Aircraft optimal abortion guidance and online trajectory planning method

Abstract

The invention provides an optimal abortion guidance and online trajectory planning method for an aircraft, belongs to the technical field of industrial control systems, and solves the problems that an existing guidance and trajectory planning algorithm is low in robustness and cannot be applied when the flight height is low in a Mars environment. Comprising the steps that 1, the thrust direction of an aircraft in the descent stage is optimized based on a safe abortion guidance algorithm; 2, establishing a power-sliding-power three-stage trajectory optimization model based on an online trajectory algorithm, and introducing a tail end trajectory constraint to generate a trajectory planning scheme; and step 3, embedding the safe abortion guidance algorithm and the online trajectory optimization algorithm into an airborne computer of the aircraft, intelligently switching the safe abortion guidance algorithm and the online trajectory optimization algorithm at different flight stages of the aircraft, completing an orbit injection task of the aircraft, and ensuring accurate insertion of a tail end orbit.

Description

Technical Field

[0001] The present invention relates to an optimal abort guidance and online trajectory planning method for an aircraft, belonging to the technical field of industrial control systems. Background Art

[0002] When a Mars aircraft encounters a storm or other fault conditions, it is crucial to be able to abort the flight during the powered descent flight phase and ascend to a predetermined orbit, which poses high requirements for the efficiency, autonomy, reliability, and robustness of the guidance system. Different from the abort missions of lunar exploration, Mars has an atmosphere and a relatively large gravity. Therefore, in the initial stage of abort, the present invention proposes a safe abort guidance algorithm to quickly stop the descent of the Mars aircraft's altitude. To meet the requirements of rendezvous and docking with the orbiter, the present invention gives the terminal orbit parameters in the near-focus coordinate system and the constraint conditions of the orbit insertion time and position. On this basis, the present invention establishes an on-board trajectory optimization problem of "power - coast - power" to achieve a higher terminal orbit, and applies a convex optimization method to quickly and accurately solve the trajectory optimization problem. Finally, through numerical simulation experiments under multiple different conditions, the reliability and robustness of the algorithm are verified, and the on-board application performance of the algorithm is proved.

[0003] The following deficiencies exist in the existing aircraft guidance and control technologies: 1. Although the Mars atmospheric density is only 1% of that of the Earth, aerodynamic forces play an important role during high-speed flight, and traditional ascent guidance algorithms based on the vacuum hypothesis cannot be applied at low flight altitudes; 2. The Mars atmosphere is extremely unstable, with frequent and unpredictable atmospheric storms, which requires the guidance and trajectory planning algorithms to have excellent robustness; 3. The abort guidance method of lunar aircraft cannot be directly applied to the abort guidance mission of Mars aircraft. Summary of the Invention

[0004] The present invention aims to solve the problems that the existing guidance and trajectory planning algorithms have low robustness and cannot be applied at low flight altitudes in the Mars environment, and further proposes an optimal abort guidance and online trajectory planning method for an aircraft.

[0005] The technical solutions adopted by the present invention to solve the above problems are as follows: The present invention includes the following steps:

[0006] Step 1: Optimize the thrust direction of the aircraft during the descent phase based on the safe abort guidance algorithm;

[0007] Step 2: Establish a three-stage trajectory optimization model of power - glide - power based on the online trajectory algorithm, and introduce terminal orbit constraints to generate a trajectory planning scheme;

[0008] Step 3: Embed the safe abort guidance algorithm and the online trajectory optimization algorithm into the aircraft's on-board computer, intelligently switch between the safe abort guidance algorithm and the online trajectory algorithm at different flight stages of the aircraft, complete the aircraft's orbital insertion mission, and ensure accurate insertion of the terminal orbit.

[0009] Preferably, step 1 specifically includes:

[0010] In the rapid ascent stage of the aircraft, adjust the thrust direction to the anti-gravity direction to ensure a rapid recovery of the flight altitude, and quickly terminate the downward trend of the flight altitude by maximizing the reverse alignment of the thrust direction and the gravity vector;

[0011] When the altitude change rate is met, the aircraft enters the altitude stabilization stage, making the direction of the thrust vector point upward in the vertical plane and perpendicular to the velocity V, where the vertical plane is the plane formed by the position vector r and the velocity vector V;

[0012] The calculation formula for the thrust direction u in the rapid ascent stage is:

[0013]

[0014] In formula (1), T1 is the maximum thrust of the engine;

[0015] The calculation formula for the thrust direction u' in the altitude stabilization stage is:

[0016]

[0017] In formula (2), r s is the safe altitude, and γ is the ballistic inclination angle.

[0018] Preferably, step 2 specifically includes:

[0019] Step 2.1: Fix the terminal time trajectory optimization problem;

[0020] Step 2.2: Calculate the path constraint conditions of the thrust magnitude based on the established three-stage trajectory optimization model of power - glide - power;

[0021] Step 2.3: Calculate the coupling constraints between time and orbital parameters based on the semi-major axis a, eccentricity e, inclination i, longitude of the ascending node Ω, and perigee parameter ω of the aircraft;

[0022] Step 2.4: Based on the path constraint conditions of the thrust magnitude and the coupling constraints between time and orbital parameters, discretize, convexify, and relax and linearize the fixed terminal time trajectory optimization problem, and perform iterative solution on the relaxed and linearized fixed terminal time trajectory optimization problem to generate a planned trajectory scheme.

[0023] Preferably, step 2.1 specifically includes:

[0024] Step 2.1.1: When entering the ascending flight phase, use the remaining propellant of the lander of the aircraft to accelerate. Based on the position r of the aircraft when the lander's propellant is exhausted f1 and the velocity vector V f1 obtain the performance index of the trajectory optimization problem;

[0025] Step 2.1.2: Introduce a correction term in the performance index of the trajectory optimization problem, and combine it with the path constraint ||u|| = T1 to obtain a fixed terminal time trajectory optimization problem;

[0026] The expression of the performance index of the trajectory optimization problem is:

[0027]

[0028] The expression of the performance index of the trajectory optimization problem after adding the correction term is:

[0029]

[0030] The expression of the fixed terminal time trajectory optimization problem is:

[0031]

[0032] In formula (5), x = [r, V, m] is the state variable, f is the right side of the differential dynamic equation, x(t0) is the initial condition, t0 is the initial time, and x0 is the initial state.

[0033] Preferably, Step 2.2 specifically includes:

[0034] Based on the established three-stage trajectory optimization model of power - glide - power, divide the ascending trajectory of the aircraft into a power propulsion stage, a thrustless glide stage, and a secondary power propulsion stage. Define the working time of the power propulsion stage of the aircraft engine as t1, the end time of the thrustless glide stage as t1', and the working time of the secondary power propulsion stage as t2. Among them, the thrust in the thrustless glide stage is zero, and the thrust remains at the maximum value in the engine power propulsion stage and the secondary power propulsion stage. Calculate the performance index of the minimum propellant problem and the path constraint conditions of the thrust magnitude;

[0035] The expression of the performance index of the minimum propellant problem is:

[0036] min J = t1 + t2 - t1' (6);

[0037] The expression of the path constraint conditions of the thrust magnitude is:

[0038]

[0039] Preferably, step 2.3 specifically includes:

[0040] Step 2.3.1: Obtain the semi-major axis a, eccentricity e, inclination i, longitude of the ascending node Ω, perigee parameter ω, and the end position r in the perifocal coordinate system of the aircraft f =[r fx , r fy , r fz and the end velocity V f =[V fx , V fy , V fz , where the direction of the end velocity is tangent to the target orbit;

[0041] Step 2.3.2: Calculate the coupling constraints between time and orbital parameters based on the parameters obtained in step 2.3.1. The coupling constraints between time and orbital parameters include the orbital plane angle constraint, the standard elliptical orbit terminal position constraint, the circular orbit terminal position constraint, the end velocity direction constraint, the angular momentum constraint, and the injection time dynamic constraint;

[0042] The expression of the orbital plane angle constraint is:

[0043]

[0044] The expression of the standard elliptical orbit terminal position constraint is:

[0045]

[0046] where b is the semi-minor axis and c is the semi-focal length;

[0047] The expression of the circular orbit terminal position constraint is:

[0048]

[0049] The expression of the end velocity direction constraint is;

[0050]

[0051] The expression of the angular momentum constraint is:

[0052] h * =r f ×V f =r xf V yf -r yf V xf (13);

[0053] The expression of the injection time dynamic constraint is:

[0054]

[0055] In formula (14), is the ideal terminal flight time of the vehicle during rendezvous, is the eccentric anomaly, t2 is the actual flight time, and ψ f is the eccentric anomaly.

[0056] Preferably, step 2.4 specifically includes:

[0057] Step 2.4.1: Use the flip-Radau pseudospectral method with collocation points in the domain (-1, 1] to calculate the discretized equation of the fixed terminal time trajectory optimization problem;

[0058] Step 2.4.2: Transform the discretized equation system into discontinuous infinite-dimensional dynamic constraint conditions;

[0059] Step 2.4.3: Based on the path constraints and discontinuous infinite-dimensional dynamic constraint conditions, obtain Problem I and Problem II, and perform discretization and lossless convexification on Problem I and Problem II to obtain the processed Problem I and Problem II;

[0060] Step 2.4.4: Apply the first-order Taylor series expansion to approximate the processed Problem I and Problem II to obtain the linear Problem I and Problem II;

[0061] Step 2.4.5: Introduce a slack variable δ h in the linear problem, and according to the functions in Problem I and Problem II after introducing the slack variable δ h , perform iterative solution on the performance index until is less than the preset value, output the optimal state value, and use the path where the optimal state value is located as the trajectory planning scheme, where ||δ h || converges to zero during the iteration process;

[0062] The expression of the discretized equation is:

[0063]

[0064] In formula (15), D is the flip-Radau pseudospectral differential matrix, τ i , (i = 1,..., N1) are the collocation points in the domain (-1, 1], N is the number of collocation points, x is the state variable, u is the control variable, and [t0, t f is mapped to (-1, 1] during the discretization process, and its expression is:

[0065]

[0066] The expression of the discontinuous infinite-dimensional dynamic constraint condition is:

[0067]

[0068] Problem I is expressed as:

[0069] ||u||≤T1 (18);

[0070] The expression of Problem II is:

[0071] ||u||≤T2 t<t1 or t> t1′ (19);

[0072] The expressions of Problem I and Problem II after processing are:

[0073]

[0074] In formulas (20)-(22), is the performance indicator function, is the equality constraint function, including the discrete dynamic equations and initial / terminal conditions, is the inequality constraint function, including the relaxed thrust magnitude constraint and formula;

[0075] The linearized expressions of Problem I and Problem II are:

[0076]

[0077] In formulas (23)-(26), is the state variable of the kth interaction, is a constant term;

[0078] Introducing slack variables δ h The linearized expressions of Problem I and Problem II are:

[0079]

[0080] In formula (27)-formula (30), δ h is the dynamic deviation, λ is the penalty parameter, and minJ is the performance indicator.

[0081] Preferably, step 3 specifically includes:

[0082] During the ascent phase of the spacecraft, an online trajectory algorithm is used; when approaching orbital insertion, it switches to the safe abort guidance command, quickly pulls up, and adjusts the thrust direction to the anti-gravity direction to eliminate the risk of collision; when the spacecraft recovers and stabilizes in altitude, the trajectory is corrected through an iterative optimization convex optimization method to ensure accurate insertion of the terminal orbit.

[0083] The beneficial effects of the present invention are:

[0084] 1. To ensure safety and optimize the propellant, the calculation method of the abort guidance command proposed by the present invention is separated according to the rate of change of altitude. At the same time, the lateral deviation in the atmosphere is considered and corrected.

[0085] 2. The trajectory optimization problem of the present invention is formulated as an optimal control problem with the minimum propellant consumption, and its terminal constraint conditions are the time position and orbital elements in the near-focus coordinate system to achieve rapid rendezvous and docking with the orbiter.

[0086] 3. The present invention deeply analyzes the advantages and disadvantages of the optimal guidance and trajectory online planning algorithms, introduces the switching strategy between trajectory online optimization and iterative guidance, and can combine the advantages of the two algorithms to complete the orbital insertion task.

[0087] 4. The present invention verifies the algorithm through numerical simulation experiments. First, simulations are carried out under different wind conditions 5 seconds before landing. Then, considering different abort times and storm conditions, Monte Carlo simulations are carried out. The simulation results show that the accuracy of the online trajectory planning and guidance algorithm can reach the level of traditional guidance algorithms such as iterative guidance. The online trajectory optimization method can calculate the optimal trajectory and guidance command within 2 to 6 seconds, meet the required accuracy and all constraint conditions. Finally, the experimental results show that the algorithm proposed by the present invention has the value of airborne application.

[0088] 5. The present invention deeply analyzes the advantages and disadvantages of the optimal guidance and trajectory online planning algorithms, introduces the switching strategy between trajectory online optimization and iterative guidance, and can combine the advantages of the two algorithms to complete the orbital insertion task. In the Mars exploration mission, special situations such as Mars storms or system failures often occur. Considering the long distance between Mars and the Earth and the inability to establish real-time communication, the Mars aircraft must have the ability of autonomous trajectory online planning and guidance in case of emergency. Therefore, the present invention proposes a method for safe abort guidance and on-board optimization of the ascent trajectory of a Mars aircraft. First, considering the safe abort to avoid collision with the ground during the descent flight phase, the present invention proposes a fast altitude recovery guidance algorithm and gives an instruction optimization calculation method after the altitude starts to rise. Then, during the ascent phase of the Mars aircraft, considering the subsequent optimal rendezvous and docking, the present invention proposes an online trajectory optimization method that takes into account the orbit insertion time and orbital parameters. Finally, the present invention proposes a switching strategy between the trajectory planning algorithm and the traditional orbital insertion guidance algorithm to achieve high-precision orbital insertion. Brief Description of the Drawings

[0089] Figure 1 It is a schematic flow chart of a method for optimal abort guidance and online trajectory planning of an aircraft provided by the present invention;

[0090] Figure 2Schematic diagram of the flight phase during the entire aborted flight mission provided by the present invention;

[0091] Figure 3 Orbit insertion limit diagram provided by the present invention;

[0092] Figure 4 Schematic diagram of the abort, ascent, and insertion flight procedures provided by the present invention;

[0093] Figure 5 Altitude curve of the Mars atmospheric storm test provided by the present invention;

[0094] Figure 6 Velocity curve graph of the Mars atmospheric storm test provided by the present invention;

[0095] Figure 7 γ curve graph of the Mars atmospheric storm test provided by the present invention;

[0096] Figure 8 σ curve graph of the Mars atmospheric storm test provided by the present invention;

[0097] Figure 9 Thrust curve graph of the Mars atmospheric storm test provided by the present invention;

[0098] Figure 10 Monte Carlo test altitude curve graph provided by the present invention;

[0099] Figure 11 Monte Carlo test velocity curve graph provided by the present invention;

[0100] Figure 12 Monte Carlo test γ curve graph provided by the present invention;

[0101] Figure 13 Monte Carlo test σ curve graph provided by the present invention;

[0102] Figure 14 Schematic diagram of the time deviation of 5000 cases in the simulation experiment provided by the present invention;

[0103] Figure 15 Schematic diagram of the remaining propellant of 5000 cases in the simulation experiment provided by the present invention. Detailed implementation manners

[0104] Detailed implementation manner one: Combine Figure 1-4 To illustrate this implementation manner, as Figure 1 shown, the steps of a method for optimal abort guidance and online trajectory planning of an aircraft described in this implementation manner include:

[0105] S1: Obtain the three-dimensional point-particle motion equation and aerodynamic force of the rocket-powered Mars rover in a rectangular coordinate system with the center of Mars as the origin.

[0106] S2: Optimize the thrust direction during the descent phase of the vehicle based on the safe abort guidance algorithm, establish a three-stage trajectory optimization model of power-glide-power based on the online trajectory algorithm, and introduce end-orbit constraints to generate a trajectory planning scheme.

[0107] S201: Safe abort guidance command.

[0108] When an emergency occurs during the powered descent phase, quickly increase the vehicle's altitude change rate to a positive value by adjusting the thrust vector direction. The specific steps are as follows:

[0109] S20101: Rapid ascent stage: During the rapid ascent stage of the vehicle, adjust the thrust direction to the anti-gravity direction to ensure a rapid recovery of the flight altitude, and quickly terminate the downward trend of the flight altitude by maximizing the reverse alignment of the thrust direction and the gravity vector.

[0110] The calculation formula for the thrust direction u during the rapid ascent stage is:

[0111]

[0112] In formula (1), T1 is the maximum thrust of the engine.

[0113] S20102: Altitude stabilization stage: When the altitude change rate is reached, the vehicle enters the altitude stabilization stage, making the direction of the thrust vector point upward in the vertical plane and perpendicular to the velocity V, where the vertical plane is the plane formed by the position vector r and the velocity vector V.

[0114] The calculation formula for the thrust direction u' during the altitude stabilization stage is:

[0115]

[0116] In formula (2), r s is the safe altitude, and γ is the ballistic inclination angle.

[0117] During this stage, by optimizing the orthogonality of the thrust direction and the velocity vector, correcting the lateral deviation and reducing the propellant consumption, ensure the safety and trajectory accuracy of the subsequent ascent stage.

[0118] S202: Online trajectory planning algorithm.

[0119] As Figure 1 shown, the core of this stage lies in establishing a three-stage trajectory optimization model of "power-glide-power" and introducing end-orbit constraints as Figure 2 shown. The specific steps are as follows:

[0120] S20201: Fixed terminal time trajectory optimization problem;

[0121] When issuing the termination command, the lander must still have some propellant left. All this propellant can be used for acceleration during the ascent flight phase. Since the propellant flow rate is fixed at the maximum value, when the propellant runs out, the terminal time is also fixed. The trajectory optimization problem of the lander's rocket engine working flight phase is a fixed end-time problem, and its performance index takes into account the end energy:

[0122]

[0123] In formula (3), r f1 , V f1 are respectively the position and velocity vectors of the vehicle when the lander's propellant is exhausted;

[0124] As the speed increases, the deviation of the terminal velocity direction must be considered to avoid large corrections to the velocity direction during high-speed flight, thus consuming more propellant. Therefore, a correction term is added to the performance index in this embodiment:

[0125]

[0126] Generally, the magnitude of the rocket engine's thrust is regarded as non-adjustable and should satisfy the following path constraint: ||u|| = T1, and the fixed end-time trajectory optimization problem is:

[0127]

[0128] In formula (5), x = [r, V, m] is the state variable, f is the right side of the differential dynamic equation, x(t0) is the initial condition, t0 is the initial time, and x0 is the initial state.

[0129] S20202: Power - glide - power three-stage trajectory optimization model;

[0130] This embodiment divides the ascent trajectory into three stages: power propulsion, thrust-free glide, and secondary power propulsion, in order to save propellant and adjust the terminal orbit insertion time. The first working time of the rocket engine is defined as t1, the end time of the glide flight phase is t1' (which is also the second ignition time of the rocket engine), and the end time of the second working of the rocket engine is t2. Since the propellant flow rate is fixed, the minimum propellant problem is equivalent to the minimum engine working time problem, and its performance index can be written as:

[0131] min J = t1 + t2 - t1' (6);

[0132] During the gliding flight phase, the thrust is zero. During the operating time of the rocket engine, the magnitude of the thrust is constant and remains at its maximum value. Therefore, the path constraint conditions for the magnitude of the thrust can be expressed as:

[0133]

[0134] S20203: Coupling Constraints between Time and Orbital Parameters:

[0135] The terminal orbit accuracy needs to meet the following key indicators: semi-major axis a, eccentricity e, inclination i, longitude of the ascending node Ω, argument of perigee ω. In addition, to meet the requirements of rendezvous and docking with the orbiter, the insertion time and position need to be constrained. The specific constraint conditions are as follows:

[0136] Given the terminal position r f =[r fx ,r fy ,r fz and velocity V f =[V fx ,V fy ,V fz , determine the following constraints:

[0137] A) Orbital Plane Accuracy

[0138] To ensure the accuracy of the longitude of the ascending node, the terminal constraint conditions in the near-focus coordinate system need to be met.

[0139]

[0140] B) Terminal Position Constraint

[0141] The terminal position must be on the target orbit. When the target orbit is an elliptical orbit, according to the standard elliptical equation, the terminal position constraint conditions are:

[0142]

[0143] where b is the semi-minor axis and c is the semi-focal length, and they satisfy the following equation:

[0144]

[0145] When the target orbit is a circular orbit, that is, the eccentricity e of the target orbit is equal to zero, formula (12) can be rewritten as:

[0146]

[0147] C) Terminal Velocity Direction Constraint

[0148] The direction of the terminal velocity must be tangent to the target orbit and satisfy the following equation:

[0149]

[0150] D) Conservation of angular momentum

[0151] The dimensionless angular momentum of the target orbit can be calculated by the following formula:

[0152] h * = a(1 - e 2 )(13);

[0153] Considering formula (11), the terminal constraint condition of angular momentum can be:

[0154] h * = r f × V f = r xf V yf - r yf V xf (14);

[0155] E) Dynamic constraint of orbit insertion time

[0156] To achieve an accurate relative position with the orbiter on the target orbit. The terminal position of the vehicle should be determined by the flight time according to the position of the orbiter. If the ideal terminal flight time and eccentric anomaly of the vehicle at rendezvous are then the actual flight time and eccentric anomaly [t2, ψ f need to satisfy the following dimensionless Kepler time equation:

[0157]

[0158] S20203: As Figure 3 shown, in this embodiment, in the near-focus coordinate system, the discretization and convexification of the trajectory optimization problem are established;

[0159] (1) Pseudospectral discretization

[0160] In order to achieve high-precision discretization of terminal constraints at a finite number of discrete points, the present invention adopts the flip-Radau pseudospectral method for collocation points in the domain (-1, 1], and the discretization equation (16) of formula (5) is as follows:

[0161]

[0162] In formula (16), D is the flip-Radau pseudospectral differential matrix, τ i , (i = 1,..., N1) are the collocation points in the domain (-1, 1], N is the number of collocation points, x is the state variable, and u is the control variable

[0163] Time domain [t0, t fIt is mapped to (-1, 1] during the discretization process, that is,

[0164]

[0165] Now, the infinite-dimensional dynamic constraint conditions with continuous time are transformed into formulas (16) and (17); it should be noted that Problem II includes three flight phases with different thrust magnitudes (power - glide power). To achieve high performance, it is divided into three discontinuous parts:

[0166]

[0167] (2) Problem convexification

[0168] The current trajectory optimization problem is finite - dimensional but non - convex. Apply lossless convexification and continuous convexification to transform the original optimization problem into a convex problem.

[0169] First, adopt the lossless convexification technique to relax the non - convex thrust magnitude constraint into a convex constraint:

[0170] Problem I:

[0171] ||u||≤T1 (19);

[0172] Problem II:

[0173] ||u||≤T2 t<t1 or t>t1′ (20);

[0174] Multiple papers ([31, 35, 36]) have proved that the solution of the relaxed convex problem is exactly the solution of the original non - convex problem ([31, 35, 36]), so this implementation method will not be elaborated here.

[0175] After discretization and lossless convexification, Problem I and Problem II can be simply expressed as:

[0176] Problem I / II:

[0177]

[0178] In formulas (21) - (23), represents the performance index function. The equality constraint function includes the discrete dynamic equation and the initial / terminal conditions. The inequality constraint function includes the relaxed thrust magnitude constraint and the formula. Obviously, the function of the performance index is non - convex, and the function of the inequality constraint is non - linear. Therefore, the trajectory optimization problem is non - convex and cannot be directly solved by IPM. The most widely used convexification technique is continuous convexification. All non - linear functions are approximated by the first - order Taylor series expansion:

[0179] Linearization Problem I / II:

[0180]

[0181] In formulas (24)-(27), is the state variable of the k-th interaction. is a constant term that can be ignored in the performance index. All partial derivative terms can be calculated according to the functions in Problem I / II. Iteratively solve the linearization problem I / II until is small enough, then the optimal state value can be calculated.

[0182] (2) Relaxed Linearization

[0183] To avoid the situation where the initial guess of the problem is inaccurate during the iterative search for the optimal solution, resulting in the non-existence of the solution to the problem, a relaxation variable δ h is adopted to ensure the convergence of the algorithm.

[0184] Relaxed Linearization Problem I / II:

[0185]

[0186] In formulas (29)-(31), δ h represents the dynamic deviation. λ is the penalty parameter. ||δ h || should converge to zero during the iteration process. Otherwise, the penalty parameter λ should be large to ensure the convergence of the algorithm.

[0187] Through the above technical means, in this embodiment, the trajectory optimization problem is formulated as an optimal control problem with the minimum propellant consumption, and its terminal constraint conditions are the time position and orbital elements in the near-focus coordinate system to achieve rapid rendezvous and docking with the orbiter.

[0188] S3: Embed the safe abort guidance algorithm and the online trajectory optimization algorithm into the aircraft-borne computer of the vehicle, and intelligently switch between the safe abort guidance algorithm and the online trajectory algorithm at different flight stages of the vehicle to complete the vehicle's orbit insertion mission and ensure accurate insertion of the terminal orbit.

[0189] S301: Adopt the online trajectory planning algorithm in the initial ascent stage to ensure real-time optimization of the path.

[0190] S302: When approaching orbit insertion, switch to the iterative guidance algorithm to improve the accuracy of orbit insertion.

[0191] S303: According to the thrust deviation coefficient, dynamically balance the real-time performance and accuracy, and make a timely switch.

[0192] This embodiment deeply analyzes the advantages and disadvantages of the optimized guidance and trajectory online planning algorithms, introduces the switching strategy between trajectory online optimization and iterative guidance, and can combine the advantages of the two algorithms to complete the orbital insertion mission.

[0193] As Figure 4 shown, the flight program and steps designed in this embodiment include:

[0194] 1) Abort lift-off: Rapidly ascend to eliminate the risk of collision with the ground.

[0195] 2) Online planning: Optimize the propellant consumption in real time to generate an initial trajectory estimate.

[0196] 3) Iterative guidance correction: Use the iterative guidance method to further correct the trajectory and ensure accurate orbital insertion.

[0197] Specific embodiment two: Combining Figures 5-15 to illustrate this embodiment, in order to verify the technical effects of the first specific embodiment, the following simulation experiments are designed in this embodiment:

[0198] (1) Experimental purpose

[0199] Verify the effectiveness and reliability of the optimal abort guidance and online trajectory planning methods for a Mars spacecraft.

[0200] Experimental setup

[0201] 1) Experimental platform: Run the simulation on a computer equipped with an Intel Core i7 processor at 2.80 GHz.

[0202] 2) Software tool: Use the MOSEK software for convex optimization.

[0203] 3) Simulation environment: Establish a Mars atmosphere model and a gravitational field;

[0204] (2) Parameter settings:

[0205] 1) Given the target orbit parameters, including the semi-major axis, eccentricity, inclination, longitude of the ascending node, and perigee parameter.

[0206] 2) Set the initial state variables of the spacecraft, including position, velocity, and altitude.

[0207] 3) Thrust parameters and fuel limit conditions.

[0208] (3) Simulation conditions

[0209] Landing point setting: The ideal longitude and latitude of the landing point on Mars are [109.9°, 25.1°].

[0210] Condition constraints: The rate of change of the thrust direction does not exceed 5 degrees per second to ensure rapid docking with the orbiter.

[0211] (4) Storm condition experiment

[0212] Mars atmospheric storm simulation: Assume that the abort landing is initiated 5 seconds before the vehicle lands, and the wind speed of the Mars atmospheric storm is 180 m / s.

[0213] Wind direction consideration: The test includes the impact of the atmospheric wind direction in four directions, east, south, west, and north, on the guidance system. The storm test results are as Figures 5-9 shown.

[0214] (5) Monte Carlo test

[0215] 1) Random condition selection: Randomly select the abort time between 5 seconds and 50 seconds before landing. The wind speed randomly varies between 120 m / s and 180 m / s.

[0216] 2) Multiple sets of trajectory simulations: Conduct 5000 simulations under random conditions, analyze the statistical data of the orbit injection parameters, time, and remaining propellant, as well as the functional relationships of the altitude, speed, γ, and σ of 5000 sets of random trajectories with time. A standard powered descent trajectory is also plotted. The test results are as Figures 10-15 shown.

[0217] (6) Analysis of experimental results

[0218] 1) Safety verification: In all cases, the flight altitude of the spacecraft is always greater than 0, ensuring flight safety.

[0219] 2) Orbit accuracy: The deviations of all orbit parameters can reach the accuracy of traditional orbit injection guidance methods, and the constraints of orbit injection time and position are considered, reducing the difficulty of subsequent rendezvous and docking missions.

[0220] 3) Propellant consumption: The average remaining propellant is 7587 kg, which can be used for subsequent rendezvous, docking, and orbit return missions.

[0221] 4) Online application: When using the convex optimization algorithm for trajectory planning, the average time consumption is 2.2 seconds, and the maximum time consumption is 5.3 seconds, which can meet the needs of online applications.

[0222] In this experiment, the main components of the design and construction of the Mars probe include the orbiter, lander, ascender, and propulsion system; the orbiter is used to maintain the Mars orbit for docking with the return capsule; the lander and ascender are responsible for the safe landing and launch of the payload. The Ruijin system includes multi-stage chemical rockets and is equipped with an efficient fuel management system. The multi-stage chemical rockets provide controllable thrust to achieve trajectory optimization, and the efficient fuel management system supports long-duration flight missions. The Computational Guidance and Control System (CG&C) uses an on-board computer for real-time trajectory analysis and optimization; applies high-speed computing technology to quickly generate guidance commands and enhance on-board autonomy.

[0223] As described above, it is only the preferred embodiment of the present invention, and it does not impose any form of limitation on the present invention. Although the present invention has been disclosed above with the preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can, within the scope of the technical solution of the present invention, make some changes or modifications to the above-disclosed technical content to form equivalent embodiments of equivalent changes. However, as long as it does not depart from the content of the technical solution of the present invention, and according to the technical essence of the present invention, within the spirit and principle of the present invention, any simple modification, equivalent replacement, and improvement made to the above embodiments still fall within the protection scope of the technical solution of the present invention.

Claims

1. An optimal abort guidance and online trajectory planning method for an aircraft, characterized in that, The steps of the optimal abort guidance and online trajectory planning method for an aircraft include: Step 1: Optimize the thrust direction of the aircraft during the descent phase based on the safe abort guidance algorithm; Step 2: Establish a three-stage trajectory optimization model of power - glide - power based on the online trajectory algorithm, introduce terminal orbit constraints, and generate a trajectory planning scheme; Step 3: Embed the safe abort guidance algorithm and the online trajectory optimization algorithm into the aircraft's on-board computer, intelligently switch between the safe abort guidance algorithm and the online trajectory algorithm at different flight stages of the aircraft, complete the aircraft's orbital insertion task, and ensure accurate insertion of the terminal orbit.

2. The optimal abort guidance and online trajectory planning method for an aircraft according to claim 1, characterized in that Step 1 specifically includes: During the rapid ascent phase of the aircraft, adjust the thrust direction to the anti-gravity direction to ensure a rapid recovery of the flight altitude, and quickly terminate the downward trend of the flight altitude by maximizing the reverse alignment of the thrust direction and the gravity vector; When the altitude change rate is such that the aircraft enters the altitude stabilization phase, the direction of the thrust vector points upward in the vertical plane and is perpendicular to the velocity V, where the vertical plane is the plane formed by the position vector r and the velocity vector V; The calculation formula for the thrust direction u during the rapid ascent phase is: In formula (1), T1 is the maximum thrust of the engine; The calculation formula for the thrust direction u' during the altitude stabilization phase is: In formula (2), r s is the safety height, and γ is the ballistic inclination angle.

3. A method for optimal abort guidance and online trajectory planning of an aircraft according to claim 1, characterized in that, Step 2 specifically includes: Step 2.1: Solve the fixed terminal time trajectory optimization problem; Step 2.2: Calculate the path constraint conditions for the thrust magnitude based on the established three-stage trajectory optimization model of power - glide - power; Step 2.3: Calculate the coupling constraints between time and orbit parameters based on the semi-major axis a, eccentricity e, inclination i, longitude of the ascending node Ω, and argument of perigee ω of the aircraft; Step 2.4: Based on the path constraint conditions for the thrust magnitude and the coupling constraints between time and orbit parameters, discretize, convexify, and relax linearize the fixed terminal time trajectory optimization problem, and iteratively solve the relaxed linearized fixed terminal time trajectory optimization problem to generate a planned trajectory scheme.

4. An optimal abort guidance and online trajectory planning method for an aircraft according to claim 3, characterized in that, Step 2.1 specifically includes: Step 2.1.1: When entering the ascending flight phase, accelerate using the remaining propellant of the lander of the aircraft, and based on the position r of the aircraft when the lander's propellant is exhausted f1 and the velocity vector V f1 obtain the performance index of the trajectory optimization problem; Step 2.1.2: Introduce a correction term into the performance index for obtaining the trajectory optimization problem, and combine with the path constraint ||u|| = T1 to obtain the fixed terminal time trajectory optimization problem; The expression of the performance index of the trajectory optimization problem is: The expression of the performance index of the trajectory optimization problem after adding the correction term is: The expression of the fixed terminal time trajectory optimization problem is: In formula (5), x = [r, V, m] is the state variable, f is the right side of the differential dynamic equation, x(t0) is the initial condition, t0 is the initial time, and x0 is the initial state.

5. A method for optimal abort guidance and online trajectory planning of an aircraft according to claim 3, characterized in that Step 2.2 specifically includes: Based on the established three-stage trajectory optimization model of power - glide - power, divide the aircraft's ascent trajectory into a power propulsion stage, a thrust-free glide stage, and a secondary power propulsion stage. Define the working time of the power propulsion stage of the aircraft engine as t1, the end time of the thrust-free glide stage as t1', and the working time of the secondary power propulsion stage as t2. Among them, the thrust in the thrust-free glide stage is zero, and the thrust remains at the maximum value in the engine power propulsion stage and the secondary power propulsion stage. Calculate the performance index of the minimum propellant problem and the path constraint conditions for the thrust magnitude; The expression of the performance index of the minimum propellant problem is: min J = t1 + t2 - t1' (6); The expression of the path constraint conditions for the thrust magnitude is:

6. The optimal abort guidance and online trajectory planning method for an aircraft according to claim 3, characterized in that Step 2.3 specifically includes: Step 2.3.1: Obtain the semi-major axis a, eccentricity e, inclination i, longitude of the ascending node Ω, argument of perigee ω of the aircraft, and the end position r in the perifocal coordinate system f =[r fx , r fy , r fz and the end velocity V f =[V fx , V fy , V fz , where the direction of the end velocity is tangent to the target orbit; Step 2.3.2: Calculate the coupling constraints of time and orbit parameters based on the parameters obtained in step 2.3.1, where the coupling constraints of time and orbit parameters include orbit plane angle constraints, standard elliptical orbit terminal position constraints, circular orbit terminal position constraints, terminal velocity direction constraints, angular momentum constraints, and orbit entry time dynamic constraints; The expression of the orbital plane angle constraint is: The expression of the terminal position constraint of the standard elliptical orbit is: In formula (9) and formula (10), b is the semi-minor axis, and c is the semi-focal length; The expression of the circular track terminal position constraint is: The expression of the terminal velocity direction constraint is: The expression of angular momentum constraint is: h * = r f × V f = r xf V yf - r yf V xf (13); The expression of the dynamic constraint of orbital entry time is: In formula (14), is the ideal terminal flight time of the aircraft at rendezvous, is the eccentric anomaly, t2 is the actual flight time, and ψ f is the eccentric anomaly.

7. A method for optimal abort guidance and online trajectory planning of an aircraft according to claim 3, characterized in that Step 2.4 specifically includes: Step 2.4.1: Use the flip-Radau pseudospectral method to calculate the discretized equation of the fixed terminal time trajectory optimization problem by configuring points in the domain (-1,1]); Step 2.4.2: Convert the discretized equations into discontinuous infinite-dimensional dynamic constraints; Step 2.4.3: Obtain Problem I and Problem II based on the path constraint and the discontinuous infinite-dimensional dynamic constraint conditions, discretize Problem I and Problem II and perform lossless convexity processing on Problem I and Problem II to obtain processed Problem I and Problem II; Step 2.4.4: Approximate the processed Problems I and II by using the first-order Taylor series expansion to obtain the linear Problems I and II; Step 2.4.5: Introduce a slack variable δ into the linear problem h , and iteratively solve the performance index according to the functions in Problem I and Problem II after introducing the slack variable δ h until is less than the preset value, output the optimal state value, and use the path where the optimal state value is located as the trajectory planning scheme, where ||δ h || converges to zero during the iteration; The expression of the discretized equation is: In Equation (15), D is the flip-Radau pseudospectral differential matrix, and τ i , (i = 1, …, N1) are the collocation points in the domain (-1, 1], N is the number of collocation points, x is the state variable, u is the control variable, and [t0, t f is mapped to (-1, 1] during the discretization process, and its expression is: The expression of discontinuous infinite-dimensional dynamic constraint is: Problem I is expressed as: ||u||≤T1 (18); The expression of Problem II is: ||u||≤T2 t<t1or t> t′1 (19); The expressions of Problem I and Problem II after processing are: In Formulas (20)-(22), L is a performance index function, is an equality constraint function, including discrete dynamic equations and initial / terminal conditions, is an inequality constraint function, including relaxed thrust magnitude constraints and formulas; The linearized expressions of Problem I and Problem II are: In Formulas (23)-(26), is the state variable of the k-th interaction, is the constant term; Introduce the slack variable δ h The expressions of the linearized problems I and II after that are as follows: In formulas (27) - (30), δ h is the dynamic deviation, λ is the penalty parameter, and minJ is the performance index.

8. A method for optimal abort guidance and online trajectory planning of an aircraft according to claim 1, characterized in that, Step 3 specifically includes: During the ascent phase of the spacecraft, an online trajectory algorithm is used; when approaching orbital insertion, it switches to the safe abort guidance command, quickly pulls up, and adjusts the thrust direction to the anti-gravity direction to eliminate the risk of collision; when the spacecraft recovers and stabilizes in altitude, the trajectory is corrected through an iterative optimization convex optimization method to ensure accurate insertion of the terminal orbit.

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