388 results about "Aircraft flight control system" patented technology

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 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 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 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 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 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 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.

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.

The invention relates to a flight control system which comprises three flight control computers, and each flight control computer comprises an I / O board used for receiving input signals of one path of a cockpit control device signal and a sensor signal of a cross-linked interface system and outputting the input signals of the other path of the cockpit control device signal and the sensor signal of the cross-linked interface system; after the input signals are simply processed, the processed input signals are forwarded to the computing board, the backup board and other flight control computers, meanwhile, remaining cockpit control device signals and interface signals sent by other computers are received, and control instructions are output to actuators of corresponding control surfaces; the three calculation boards are used for calculating a corresponding control instruction based on a control law according to the received input signal and forwarding the control instruction to the I / O board; the backup board is used for entering a pre-position state when the computing board of the flight control computer fails; wherein when the backup boards of all the flight control computers enter the pre-position state, the flight control system enters the backup mode, and the backup boards calculate backup control instructions and transmit the backup control instructions to the actuators of the corresponding control surfaces.

The invention discloses an air-drop and air-delivery large unmanned aerial vehicle ultra-low-altitude flight control system and method, and belongs to the field of flight control systems, and the system comprises a flight control module, a drop control module, an environment sensing module, a gravity center balance adjustment module and a data storage and interaction module. The flight control module is used for controlling the unmanned aerial vehicle to fly at an ultra-low altitude and ensuring that the flight attitude is stable, so that the position error is less than or equal to 10m, the height error is less than or equal to 2m and the flight path deviation is less than or equal to 10m when the unmanned aerial vehicle flies at the ultra-low altitude; the delivery control module is used for supporting a single delivery mode and a rapid continuous delivery mode, the maximum delivery of 1.2 tons of goods can be achieved, at most four pieces of goods can be delivered in a single flight, the delivery control module is compatible with parachute air delivery and non-parachute air delivery, and the delivery precision of the parachute air delivery at the high altitude of 300 meters is smaller than 100 meters. According to the invention, the unmanned aerial vehicle delivery precision can be improved, the stability is high, and material waste or incapability of accurately delivering to a target area is avoided.

The embodiment of the invention provides a low-altitude airborne communication, guidance and sensing integrated multi-mode fusion communication system and method, and the system comprises low-altitude airborne communication, guidance and sensing integrated multi-mode fusion equipment which is used for carrying out the control of communication modes, carrying out the information transmission with all communication systems and an aircraft, and carrying out the flight control of an aircraft flight control system; the ground low-altitude private network communication system is used for realizing information transmission between the low-altitude aircraft and the ground equipment through a ground low-altitude private network; the ground public network 4G / 5G communication system is used for realizing information transmission between the low-altitude aircraft and the ground equipment through a ground public network; the low-orbit satellite communication system and the ground satellite communication system are used for realizing information transmission between the low-altitude aircraft and ground equipment through low-orbit and ground satellite communication; the Beidou communication system is used for realizing flight navigation of the aircraft and acquiring flight information; and the other aircraft ADS-B communication system is used for sensing information of other aircrafts.

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.

The invention discloses a digital twin platform of an aircraft flight control system, a data processing method of the digital twin platform and a medium, and relates to the technical field of aircraft simulation training, and the platform comprises a parameter setting unit which is used for setting aircraft basic information and current flight state parameters; the control signal acquisition unit is used for acquiring an electric signal generated when a user operates the control device; the calculation unit is used for calculating the initial control surface aerodynamic force according to the control surface aerodynamic force calculation model, calculating the control surface deflection angle according to the control surface aerodynamic-control device force transmission model, and recalculating the control surface aerodynamic force according to the control surface aerodynamic force calculation model; and the force sensing feedback unit is used for calculating joystick feedback force in the current aircraft flight state and the current control state according to the control surface aerodynamic force-control device force transmission model, and feeding back generated corresponding control resistance to the control device. The platform not only saves the economic cost and occupied area for establishing an entity iron bird test bench, but also can test the performance of a flight control system of an airplane in different flight states.

A system for flight control configured for use in an electric aircraft includes an inertial measurement unit (IMU) and configured to detect an aircraft angle and an aircraft angle rate. The system includes a flight controller including an outer loop controller configured to receive the input datum from the sensor, receive the aircraft angle from the IMU, and generate a rate setpoint as a function of the input datum. The system includes an inner loop controller configured to receive the aircraft angle rate, receive the rate setpoint from the outer loop controller, and generate a moment datum as a function of the rate setpoint. The system includes a mixer configured to receive the moment datum, perform a torque allocation as a function of the moment datum, and generate a motor command datum as a function of the torque allocation.

The invention discloses an unmanned aerial vehicle natural language multi-modal navigation method and system based on a multi-dimensional thinking chain. The method mainly comprises the following steps: constructing a software-in-the-loop simulation environment deeply integrated with task planning software, and collecting a live-action image with a time-space stamp; secondly, the natural language task instruction is analyzed, and key landmarks and position coordinates of the key landmarks are recognized in combination with a real scene image; then the recognition result and the structured task sequence are input into a multi-dimensional thinking chain reasoning engine in parallel to be processed; and finally, integrating the output of the multi-dimensional thinking chain inference engine, generating a flight action sequence, and issuing the flight action sequence to a flight control system to complete a navigation task. According to the method, the explicit thinking chain reasoning process is embedded into each step of navigation decision and is deeply integrated with the existing task planning ecology, so that the autonomy, adaptability and task success rate of the unmanned aerial vehicle in an unknown or dynamic environment can be remarkably improved.

An aircraft having a plurality of independent yaw control mechanisms includes an airframe having a central fuselage sized to hold at least one operator or payload and a wing extending from the fuselage. The aircraft includes a distributed thrust array coupled to the airframe, the thrust array having at least a first, second, and third pair of propulsion assemblies. Each propulsion assembly includes a rotor and is operable for at least single-axis thrust vectoring. The aircraft further includes a flight control system operable to independently control and combine each yaw mechanism of the propulsion assemblies. The yaw mechanisms for inducing a yaw moment include: canting at least one pair of propulsion assemblies away from the fuselage, selectively tilting at least one pair of propulsion assemblies forwards and backwards, varying an aerodynamic control surface of at least one propulsion assembly, and varying a rotational speed of at least one propulsion assembly.

A system and method for flight control of an aircraft, the flight control system including a plurality of flight components coupled to an aircraft, wherein the plurality of flight components includes a plurality of redundant control surfaces, a plurality of redundant low voltage buses communicatively connected to the plurality of flight components, wherein a failure in a redundant low voltage bus of the plurality of redundant low voltage busses does not impact the operability of the aircraft.

The present disclosure is generally directed to a flight control system and method of flight control for an electric aircraft. The system includes a pilot input communicatively connected to an electric aircraft, wherein the pilot input is configured to receive an input datum, and plurality of flight components communicatively connected to the electric aircraft, wherein the plurality of flight components includes a plurality of control surfaces. The system also includes a flight controller, wherein the flight controller is configured to determine a phase of flight, determine a command datum to control a position of the plurality of control surfaces as a function of the input datum, and command, when the phase of flight is determined to be hover, the plurality of control surfaces using the command datum.

An unmanned quadcopter aircraft system integrated with a pneumatic paintball projectile launching mechanism for recreational and training applications. The system includes a compressed gas powered single-shot paintball launcher with gravity-fed ammunition hopper, mechanical counterweight recoil compensation, and multiple electronic safety systems. GPS-based geofencing prevents firing in restricted zones, while altitude sensors prevent firing above predetermined heights. The flight control system automatically reduces aircraft speed and altitude limits when the projectile system is armed. A ground control system integrates standard flight controls with firing controls, safety indicators, and emergency stop capability. An alternative embodiment allows a used to adjust the elevation of the firing system while maintaining the orientation of the drone in space and also allowing recoil compensation. The system enables aerial paintball gaming and training scenarios while maintaining safety through redundant control systems and automatic parameter limiting.

The invention discloses a self-coupling PID (proportion integration differentiation) flight control method of a rudder-controlled transverse-row type rotor unmanned aerial vehicle based on dynamic multi-dimensional heterogeneous fuzzy logic, and belongs to the technical field of flight control of rudder-controlled transverse-row type rotor unmanned aerial vehicles. A flight control system architecture of the self-coupling PID flight control method is composed of three core modules: (1) a dynamic multi-dimensional fuzzy logic coupling PID controller (DMHFL-SCPID); (2) control instruction distribution; and (3) a six-degree-of-freedom dynamical model of the rudder-controlled transverse rotorcraft. According to the control algorithm, input and output dynamic parameters of a system are analyzed in real time, and four-dimensional flight control quantities (roll, pitch, yaw and height instructions) are generated. A control distribution strategy based on the aerodynamic characteristics of the rotor converts the instructions into actuating quantity of an actuating mechanism, and flight attitude adjustment is realized by changing aerodynamic load distribution of a rotor system, so that the unmanned aerial vehicle is guided to realize accurate tracking along a predetermined flight path.

An inceptor apparatus for an aircraft having a primary inceptor member provided in the form of a stick member having a grip portion, at which the stick member can be gripped by a pilot's hand, and a secondary inceptor member provided at an upper portion of the primary inceptor member and having an actuating portion, at which the secondary inceptor member can be manually actuated by a pilot's thumb. Both inceptor members have associated a respective sensor assembly which is provided to generate electronic flight control signals or commands in response to at least one of i) pivoting movements of the respective inceptor member around each of two independent maneuvering axes associated to the inceptor member, ii) forces acting on or via the respective inceptor member in pivoting directions with respect to each of the maneuvering axes, and iii) lateral flexing or bending of the respective inceptor member.