land occupation is limited and transmission loss is high are solved, light weight, wind resistance stability and day and night power supply capacity are achieved, and the power generation efficiency and environmental adaptability are greatly improved.

The invention relates to the technical field of flight control, in particular to an unmanned aerial vehicle single-shot failure control method, system and device and a medium, and the method comprises the steps: single-shot failure control in the take-off stage of an unmanned aerial vehicle, namely, taking-off is interrupted, an engine on the other side is closed, a brake is used, and a rudder and a front wheel are used for carrying out deviation correction control on the unmanned aerial vehicle; single-engine failure control in the air cruise stage of the unmanned aerial vehicle: compensating additional yaw moment brought by a single-side engine through a transverse heading automatic compensation controller and a flight control system together to enable the aircraft to reach a balanced state, and adjusting the heading of the unmanned aerial vehicle to a designed emergency course to complete recovery; the single-shot failure control in the landing stage of the unmanned aerial vehicle comprises the following steps of: keeping the balance of the unmanned aerial vehicle by utilizing a transverse heading automatic compensation controller before landing, immediately closing an engine on the other side after touching the ground, and adopting a rudder and a front wheel to rectify deviation to control and track a ground route. The flight safety of the unmanned aerial vehicle is guaranteed, and the single-shot failure emergency processing capacity of the unmanned aerial vehicle is improved.

The invention discloses an unmanned aerial vehicle non-cooperative environment autonomous landing method and system based on three-dimensional visual semantic information, and relates to the technical field of unmanned aerial vehicle landing recognized.The method comprises the steps that during emergency landing, a semantic segmentation model is used for generating a two-dimensional semantic segmentation map and obtaining semantic tags, and a landing area is automatically screened and tracked; fusing vision and inertia information through an airborne binocular camera and an IMU (Inertial Measurement Unit), outputting an accurate pose and depth map, and inputting a map construction algorithm to reconstruct a landform map of a landing area; projecting the semantic tag to a three-dimensional space and fusing the semantic tag with a terrain map to generate an evaluation map; and finally, an optimal landing point is determined by a landing point selection algorithm, and autonomous landing is completed through a flight control system. The method has the characteristics of high precision and fast reasoning, gets rid of dependence on external signals, significantly improves the landing capability under complex terrains, and guarantees the safety and reliability of autonomous landing.

A VTOL UAV comprising a chassis, a propulsion system mounted on the chassis, electronics including a flight control system, and a 3D LIDAR sensor mounted on the chassis. The 3D LIDAR sensor has a laser beam 360° rotation axis (R) and an azimuthal scanning angle (α) in a range of 70° to 110°. The propulsion system comprises a plurality of propellers with motors configured for flight without wings, including static hovering flight, the VTOL UAV comprising a yaw axis (Z) about which it can rotate upon itself. The 3D LIDAR sensor is statically mounted on the chassis such that the LIDAR laser beam 360° rotation axis (R) is inclined at a LIDAR inclination angle (β) with respect to the yaw axis (Z) in a range of 35° to 50°.

The invention discloses an unmanned aerial vehicle load engine state identification method based on deep learning, and the method comprises the following steps: S1, collecting the multi-mode operation data of an unmanned aerial vehicle load engine, and completing the standardization preprocessing; s2, inputting the spatial data into a spatial pyramid pooling network, and extracting a multi-scale spatial feature matrix; s3, inputting the spatial features into a masking auto-encoder, performing feature masking, encoding and reconstruction, and generating reconstruction features and error information; s4, fusing the original features, the reconstructed features and the non-spatial data, inputting a discrimination network, and outputting a health label and a risk score; and S5, according to an identification result, a flight control system or a maintenance platform is linked to realize alarm and scheduling. According to the method, intelligent identification and abnormal risk early warning of the state of the load engine of the unmanned aerial vehicle are realized by fusing multi-scale spatial feature extraction and a covering self-encoding reconstruction mechanism.

The invention provides a civil aircraft flight control system dynamic reconstruction method fusing uncertainty quantification and toughness decision, and belongs to the field of aviation electromechanical product reliability engineering, and the method comprises the steps: S1, multi-source uncertainty modeling and optimization; s2, running state monitoring and risk prediction; s3, evaluating the toughness margin of the flight control system; S4, generating a candidate toughness response strategy; s5, multi-attribute utility evaluation based on online model prediction; s6, selecting an optimal robust toughness decision; s7, executing an optimal robust toughness decision; and S8, empirical learning and model adaptive optimization are carried out. The invention provides a novel operation safety guarantee theory and method which deeply integrates uncertainty quantification, a toughness engineering principle and a multi-criterion robust decision-making aiming at severe uncertainty faced by a dynamic reconfigurable civil aircraft flight control system in a complex operation environment and challenge on flight safety. And the safety decision quality and the overall operation toughness of the system under uncertainty can be obviously improved.

The invention discloses a combined type coaxial high-speed helicopter flight / turboshaft engine comprehensive control system. The system comprises the following steps that according to control mechanism parameters output by a flight control system and a combined type coaxial high-speed helicopter fuzzy torque prediction model, a flight required torque advanced prediction value of a combined type coaxial high-speed helicopter system is output; the flight required torque advanced prediction value is input into a turboshaft engine system identification model after being superposed by a variable transmission ratio proportionality coefficient to obtain a fuel prediction value, and the fuel prediction value is further superposed with the output of an engine control system to obtain the fuel input of the turboshaft engine system; calculating a flight required torque advanced prediction value error, and if the error value is greater than a threshold value, updating the fuzzy torque prediction model by using the flight state parameters of the composite coaxial high-speed helicopter system; the relative error value of the current power turbine rotating speed is calculated, and if the relative error value is larger than a threshold value, primary parameter updating is conducted on the identification model of the turboshaft engine system through a recursive least square algorithm according to the real-time and historical torque input of the turboshaft engine system and the power turbine rotating speed. According to the method, under the condition that the constant rotating speed control target of the turboshaft engine is considered, the stability of a coaxial high-speed helicopter flight / turboshaft engine comprehensive control system is enhanced, the response speed of the turboshaft engine is increased, and the overshoot and droop amount of the rotating speed of a power turboshaft is reduced.

The invention relates to a passive fault-tolerant control method and system for a tilt-rotor unmanned aerial vehicle based on a nonlinear variable parameter model. The method comprises the following steps: S1, establishing an actuator fault model; s2, constructing a longitudinal dynamic model of the tilt-rotor aircraft, and converting the longitudinal dynamic model of the tilt-rotor aircraft into a nonlinear variable parameter model of the tilt-rotor aircraft; s3, introducing an actuator fault model into the nonlinear variable parameter model to obtain a nonlinear variable parameter model containing an actuator fault; s4, designing a fault-tolerant controller by adopting a quadratic sum convex programming method; s5, based on the Lyapunov stability theory, establishing a fault-tolerant controller solvability condition of the nonlinear variable parameter system model containing the actuator fault, enabling the nonlinear variable parameter model to have exponential stability, and solving the solvability condition to obtain the fault-tolerant controller; and S6, applying the fault-tolerant controller to a flight control system of the tilt-rotor aircraft.

The invention relates to the technical field of deep learning, aircrafts and the like, and provides an intelligent outdoor flight waistcoat based on deep learning, the intelligent outdoor flight waistcoat is provided with a waistcoat body and an intelligent flight control system, the intelligent flight control system comprises a deep learning control module, a main flight power device, an attitude auxiliary adjusting device and an environment sensing system; the environment sensing system comprises a visual sensor, a laser radar sensor, an ultrasonic sensor, a height sensor and a GPS positioning module and is used for collecting outdoor environment data in real time and transmitting the outdoor environment data to the deep learning control module, and the deep learning control module analyzes the environment data in real time and automatically optimizes the flight state through an autonomous learning algorithm. The main flight power device is controlled to provide lift force and propulsion power, and the attitude auxiliary adjusting device is utilized to automatically correct the flight attitude, so that the flight waistcoat is convenient to wear, intelligent in environmental perception, high in self-adaptability, safe and stable, and meets diversified outdoor flight and rescue task requirements in the future.

The invention relates to the technical field of intelligent flight control, in particular to a multi-source sensing data fused low-altitude flight track offset intelligent repair method, which comprises the following steps of: performing jitter correction on a visual image stream through a motion compensation module, performing resampling on an ADS-B positioning signal by adopting a Lagrange interpolation method, and performing multi-source sensing data fusion on the ADS-B positioning signal; the radar point cloud is mapped to an image coordinate system, and a fusion track representation vector with time-space synchronization is generated; extracting a curvature abrupt change point set, an environment shielding factor and an airspace visibility gradient based on the vector, and constructing a multi-dimensional offset feature tensor in combination with aircraft dynamics constraints; inputting the tensor into a pre-trained space-time diagram neural network, and outputting a risk grading restoration decision comprising a restoration angle, a climbing rate and a speed compensation amount; injecting the repair decision into a flight control simulator for verification, and outputting a final repair instruction when the trajectory deviation value is smaller than a safety threshold value; the method has high precision and closed-loop execution flight path repairing capability, and is suitable for a flight control system in a complex low-altitude environment.

The invention relates to the technical field of fire-fighting unmanned aerial vehicles, in particular to an unmanned aerial vehicle applied to fire fighting and a using method. The unmanned aerial vehicle comprises a vehicle body, a rotor wing assembly, a pressure water gun, a detector and a control unit; the top of the pressure water gun is provided with a transparent window, the detector is provided with a plurality of detection units, and the detection units are located right above the transparent window; and the control unit is used for transmitting the flow velocity to a flight control system, so that the flight control system determines the magnitude of recoil force according to the flow velocity, and further adjusts flight parameters of the unmanned aerial vehicle according to the magnitude of the recoil force. The detector is arranged above the pressure water gun, the speed of water flow during spraying is calculated according to the distance between the detection units, and then the recoil force applied to the unmanned aerial vehicle when the pressure water gun sprays water is calculated, so that a flight control system adjusts the flight attitude of the unmanned aerial vehicle according to the recoil force; therefore, stable flight and efficient fire extinguishing of the unmanned aerial vehicle are ensured.

The invention discloses an unmanned aerial vehicle and robot dog intelligent cooperation system. The system comprises an unmanned aerial vehicle, a robot dog and a ground control system; the unmanned aerial vehicle comprises an unmanned aerial vehicle sensor, an unmanned aerial vehicle communication module and a flight control system, the unmanned aerial vehicle communication module is used for performing data communication with the robot dog and the ground control system, and the flight control system is used for controlling flight of the unmanned aerial vehicle; the robot dog comprises a parking apron, a robot dog sensor, a robot dog communication module and a semantic recognition module, the parking apron is used for carrying the unmanned aerial vehicle, the robot dog communication module is used for performing data communication with the unmanned aerial vehicle and the ground control system, and the semantic recognition module is used for performing semantic analysis on the first environment data and the second environment data; generating a dynamic semantic map; the ground control system comprises a ground communication module used for performing data communication with the robot dog and the unmanned aerial vehicle; according to the invention, efficient and intelligent docking of the unmanned aerial vehicle and the ground equipment can be realized, and autonomy, reliability and task execution efficiency of collaborative operation are improved.

The invention provides an aircraft hydraulic servo actuation control system, which relates to the field of hydraulic servo electronic control, and comprises a main flight control system, a backup control system, an enhanced electronic unit and a tail end actuator, the main flight control system is connected with the enhanced electronic unit through a first MCB communication bus, the backup control system is connected with the electronic unit through a second MCB communication bus, and the main flight control system is switched to the backup control system when power supply or communication abnormity occurs in the main flight control system; the enhanced electronic unit is connected with the actuator; the enhanced electronic unit comprises a public area, an instruction channel and a monitoring channel, and the instruction channel and the monitoring channel are used for monitoring the health state and data validity of the first MCB communication bus or the second MCB communication bus in real time. According to the invention, bidirectional switching between the main flight control system and the backup system of the enhanced electronic unit is ensured, uninterrupted work is kept, and the working reliability of the hydraulic servo actuation control system is effectively improved.

The invention discloses an unmanned aerial vehicle communication low-complexity beam prediction method and system based on a control view angle, and aims to solve the problem of narrow beam alignment and tracking in a high mobility scene. According to the method, beam offset is decomposed into offset caused by attitude change of the unmanned aerial vehicle and offset caused by relative position change of the unmanned aerial vehicle and the base station. For the offset generated by the attitude change of the unmanned aerial vehicle, the variable quantity of the offset relative to the previous time slot is predicted, based on a command / control sequence provided by a flight control system, local dynamic characteristics are captured through exponential decay variation, a dynamic action space is constructed in combination with the beam offset variable quantity, and the precision and real-time performance of beam prediction are optimized. And the beam offset caused by the change of the relative position of the base station is directly calculated through a geometric method. The method is extremely low in calculation complexity, can be realized in real time, and has a relatively high prediction success rate, relatively low beam scanning overhead and relatively high data transmission performance in a high dynamic scene of the unmanned aerial vehicle.

The invention discloses an unmanned aerial vehicle target active detection method and system based on a millimeter wave radar, and relates to the technical field of unmanned aerial vehicle detection, and the method comprises the steps: carrying out the frequency mixing of an obtained emission frequency modulation continuous wave signal and an echo signal, and obtaining an intermediate frequency signal; performing fast Fourier transform on the intermediate frequency signal to obtain distance information of the target unmanned aerial vehicle; acquiring a plurality of Chirp signals based on the echo signals; based on the phase difference among the plurality of Chirp signals, calculating to obtain the speed information of the target unmanned aerial vehicle; performing angle estimation on the echo signal to obtain angle information of the target unmanned aerial vehicle; integrating the distance information, the speed information and the angle information of the target unmanned aerial vehicle to obtain motion parameters of the target unmanned aerial vehicle; and transmitting the motion parameters of the target unmanned aerial vehicle to a flight control system or other data receiving equipment of the unmanned aerial vehicle. The detection capability and the tracking effect of the unmanned aerial vehicle target can be improved, and the operation safety of the unmanned aerial vehicle is effectively guaranteed.

The invention discloses a tilt rotorcraft decoupling flight control method based on model compensation. The tilt rotorcraft decoupling flight control method comprises the steps that a flight control system reads airborne sensor data in real time; according to control input acquired in real time and airborne sensor data, superposing force and torque generated by each component by utilizing a nonlinear dynamic model of the tilt rotorcraft to obtain the stress condition of the rotorcraft body; determining a first-order differential of the model state quantity of the tilt rotorcraft; linearizing the nonlinear dynamic model, extracting a control matrix and constructing attitude inner ring virtual control input; on the basis of inversion of the transfer function, constructing an inverse model expression of each channel of the tilt rotorcraft; constructing an error compensation link of each channel to compensate a model error; and minimizing an increment norm of the compensated virtual control quantity through a weighted pseudo-inverse control distribution model to obtain an increment of the control quantity, and forming a final execution command in combination with the current total pitch and the helm deflection angle to be sent to a rotor and control surface execution mechanism of the tilt rotorcraft.

Aspects of the present disclosure generally relate to systems and methods for flight control of aircrafts driven by electric propulsion systems and in other types of vehicles. In some embodiments, a flight control system of an aircraft is disclosed, configured to receive one or more signals to control movement of the aircraft, determine at least one flight condition of the aircraft, calculate at least one or more loads associated with the aircraft based on the determined at least one flight condition; determine a flight configuration to alleviate loads on one or more components of the aircraft based on the received one or more signals and the calculated one or more loads by predicting a distribution of loads associated with the aircraft, generate one or more effector commands based on the flight configuration; and actuate one or more aircraft effectors based on the one or more effector commands.

In the descending process of the aircraft, the flight control system seeks and keeps a glide gradient, and the glide gradient is determined according to the parameters input by a single pilot; for example, the position of a control shaft of a flight joystick. During landing approach, a pilot visually observes a predetermined landing site, so that the pilot can easily determine an appropriate guide slope selection. The glide slope defines a ratio of forward and descent speeds that is automatically established and maintained / updated as a function of pilot input, while these speeds are automatically lowered during descent, although the two speed directions are each affected by significantly different aircraft mechanisms controlling them. The aircraft ends its glide path when the speed is zero or near zero so as to be able to land by short-distance vertical descent. Thus, the pilot gets rid of both the problems of coordinating the speed to reach a specific guide gradient and reducing the speed while maintaining such coordination.

An aircraft includes a flight plan and contingency data including a plurality of contingency landing sites. An aircraft communication system communicates with a ground control station via a communication link and detects a lost communication scenario in response to degradation in the communication link. An aircraft flight control system controls the aircraft en route according to the flight plan. In response to detecting the lost communication scenario, the flight control system routes the aircraft back to a last known location in which the communication link was not degraded. In response to failure to re-establish the communication link, the flight control system selects a landing site based on a current aircraft location relative to a plurality of potential landing sites including an origin airport, a destination airport, and a first continency landing site. The flight control system controls the aircraft to approach and land at the selected landing site.