A fault-adaptive fixed-wing UAV path planning system and method

Abstract

The invention discloses a fault-adaptive fixed-wing unmanned aerial vehicle path planning system and method, comprising a fixed-wing unmanned aerial vehicle state solving module, a fault processing module, a planning environment module, an adaptive path planning module, an adaptive tracking control module, a state integration module and a terminal display module; the solving module is used for updating and solving the flight state; the fault processing module is used for fault type analysis, performance parameter analysis and stable flight state solving; the planning environment module is used for setting the planning space boundary, obstacle information and the start and end point information of the flight mission; the adaptive path planning module can autonomously plan a path from the start point to the end point that bypasses the obstacle; the adaptive tracking control module is used for giving control instructions that meet the control performance constraints and model constraints in real time; the state integration module is used for obtaining the real-time posture of the unmanned aerial vehicle; the terminal display module is used for storing and real-time displaying the flight data, thereby realizing the integrated research from planning to tracking.

Description

Technical Field

[0001] The present invention relates to the field of fixed-wing unmanned aerial vehicle path planning, and in particular to a fault-adaptive fixed-wing unmanned aerial vehicle path planning system and method. Background Art

[0002] Fixed-wing UAVs have the characteristics of fast flight speed, long cruising time, strong anti-interference, and large carrying capacity, and have good application prospects and research significance. Intelligence and autonomy are the research focuses of the development of UAV technology in recent years, and path planning is an important part of realizing autonomous navigation and control of UAVs.

[0003] At present, path planning is mostly studied for rotary-wing UAVs, and relatively mature technologies are available for application, such as A*, RRT, genetic algorithms and other path planning methods. For fixed-wing UAVs, most existing studies treat them as a particle, and the modeling process is too abstract and ideal, resulting in poor tracking effects and crossing routes when tracking the path. After planning the path, it is often necessary to combine cumbersome trajectory optimization techniques, such as polynomial curves, B-splines, Bezier curves and other methods; secondly, the flight status and performance of the fixed-wing UAV itself are not considered enough, and the constraints of fuel consumption, flight angle, turning radius and other actual conditions are ignored, resulting in the path being realized only through simulation and unable to be tracked in the actual environment, resulting in low confidence in the simulation results.

[0004] At the same time, the model construction and fault-tolerant control research of faulty / damaged aircraft has made great progress, including fault detection and diagnosis technology, damaged aircraft model construction methods, and active and passive fault-tolerant control technology. At present, the systematic research on fixed-wing UAV path planning based on fault adaptation is still immature. In order to ensure the efficient completion of the entire flight mission, it is necessary to organically combine the above research results and build a systematic architecture for fixed-wing UAVs. This architecture can autonomously and flexibly adjust the flight plan and control parameters according to the flight status of the aircraft under faulty / damaged conditions, so that the fixed-wing UAV has the ability to "self-recover" in the faulty / damaged state. Summary of the invention

[0005] In view of this, the object of the present invention is to provide a fixed-wing UAV path planning system and method based on fault adaptation, which can autonomously and flexibly adjust the flight path and control parameters according to the flight status of the aircraft under fault / damage conditions, while meeting the fixed-wing UAV model and performance constraints.

[0006] To achieve the above object, the present invention adopts the following technical solution:

[0007] A fault-adaptive fixed-wing UAV path planning system, comprising a fixed-wing UAV state solution module, a fault processing module, a planning environment module, an adaptive path planning module, an adaptive tracking control module, a state integration module and a terminal display module;

[0008] After a fault occurs, the fixed-wing UAV state calculation module makes adjustments based on the model parameters corresponding to the fault type, including control surface fault parameters, lift surface structure fault parameters, and engine fault parameters, for updating and calculating the flight state and serving as a model constraint for the stable flight state calculation and adaptive tracking control module;

[0009] The fault processing module includes fault type analysis, performance parameter analysis and stable flight state solution; the fault processing module transmits the quantized fault parameters (performance parameters and model parameters) to the adaptive path planning module, the adaptive tracking control module and the fixed-wing UAV state solution module for adjustment through fault type analysis, such as actuator fault parameters and model structure faults, and obtains control performance constraints and planning performance constraints through performance parameter analysis, and uses the normal or faulty performance constraints and the model as inputs to the aircraft stable state solution module. The aircraft stable state solution module will solve the stable flight state and the corresponding control instructions according to the expected flight speed, altitude, climb angle, etc. through a constrained nonlinear constraint programming method;

[0010] The adaptive path planning module plans a path from the starting point to the end point, avoiding obstacles, based on the stable flight state of the aircraft under fault / normal conditions and the environmental information obtained by the planning environment module, while satisfying the planning performance constraints;

[0011] During the flight, the adaptive tracking control module gives real-time control instructions based on the real-time posture and expected path of the aircraft, while satisfying the control performance constraints and model constraints under the fault / normal conditions of the fixed-wing UAV, and transmits them to the fixed-wing UAV state solution module.

[0012] The state integration module is connected to the fixed-wing UAV state solution module and is used to integrate various state derivatives of the fixed-wing UAV during flight to obtain real-time posture.

[0013] The terminal display module is connected to the planning environment module, the adaptive path planning module and the state integration module, and is used to transmit environmental information, planned path points and heading information, and the real-time position and posture of the fixed-wing UAV during flight to the terminal for visual display.

[0014] Furthermore, the planning environment module is connected to the adaptive path planning module and the terminal display module to set the planning space boundary, obstacle information and the start and end point information of the flight mission.

[0015] Furthermore, the fixed-wing UAV state solution module also includes a wind model, an aerodynamic model, a thrust model and a six-degree-of-freedom fixed-wing motion dynamics model; the wind model simulates wind interference during flight; the aerodynamic model calculates aerodynamic force (moment) based on rudder instructions, fault parameters, and airspeed, angle of attack and sideslip angle information; the thrust model calculates thrust based on throttle instructions, engine fault parameters, and airspeed, angle of attack and sideslip angle information; the six-degree-of-freedom fixed-wing motion dynamics model calculates the derivatives of state variables based on aerodynamic force (moment) and thrust.

[0016] Furthermore, the adaptive path planning module includes a coordinated turning model, a Dubins curve, an RRT* path planning algorithm, and a path point switching mechanism;

[0017] The coordinated turning model is used to give the minimum turning radius of the fixed-wing UAV based on the stable flight state and the planning performance parameters;

[0018] The Dubins curve is used to determine the shortest path between two points on a plane under the condition of specifying the tangent directions of the starting and ending points, and satisfying the curvature constraint;

[0019] The RRT* path planning algorithm connects the sampling points through the Dubins curve to determine a feasible path from the starting point to the end point that bypasses obstacles in the environment;

[0020] The path point switching mechanism is repeatedly operated during the flight process to provide the path points and heading information expected to be reached during the flight of the fixed-wing UAV.

[0021] Furthermore, the adaptive tracking control module includes a nonlinear model predictive control structure; the nonlinear model predictive control structure is connected to the adaptive path planning module, the fixed-wing UAV state solution module and the state integration module, and gives real-time control instructions based on the real-time posture of the fixed-wing UAV and the expected path point and heading information under the condition of satisfying the control performance constraints and model constraints.

[0022] A fault-adaptive fixed-wing UAV path planning method comprises the following steps:

[0023] Step S1: Obtaining environmental information of the flight mission;

[0024] Step S2: Determine whether a fault occurs. If a fault occurs, determine the fault type and quantified fault parameters, and adjust the model and performance constraints.

[0025] Step S3: The performance parameters under normal or fault conditions and the model are used as constraints for solving the steady state of the aircraft. The steady state of the aircraft under normal / fault conditions and the corresponding control quantity under the state are output through the constrained nonlinear programming method, and step S4 is executed; if the steady flight state solution fails, it is considered that the mission cannot be completed, and the aircraft should land nearby or not perform the flight mission;

[0026] Step S4: Based on the environmental information given in step S1 and the stable flight state of the fixed-wing UAV obtained by step S3, an optimized feasible path from the starting point to the end point is obtained, which avoids obstacles in the environment and meets the planning performance constraints, including all path points and heading information that need to be tracked during the entire flight process, and the data is stored;

[0027] Step S5: monitor the entire flight mission in real time, and based on the waypoint switching mechanism, provide the waypoint information and reference heading expected to be tracked at the current moment according to the real-time position of the aircraft;

[0028] Step S6: Based on the expected path point and heading information in step S5 and the aircraft posture obtained by real-time monitoring, a control instruction that satisfies the control constraints and the fixed-wing UAV model constraints is given;

[0029] Step S7: Receive the control instruction of step S6, update the position and attitude information of the fixed-wing UAV, and update the real-time data.

[0030] Furthermore, in step S1, the planning environment module obtains the planning space and obstacle data (including quantity, size, and location) through preliminary survey to determine the starting and ending points of the fixed-wing UAV flight mission;

[0031] Furthermore, in step S2, the fault type and fault parameters are analyzed, which specifically includes the following steps:

[0032] A1: Determine whether a fault has occurred. If a fault has occurred, further determine the type of fault that affects the fixed-wing UAV model and performance;

[0033] A2: Determine whether the quantitative parameter corresponding to the fault type is a performance parameter or a model parameter. If it is a performance parameter, further determine whether it is a planning performance parameter or a control performance parameter.

[0034] A3: If it is a model parameter (control surface fault parameter, lift surface fault parameter, engine fault parameter, etc.), it will be transmitted to the corresponding position of the fixed-wing UAV state solution module for adjustment.

[0035] Furthermore, in step S3, based on the performance constraints and model constraints of the fixed-wing UAV under fault / normal conditions, the control quantities near the given expected flight state point are solved by a constrained nonlinear constraint programming method, including the deflection angles of the three control surfaces of the aileron, elevator, and rudder and the throttle opening. If the balance point exists (i.e., there is a solution), a stability analysis is performed to verify the analysis.

[0036] Further, in step S4, path planning that meets the performance constraints is performed based on the stable flight state of the fixed-wing UAV under fault / normal conditions, specifically including the following steps:

[0037] B1: According to the stable flight state given in step S3, based on the coordinated turning model, the fixed-wing UAV is kept at the current stable flight speed V g * The maximum lift coefficient under According to the coordinated turning formula, the maximum roll angle φ for coordinated turning at the current speed can be obtained: max And the corresponding minimum turning radius R min ;

[0038] B2: Use the minimum turning radius calculated above as the curvature radius of the Dubins curve;

[0039] B3: At the current stable flight altitude, based on the RRT* path planning algorithm, according to the environmental information given in step S1, the Dubins curve is used as the path connecting the two sampling points, and the heading information and path length to each path point are determined, and finally a feasible path from the starting point to the end point is obtained, which bypasses obstacles in the environment and meets the planning performance constraints;

[0040] B4: Optimize the path by cutting it, and store all the path points and heading information that need to be tracked during the entire flight process.

[0041] Furthermore, in step S6, a control instruction is given according to the aircraft posture obtained by real-time monitoring, specifically including:

[0042] C1: Receive the path point and heading information expected at the current moment;

[0043] C2: Calculate the deviation between the fixed-wing UAV’s real-time position and the desired path point and heading information;

[0044] C3: Based on the nonlinear model prediction inner and outer loop double-layer control architecture, real-time control instructions are given under the conditions of satisfying the control performance constraints and model constraints, including the three rudder angles of aileron, elevator and rudder and the throttle opening.

[0045] Further, in step S7, the state of the fixed-wing UAV is updated according to the control instruction, specifically including:

[0046] D1: Get airspeed, angle of attack and sideslip angle through wind model;

[0047] D2: Receive the control surface instruction and throttle amount instruction given in step S6 to calculate the aerodynamic force (moment) and thrust;

[0048] D3: Calculate the derivatives of state variables based on the six-degree-of-freedom fixed-wing motion dynamics model;

[0049] D4: Update the position and attitude information of the fixed-wing UAV through state integration.

[0050] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0051] The invention proposes a fault-adaptive fixed-wing unmanned aerial vehicle path planning system and method, determines the relevant technical route, constructs a relatively accurate fixed-wing unmanned aerial vehicle six-degree-of-freedom solution model through a fixed-wing unmanned aerial vehicle state solution module, including motion dynamics characteristics, wind characteristics, aerodynamic characteristics, thrust characteristics, etc.; analyzes and quantifies the fault types that may affect the performance and model of the fixed-wing unmanned aerial vehicle through a fault processing module, and gives a stable flight state under fault / normal conditions; outputs a path that meets the planning performance constraint through an adaptive path planning module based on the obtained stable flight state; tracks the planned path under the premise of meeting the current control constraint and the model constraint through an adaptive tracking control module, and monitors the environment, the reference path and the real-time data of the flight state of the fixed-wing unmanned aerial vehicle through a terminal display module; the flight state and planning performance constraint of the fixed-wing unmanned aerial vehicle are considered in the path planning process, the traceability of the path is improved, and adaptive adjustment can be performed according to the fault type, and the adaptive tracking control technology is integrated to realize the integrated research of the path from planning to tracking, so that the fixed-wing unmanned aerial vehicle can autonomously and flexibly adjust the flight path and control parameters according to the flight state under fault / normal conditions, and improve the autonomy and intelligence of the fixed-wing unmanned aerial vehicle application. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0053] Figure 1 It is a schematic diagram of a fault-adaptive fixed-wing UAV path planning system of the present invention.

[0054] Figure 2The present invention is a flow chart of a fault-adaptive fixed-wing UAV path planning method.

[0055] Figure 3 The present invention discloses a fault handling flow chart of a fault-adaptive fixed-wing UAV path planning method.

[0056] Figure 4 The present invention discloses a path planning flow chart of a fault-adaptive fixed-wing unmanned aerial vehicle path planning method.

[0057] Figure 5 The present invention discloses a tracking control flow chart of a fault-adaptive fixed-wing UAV path planning method.

[0058] Figure 6 The present invention discloses a posture solution flow chart of a fault-adaptive fixed-wing UAV path planning method.

[0059] Figure 7a It is a schematic diagram of the planned path of the fixed-wing UAV in an embodiment of the present invention when there is no fault.

[0060] Figure 7b It is a schematic diagram of the planned path when the elevator efficiency of the fixed-wing UAV in an embodiment of the present invention is lost by 30%.

[0061] Figure 8a It is a schematic diagram of the tracking trajectory of the fixed-wing UAV in an embodiment of the present invention when there is no fault.

[0062] Figure 8b It is a schematic diagram of the tracking trajectory when the elevator efficiency of the fixed-wing UAV of an embodiment of the present invention loses 30%. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0064] like Figure 1As shown, this embodiment provides a fault-adaptive fixed-wing UAV path planning system, including: a fault processing module including fault type analysis, performance parameter analysis and stable flight state solution; the fault processing module transmits quantized fault parameters (performance parameters and model parameters) to the adaptive path planning module, the adaptive tracking control module and the fixed-wing UAV state solution module through fault type analysis, such as actuator fault parameters and model structure faults, to make adjustments, obtains control performance constraints and planning performance constraints through performance parameter analysis, and uses the normal or post-fault performance constraints and the model as inputs for aircraft stable state solution, and solves the stable flight state through a constrained nonlinear constraint programming method;

[0065] After a fault occurs, the fixed-wing UAV state calculation module makes adjustments based on the model parameters corresponding to the fault type, including control surface fault parameters, lift surface structure fault parameters, and engine fault parameters, for updating and calculating the flight state and as a model constraint for the stable flight state calculation and adaptive tracking control modules;

[0066] The adaptive path planning module plans a path from the starting point to the end point, avoiding obstacles and meeting the planning performance constraints, based on the stable flight status of the aircraft under fault / normal conditions and the environmental information obtained by the planning environment module;

[0067] During the flight, the adaptive tracking control module gives real-time control instructions based on the real-time posture and expected path of the aircraft, while satisfying the control performance constraints and model constraints under the fault / normal conditions of the fixed-wing UAV, and transmits them to the fixed-wing UAV state solution module.

[0068] The state integration module is connected to the fixed-wing UAV state solution module to integrate the various state derivatives of the fixed-wing UAV during flight to obtain the real-time posture.

[0069] The terminal display module is connected to the planning environment module, the adaptive path planning module and the state integration module to transmit the environmental information, the planned path points and heading information and the real-time position and posture of the fixed-wing UAV during flight to the terminal for visual display.

[0070] The planning environment module is connected to the adaptive path planning module and the terminal display module to set the planning space boundary, obstacle information and the start and end point information of the flight mission.

[0071] The fixed-wing UAV state calculation module also includes a wind model, an aerodynamic model, a thrust model and a six-degree-of-freedom fixed-wing motion dynamics model; the wind model simulates wind interference during flight, V w =V ws +V wg , where Vws is a constant vector representing a stable ambient wind, V wg is a random process representing gusts and other atmospheric disturbances; the aerodynamic model is based on the control surface command δ ele , δ ail , δ rud and airspeed V a , angle of attack α and sideslip angle β information to calculate the aerodynamic force (moment); the thrust model is based on the throttle command δ t and airspeed V a , angle of attack α and sideslip angle β information to calculate thrust T p The six-degree-of-freedom fixed-wing motion dynamics model calculates the derivative of the state variable based on the aerodynamic force (moment) and thrust, and the expression is as follows:

[0072]

[0073]

[0074]

[0075]

[0076] In the formula, p n 、p e 、p d , u, v, w, φ, θ, ψ, p, q, r are 12 flight state quantities of the aircraft, representing the forward position, lateral position, vertical position, forward component of ground speed, lateral component of ground speed, vertical component of ground speed, roll angle, pitch angle, yaw angle, roll angular velocity, pitch angular velocity and yaw angular velocity respectively; F x ,F y ,F z are the components of the total external force acting on the aircraft along the three axes of the body coordinate system; l, m, n are the components of the total external moment around the three axes of the body coordinate system, representing the rolling moment, pitching moment and yaw moment respectively.

[0077] The adaptive path planning module includes a coordinated turning model, a Dubins curve, an RRT* path planning algorithm, and a path point switching mechanism; the coordinated turning model adjusts the fixed-wing UAV to the current stable flight speed according to the stable flight state. The maximum lift coefficient under The maximum roll angle φ for coordinated turning at the current speed can be obtained according to the following formula: max And the corresponding minimum turning radius R min :

[0078]

[0079]

[0080] ρ φ is the safety factor, which is 0.8 here, for the actual planned turning radius; the Dubins curve is used to determine the shortest path between two points on the plane under the condition of specifying the tangent direction of the starting and ending points, and satisfying R min The constraint of curvature radius; the RRT* path planning algorithm connects the sampling points through the Dubins curve to determine a feasible path from the starting point to the end point that bypasses obstacles in the environment. The algorithm can also be replaced by other path planning algorithms, such as RRT, Connect-RRT*, etc.; the path point switching mechanism is repeatedly operated during the flight to provide the path point and heading information expected to be reached during the flight of the fixed-wing UAV. For example, the judgment rule is that if the aircraft passes through the half plane determined by the current expected path point and the expected heading, then switch to the next expected path point.

[0081] Among them, the adaptive tracking control module includes a nonlinear model predictive control structure; the nonlinear model predictive control structure is connected to the adaptive path planning module, the fixed-wing UAV state solution module and the state integration module, and gives real-time control instructions according to the real-time posture of the fixed-wing UAV and the expected path point and heading information under the condition of satisfying the control performance constraints and model constraints.

[0082] The fault-adaptive fixed-wing UAV path planning system described in the present invention provides a complete fault-adaptive system for fixed-wing UAV path planning. The flight state and planning performance constraints of the fixed-wing UAV are taken into account during the path planning process, thereby improving the traceability of the path. The system can also be adaptively adjusted according to the fault type and specific action form. At the same time, adaptive tracking control technology is integrated to realize an integrated study of the path from planning to tracking, so that the fixed-wing UAV can autonomously and flexibly adjust the flight path and control parameters according to the flight state under fault / normal conditions, thereby improving the autonomy and intelligence of fixed-wing UAV applications.

[0083] Combination Figure 2 As shown, the present invention also provides a fault-adaptive fixed-wing UAV path planning method, comprising:

[0084] S1: Obtain environmental information of the flight mission, including the flight start and end points, map range and obstacle information, etc.

[0085] S2: Determine whether a fault occurs. If a fault occurs, determine the fault type and quantified fault parameters, and adjust the model and performance constraints.

[0086] S3: The performance parameters under normal or fault conditions and the model are used as constraints for solving the steady state of the aircraft. The steady state of the aircraft under normal / fault conditions and the corresponding control quantity under the state are output through the constrained nonlinear programming method, and step S4 is executed; if the steady flight state solution fails, it is considered that the mission cannot be completed, and the aircraft should land nearby or not perform the flight mission;

[0087] S4: Based on the environmental information given in step S1 and the stable flight state of the fixed-wing UAV obtained by solving step S3, an optimized feasible path from the starting point to the end point that bypasses obstacles in the environment and meets the planning performance constraints is obtained, including all path points and heading information that need to be tracked during the entire flight process, and the data is stored;

[0088] S5: Monitor the entire flight mission in real time. Based on the waypoint switching mechanism, the waypoint information and reference heading expected to be tracked at the current moment are given according to the real-time position of the aircraft.

[0089] S6: Based on the expected path point and heading information in step S5 and the aircraft posture obtained by real-time monitoring, a control instruction that satisfies the control constraints and the fixed-wing UAV model constraints is given;

[0090] S7: Receive the control instruction of step S6, update the position and attitude information of the fixed-wing UAV, and update the real-time data.

[0091] Combination Figure 3 As shown, S2 includes:

[0092] A1: Determine whether a fault has occurred. If a fault has occurred, further determine the type of fault that affects the fixed-wing UAV model and performance. For example, common fault types include actuator faults, main structure faults, and sensor faults. Furthermore, actuator faults may cause a decrease in efficiency, which is manifested as the actual rudder deflection value being reduced by a certain percentage compared to the expected deflection value, or the actual engine thrust being the expected engine thrust reduced by a certain loss factor.

[0093] A2: Determine whether the quantitative parameter corresponding to the fault type is a performance parameter or a model parameter. If it is a performance parameter, determine whether it is a planning performance parameter or a control performance parameter based on the parameter action module.

[0094] A3: If it is a model parameter (control surface fault parameter, lift surface fault parameter and engine fault parameter, etc.), it is transmitted to the corresponding position of the fixed-wing UAV state solution module for adjustment. For example, when the control surface efficiency (gain) is lost and the engine thrust decreases, the actual control instruction δ is different from the expected control instruction δ c There is a loss factor ρ δ , the expression is as follows:

[0095]

[0096] Combination Figure 4 As shown, S4 includes:

[0097] B1: According to the stable flight state given in step S3, based on the coordinated turning model, the fixed-wing UAV is kept at the current stable flight speed. The maximum lift coefficient under The maximum roll angle φ for coordinated turning at the current speed can be obtained according to the following formula: max And the corresponding minimum turning radius R min :

[0098]

[0099]

[0100] Among them, m kg is the mass of the fixed-wing UAV, g is the gravity coefficient, S is the effective wing area, ρ φ is the safety factor;

[0101] B2: Use the minimum turning radius calculated above as the curvature radius of the Dubins curve;

[0102] B3: At the current stable flight altitude, based on the RRT* path planning algorithm, according to the environmental information given in step S1, the Dubins curve is used as the path connecting the two sampling points, and the heading information and path length to each path point are determined, and finally a feasible path from the starting point to the end point is obtained, which bypasses obstacles in the environment and meets the planning performance constraints;

[0103] B4: Optimize the path by cutting it, and store all the path points and heading information that need to be tracked during the entire flight process.

[0104] Combination Figure 5 As shown, S6 includes:

[0105] C1: Receive the path point and heading information expected at the current moment;

[0106] C2: Calculate the deviation between the real-time position and posture of the fixed-wing UAV and the expected path point and heading information; C3: Based on the nonlinear model prediction of the inner and outer loop double-layer control architecture, give real-time control instructions, including the three rudder angles of aileron, elevator and rudder and the throttle opening, while satisfying the control performance constraints and model constraints.

[0107] Combination Figure 6 As shown, S7 includes:

[0108] D1: Get airspeed, angle of attack and sideslip angle through wind model;

[0109] D2: Receive the control surface instruction and throttle amount instruction given in step S6 to calculate the aerodynamic force (moment) and thrust;

[0110] D3: Calculate the derivatives of state variables based on the six-degree-of-freedom fixed-wing motion dynamics model;

[0111] D4: Update the position and attitude information of the fixed-wing UAV through state integration.

[0112] After the above steps, Table 1, Table 2, Figure 7a , Figure 7b , Figure 8a , Figure 8b The experimental effects of the present invention are shown in two cases: an aircraft without fault and an aircraft with elevator efficiency loss fault.

[0113] Example:

[0114] First, step S1 obtains environmental information, and the spatial range is

[0115]

[0116] There are 144 buildings with random configurations and the starting point coordinates are p s (-1800,-1600,200), the end point coordinate is p e (1450,1450,200).

[0117] Step S2 determines whether a fault occurs. If a fault occurs, the fault type and quantified fault parameters are determined, and the model and performance constraints are adjusted. In this embodiment, a 30% loss in elevator efficiency is taken as an example, and the actual elevator command is 70% of the expected command. Given the expected flight altitude of 200m and the flight speed of 25m / s, the solution value of the straight and level flight state obtained by step S3 is shown in Table 1.

[0118] Table 1 Solution values ​​of the straight and level flight state in two cases

[0119]

[0120] In order to reflect the difference in aircraft path planning effects before and after a fault, this embodiment adopts the idea of ​​controlling variables to ensure that the environment, start and end points of each planning are consistent. The planning parameters and results obtained in step S4 are shown in Table 2, and the visualization effect of the planned path is shown in Table 2. Figure 7a and Figure 7b .

[0121] Table 2 Comparison of path planning parameters and effects in two cases

[0122]

[0123] During the flight, steps S5-S7 are repeated to record the actual flight trajectory of the aircraft following the above-planned path, and the test results of the two cases of no fault and elevator efficiency loss fault are obtained. Figure 8a and Figure 8b Under certain fault conditions, based on the fault-adaptive fixed-wing UAV path planning method proposed in this patent, the aircraft can find a stable flight state, have the possibility of completing the mission, and can find a feasible path, which reflects its good adaptability to faults.

[0124] The specific implementation methods of the present invention are described above in conjunction with the accompanying drawings, but the above embodiments cannot be understood as limiting the scope of the present invention. The scope of protection of the present invention is defined by the accompanying claims, and any changes based on the claims of the present invention belong to the scope of protection of the present invention.

[0125] The fault-adaptive fixed-wing UAV path planning method described in the present invention provides a complete set of fault-adaptive methods for fixed-wing UAV path planning. The flight state and planning performance constraints of the fixed-wing UAV are taken into account during the path planning process, thereby improving the traceability of the path. The method can be adaptively adjusted according to the fault type and specific action form, and at the same time, the adaptive tracking control technology is integrated to realize the integrated research from path planning to tracking, so that the fixed-wing UAV can autonomously and flexibly adjust the flight path and control parameters according to the flight state under fault / normal conditions, thereby improving the autonomy and intelligence of fixed-wing UAV applications.

[0126] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0127] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A fault-adaptive fixed-wing UAV path planning system, characterized by: It includes fixed-wing UAV state solution module, fault handling module, planning environment module, adaptive path planning module, adaptive tracking control module, state integration module and terminal display module; The fixed-wing UAV state calculation module makes adjustments based on the model parameters corresponding to the fault type after a fault occurs, including control surface fault parameters, lift surface structure fault parameters and engine fault parameters, which are used for updating and calculating the flight state and as model constraints for the stable flight state calculation and adaptive tracking control module; The fault processing module includes fault type analysis, performance parameter analysis and stable flight state solution; the fault processing module transmits the quantified fault parameters to the adaptive path planning module, the adaptive tracking control module and the fixed-wing UAV state solution module through fault type analysis, obtains control performance constraints and planning performance constraints through performance parameter analysis, and uses the normal or post-fault performance constraints and the model as inputs to the aircraft stable state solution module. The aircraft stable state solution module will solve the stable flight state and the corresponding control instructions according to the expected flight speed, altitude and climb angle through the constrained nonlinear constraint programming method; The planning environment module is connected to the adaptive path planning module and the terminal display module to set the planning space boundary, obstacle information and the start and end point information of the flight mission; The adaptive path planning module plans a path from the starting point to the end point, avoiding obstacles, based on the stable flight state of the aircraft under fault / normal conditions and the environmental information obtained by the planning environment module, while satisfying the planning performance constraints; The adaptive tracking control module gives real-time control instructions according to the real-time posture and expected path of the aircraft during flight, while satisfying the control performance constraints and model constraints under the fault / normal conditions of the fixed-wing UAV, and transmits them to the fixed-wing UAV state solution module; The state integration module is connected to the fixed-wing UAV state solution module and is used to integrate various state derivatives of the fixed-wing UAV during flight to obtain real-time posture; The terminal display module is connected to the planning environment module, the adaptive path planning module and the state integration module, and is used to transmit environmental information, planned path points and heading information, and the real-time position and posture of the fixed-wing UAV during flight to the terminal for visual display.

2. A fault-adaptive fixed-wing UAV path planning system according to claim 1, characterized in that: The fixed-wing UAV state solution module also includes a wind model, an aerodynamic model, a thrust model and a six-degree-of-freedom fixed-wing motion dynamics model; the wind model simulates wind interference during flight; the aerodynamic model calculates aerodynamic force and torque based on rudder instructions, fault parameters, and airspeed, angle of attack and sideslip angle information; the thrust model calculates thrust based on throttle amount instructions, engine fault parameters, and airspeed, angle of attack and sideslip angle information; the six-degree-of-freedom fixed-wing motion dynamics model calculates the derivatives of state variables based on aerodynamic force and thrust.

3. The fault-adaptive fixed-wing UAV path planning system according to claim 1, characterized in that: The adaptive path planning module includes a coordinated turning model, a Dubins curve, an RRT* path planning algorithm and a path point switching mechanism; the coordinated turning model is used to give the minimum turning radius of the fixed-wing UAV according to the stable flight state and the planning performance parameters; the Dubins curve is used to determine the shortest path between two points on the plane under the condition of specifying the tangent direction of the starting end and the end end, and satisfying the curvature constraint; the RRT* path planning algorithm connects the sampling points through the Dubins curve to determine a feasible path from the starting point to the end point that bypasses obstacles in the environment; the path point switching mechanism is repeatedly operated during the flight to give the path points and heading information expected to be reached during the flight of the fixed-wing UAV.

4. The fault-adaptive fixed-wing UAV path planning system according to claim 1, characterized in that: The adaptive tracking control module includes a nonlinear model predictive control structure; the nonlinear model predictive control structure is connected to an adaptive path planning module, a fixed-wing UAV state solution module and a state integration module, and gives real-time control instructions according to the real-time posture of the fixed-wing UAV and the expected path point and heading information under the condition of satisfying control performance constraints and model constraints.

5. A method for implementing the fault-adaptive fixed-wing UAV path planning system of claim 1, characterized in that: The following steps are involved: Step S1: Acquire environmental information of the flight mission; Step S2: Determine whether a fault occurs. If a fault occurs, determine the fault type and quantified fault parameters, and adjust the model and performance constraints. Step S3: The performance parameters under normal or fault conditions and the model are used as constraints for solving the steady state of the aircraft. The steady state of the aircraft under normal / fault conditions and the corresponding control quantity under the state are output through the constrained nonlinear programming method, and step S4 is executed; if the steady flight state solution fails, it is considered that the mission cannot be completed, and the aircraft should land nearby or not perform the flight mission; Step S4: Based on the environmental information given in step S1 and the stable flight state of the fixed-wing UAV obtained by step S3, a feasible path from the starting point to the end point is obtained, including all path points and heading information that need to be tracked during the entire flight process, and the information is stored; Step S5: monitor the entire flight mission in real time, and based on the waypoint switching mechanism, provide the waypoint information and reference heading expected to be tracked at the current moment according to the real-time position of the aircraft; Step S6: Based on the expected path point and heading information in step S5 and the aircraft posture obtained by real-time monitoring, a control instruction that satisfies the control constraints and the fixed-wing UAV model constraints is given; Step S7: Receive the control instruction of step S6, update the position and attitude information of the fixed-wing UAV, and update the real-time data.

6. The method according to claim 5, characterized in that: In step S2, the fault type and fault parameters are analyzed, which specifically includes the following steps: A1: Determine whether a fault has occurred. If a fault has occurred, further determine the type of fault that affects the fixed-wing UAV model and performance; A2: Determine whether the quantitative parameter corresponding to the fault type is a performance parameter or a model parameter. If it is a performance parameter, further determine whether it is a planning performance parameter or a control performance parameter. A3: If it is a model parameter, including a control surface fault parameter, a lift surface fault parameter and an engine fault parameter, it will be transmitted to the corresponding position of the fixed-wing UAV state solution module for adjustment.

7. The method according to claim 5, characterized in that: In step S3, based on the performance constraints and model constraints of the fixed-wing UAV under fault / normal conditions, the control quantities near the given expected flight state point, including the deflection angles of the three control surfaces of aileron, elevator, and rudder and the throttle opening, are solved through a constrained nonlinear constraint programming method. If a balance point exists, a stability analysis is performed to verify the analysis.

8. The method according to claim 5, characterized in that: In step S4, path planning that meets performance constraints is performed based on the stable flight state of the fixed-wing UAV under fault / normal conditions, specifically including the following steps: B1: According to the stable flight state given in step S3, based on the coordinated turning model, the fixed-wing UAV is kept at the current stable flight speed. The maximum lift coefficient under According to the coordinated turning formula, the maximum roll angle φ of the coordinated turning at the current speed is obtained: max And the corresponding minimum turning radius R min ; B2: Use the minimum turning radius as the curvature radius of the Dubins curve; B3: At the current stable flight altitude, based on the RRT* path planning algorithm, according to the environmental information given in step S1, the Dubins curve is used as the path connecting the two sampling points, and the heading information and path length to each path point are determined, and finally a feasible path from the starting point to the end point is obtained; B4: Optimize the path by cutting it, and store all the path points and heading information that need to be tracked during the entire flight process.

9. The method according to claim 5, characterized in that: In step S6, control instructions are given according to the aircraft posture obtained by real-time monitoring, including: C1: Receive the path point and heading information expected at the current moment; C2: Calculate the deviation between the fixed-wing UAV’s real-time position and the desired path point and heading information; C3: Based on the nonlinear model prediction inner and outer loop double-layer control architecture, real-time control instructions are given under the conditions of satisfying the control performance constraints and model constraints, including the three rudder angles of aileron, elevator and rudder and the throttle opening.

10. The method according to claim 5, characterized in that: In step S7, the state of the fixed-wing UAV is updated according to the control instruction, specifically including: D1: Get airspeed, angle of attack and sideslip angle through wind model; D2: receiving the control surface instruction and throttle command given in step S6 to calculate the aerodynamic force and thrust; D3: Calculate the derivatives of state variables based on the six-degree-of-freedom fixed-wing motion dynamics model; D4: Update the position and attitude information of the fixed-wing UAV through state integration.

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