Tilt-rotor unmanned aerial vehicle transition section fixed time control method considering state constraint

Abstract

The invention discloses a tilt-rotor unmanned aerial vehicle transition section fixed time control method considering state constraints. The tilt-rotor unmanned aerial vehicle transition section fixed time control method comprises the steps that a tilt-rotor unmanned aerial vehicle longitudinal motion switching nonlinear dynamic model is established; designing a tilt corridor calculation method for the transition section of the tilt-rotor unmanned aerial vehicle, and constructing an asymmetric time-varying obstacle Lyapunov function to solve a state constraint problem; designing a neural network system for estimating strong modeling uncertainty caused by complex pneumatic coupling between the rotor wing and the wing; designing a fixed-time adaptive flight control law, and compensating unknown modeling uncertainty by using a neural network system signal; a closed-loop multi-Lyapunov function is constructed, parameters of a flight control law are designed and analyzed, it is ensured that the state of the transition section system of the unmanned aerial vehicle is always kept within a constraint range, and stability within fixed time can be achieved. The tilt-rotor unmanned aerial vehicle longitudinal transition section fixed time control method solves the problem of tilt-rotor unmanned aerial vehicle longitudinal transition section fixed time control under the conditions of strong uncertainty and state constraint.

Description

Technical Field

[0001] The present invention belongs to the technical field of aircraft safety control, and particularly relates to a fixed-time control method for the transition section of a tilt-rotor unmanned aerial vehicle considering state constraints. Background Art

[0002] With the continuous development of aviation technology, people's requirements for the performance of aircraft are also constantly increasing. Tilt-rotor unmanned aerial vehicles integrate the functions of helicopters and fixed-wing aircraft, can take off and land vertically without a runway and adapt to complex terrains, have the capabilities of precise hovering and high-speed cruising, and are widely used in military, civilian, and commercial fields. Especially, they have great application potential in the low-altitude economy such as agricultural monitoring and logistics, and have become the focus of global aviation research in recent years.

[0003] The control strategies for tilt-rotor unmanned aerial vehicles in helicopter and fixed-wing modes are already mature, but due to their special aerodynamic layout during the transition and switching, the control problems are still challenging. Since its transition process mainly occurs in the longitudinal plane, the flight stability of tilt-rotor unmanned aerial vehicles depends to a large extent on the longitudinal flight control law. During the tilt process of the rotor, there is an area called the "tilt corridor", which imposes strict restrictions on the state of the unmanned aerial vehicle system. Once the system state deviates from this area, it will be difficult to ensure the safety of the transition process of the tilt-rotor unmanned aerial vehicle. In addition, the aerodynamic coupling effect between the rotor and the wing not only increases the difficulty of system modeling, but also poses higher requirements for the design of the control system. The complexity of aerodynamic coupling leads to modeling uncertainties, thus affecting the design of control strategies. The control of the tilt process poses extremely high requirements for the convergence of the system. If the convergence of the control system is insufficient, it may cause the unmanned aerial vehicle to swing violently during the transition process, and may even lead to serious consequences such as out-of-control and crashing. Therefore, the existing control methods still have certain limitations in ensuring the smoothness and safety of the transition process. Summary of the Invention

[0004] The technical problem solved by the present invention is: to provide a fixed-time control method for the longitudinal motion transition section of a tilt-rotor unmanned aerial vehicle under strong uncertainties and state constraints. It not only effectively solves the control problem under the tilt corridor restriction, but also relaxes the accuracy requirements for the aerodynamic coupling modeling between the rotor and the wing, and significantly improves the convergence performance of the system through fixed-time control technology. This method solves the problems of strong modeling uncertainties and state constraints by introducing a neural network system and an asymmetric barrier Lyapunov function, and improves the convergence of the system by designing a fixed-time controller.

[0005] The present invention adopts the following technical solutions to solve the above technical problems: A fixed-time control method for the transition section of a tilt-rotor unmanned aerial vehicle considering state constraints, including the following steps:

[0006] Step 1: Establish a switched nonlinear system model according to the longitudinal motion characteristics of the tilt-rotor UAV;

[0007] Step 2: Establish a tilt corridor for the multi-constraint conditions in the transition section of the tilt-rotor UAV. On this basis, construct an asymmetric barrier Lyapunov function to confine the system state within the range that satisfies the tilt corridor constraints;

[0008] Step 3: Design a neural network system to estimate the unknown modeling uncertainties caused by the aerodynamic coupling between the rotors and the wings, and obtain an approximation of the unknown modeling uncertainties;

[0009] Step 4: Design a flight control law and an adaptation law, and use the output signal of the neural network system obtained in Step 3, that is, the approximation of the unknown modeling uncertainties, to compensate for the unknown modeling uncertainties;

[0010] Step 5: Construct a closed-loop multi-Lyapunov function, and design and analyze the parameters of the flight control law to keep the state of the tilt-rotor UAV transition section system within the constraint range at all times, and achieve stability within a fixed time.

[0011] As a preferred solution of the present invention, Step 1 establishes a longitudinal switched nonlinear system dynamics model of the tilt-rotor UAV, including the velocity in the axis direction of the body coordinate system and the pitch angle pitch angular velocity to form a loop;

[0012] The longitudinal motion dynamics model of the tilt-rotor UAV is as follows:

[0013] ,

[0014] where is the pitch angle, is the velocity vector, is the velocity in the axis direction of the body coordinate system, is the velocity in the axis direction of the body coordinate system, is the pitch angular velocity, , are respectively and the first derivatives, , is the nacelle tilt angle; is the control input, is the thrust generated by the rotor, is the longitudinal cyclic margin angle, is the elevator deflection angle; is the modeling uncertainty function of the aerodynamic coupling between the rotor and the wing, both are unknown nonlinear functions; and are the modeling known nonlinear function and the control gain respectively, expressed as follows:

[0015] , ,

[0016] where is the gravitational acceleration, is the weight of the tilt-rotor UAV, is the moment of inertia, is the mast height, is a constant coefficient, , are the component force coefficients of the aerodynamic force coefficient decomposed in the body coordinate system axis direction and axis direction respectively, is the pitching moment coefficient generated by the aerodynamic force; is the pitching moment coefficient generated by the elevator deflection angle , , are the component force coefficients generated by the elevator deflection angle in the body coordinate system axis direction and axis direction respectively;

[0017] According to the change of the nacelle inclination angle of the tilt-rotor UAV in the transition section, the longitudinal motion model of the transition section is divided into several nonlinear subsystems, and the switched nonlinear system model of the tilt-rotor UAV in the transition section is obtained, expressed as follows:

[0018] ,

[0019] where represents is a right-continuous switching signal, is the total number of subsystems. When , it means that the th subsystem is being activated; , , represent the modeling known nonlinear function, the control gain and the modeling unknown uncertainty function corresponding to the system at the time of the switching signal respectively.

[0020] As a preferred embodiment of the present invention, the calculation method of the tilt corridor in the transition section of the tilt-rotor UAV in step 2 is as follows:

[0021] Determine the constraint conditions in the transition section of the tilt-rotor UAV, including:

[0022] ,

[0023] where and are the resultant external forces in the x-axis direction and the y-axis direction in the body coordinate system respectively, the axis direction, is the thrust generated by the rotor, is the nacelle tilt angle, is the weight of the tilt-rotor UAV, is the acceleration due to gravity, is the pitch angle, and are the aerodynamic lift and drag respectively, is the angle of attack of the tilt-rotor UAV, and are the zero-lift angle of attack and the critical stall angle of attack respectively, is the maximum thrust provided by the rotor, is the nacelle tilt angle at time t;

[0024] By solving the multiple constraint conditions in the above formula, the high-speed section speed and the low-speed section speed of the tilt corridor in the transition section are obtained; define the system state constraints as follows:

[0025] ,

[0026] where and are the upper and lower bounds of the system state constraints respectively, is the state of the system;

[0027] As a preferred embodiment of the present invention, construct an asymmetric logarithmic barrier Lyapunov function as follows:

[0028] ,

[0029] where is the tracking error of the system state, is a piecewise function, and are briefly denoted as and respectively, and the expressions are as follows:

[0030] ,

[0031] wherein is the desired tracking instruction.

[0032] As a preferred embodiment of the present invention, the neural network system described in step 3 is designed as:

[0033] ,

[0034] wherein is the modeling unknown uncertainty function corresponding to the th subsystem, is a block diagonal matrix, is the ideal weight matrix, is the component of the ideal weight matrix, , is the number of nodes of the neural network, is a vector composed of basis functions, is the vector component, is a Gaussian basis function, is the neural network approximation error.

[0035] As a preferred embodiment of the present invention, step 4 designs a fixed-time adaptive flight control law and an adaptation law, and uses the output signal of the neural network system obtained in step 3, that is, the approximation value of the unknown modeling uncertainty, to compensate for the unknown modeling uncertainty. The following error variables are defined:

[0036] ,

[0037] wherein is the pitch angle tracking error, is the tracking error of the velocity in the axis direction in the body coordinate system, is the tracking error of the velocity in the axis direction in the body coordinate system, is the tracking error of the pitch angular velocity, is the filtering error, is the adaptive parameter tracking error, , , are respectively , , desired tracking instructions, is the virtual control law, is the adaptive parameter, , is The estimated value, is the output of the following first-order non-linear switching filter:

[0038]

[0039] where is the switching filter time constant, and are the parameters to be designed respectively and satisfy , is the virtual control law to be designed, is the first derivative of

[0040] The fixed-time adaptive flight control law and the adaptive law are designed as:

[0041] ,

[0042] ,

[0043] ,

[0044] where and and and and and are positive constants to be designed respectively, , is the state constraint gain, is the pitch angle desired command the first derivative of is the control input, is the inverse matrix of the control gain matrix, is the velocity desired command the first derivative of is the diagonal matrix composed of adaptive parameters, is the adaptive parameter, is the first derivative of denotes the known non-linear function of the subsystem corresponding to the and and are positive definite diagonal matrices to be designed respectively, is the state constraint gain diagonal matrix, is the error variable of the velocity, , , and and and The expression is as follows:

[0045] ,

[0046] where 、 、 are defined as follows: , , 、 are the upper and lower bounds of the state constraint respectively.

[0047] As a preferred embodiment of the present invention, a closed-loop multi-Lyapunov function is constructed as follows:

[0048] When the -th subsystem is activated, the closed-loop multi-Lyapunov function is selected as follows:

[0049] ,

[0050] where 、 、 are the Lyapunov functions constructed considering the error variable pitch angle tracking error , speed tracking error and filtering error respectively, and the expressions are as follows:

[0051] ,

[0052] where 、 are respectively abbreviated as 、 , and are defined as follows: , 、 are the upper and lower bounds of the state constraint respectively, is defined as follows: ;

[0053] The derivative of the multi-Lyapunov function with respect to time is as follows:

[0054] ,

[0055] where , , is the upper bound of the derivative of the desired command, is a positive constant, is the filtering time constant, is the upper bound of the approximation error of the neural network;

[0056] Design the average dwell time , where is a normal value, is any value in the interval, and we obtain:

[0057] ,

[0058] where is the multiple Lyapunov function of the system, is the initial value of the multiple Lyapunov function of the system at time , is the chattering boundary, is a bounded value, that is, the closed-loop switching system is bounded, and at the same time, the system states are all within the safe range of the state constraints.

[0059] As a preferred solution of the present invention, considering the system state error variable, design the common Lyapunov function as follows:

[0060] ,

[0061] The derivative of the common Lyapunov function with respect to time satisfies the following conditions:

[0062] ,

[0063] where , , , , , that is, the system state error converges to the set at time , and the expression is as follows:

[0064] ,

[0065] where is a positive constant, is the tracking error of the system state;

[0066] By adjusting the control parameters , , , , , , , , control the tracking errors of the attitude angle and speed of the tilt-rotor UAV.

[0067] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

[0068] (1) The present invention uses a switched nonlinear system to describe the longitudinal motion dynamics model of a tilt-rotor unmanned aerial vehicle. Compared with the traditional single-system model and switched linear system model, the dynamics model proposed by the present invention is more general and has practical application value.

[0069] (2) The present invention uses a neural network system to approximate the complex aerodynamic coupling between the rotors and the wings, reduces the constraints on modeling uncertainties, and improves the robustness of the control system.

[0070] (3) The present invention uses an asymmetric time-varying barrier Lyapunov function to solve the state constraint problem caused by the tilt corridor, ensuring a smooth transition in the transition section of the tilt-rotor unmanned aerial vehicle. Description of the drawings

[0071] Figure 1 is the overall flowchart of the method of the present invention.

[0072] Figure 2 is the speed of the tilt-rotor unmanned aerial vehicle in the embodiment of the present invention simulation curve.

[0073] Figure 3 is the speed of the tilt-rotor unmanned aerial vehicle in the embodiment of the present invention simulation curve.

[0074] Figure 4 is the pitch angle of the tilt-rotor unmanned aerial vehicle in the embodiment of the present invention simulation curve.

[0075] Figure 5 is the pitch angular velocity of the tilt-rotor unmanned aerial vehicle in the embodiment of the present invention simulation curve.

[0076] Figure 6 is the adaptive parameter in the embodiment of the present invention simulation curve.

[0077] Figure 7 is the adaptive parameter in the embodiment of the present invention simulation curve.

[0078] Figure 8 is the adaptive parameter in the embodiment of the present invention simulation curve.

[0079] Figure 9 is the control switching signal diagram. Detailed implementation manners

[0080] The present invention will be further illustrated below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. After reading the present invention, various equivalent modifications made by those skilled in the art fall within the scope defined by the appended claims of this application.

[0081] A fixed-time control method for the transition section of a tilt-rotor UAV considering state constraints according to the present invention has a process as Figure 1 , and includes the following steps:

[0082] (1) First, according to the longitudinal motion characteristics of the tilt-rotor UAV, a switched nonlinear system model is established.

[0083] In this embodiment, taking the longitudinal control of the tilt-rotor UAV as an example, a longitudinal switched nonlinear system dynamics model of the tilt-rotor UAV is established, including the velocity in the axis direction of the body coordinate system and the velocity in the axis direction, pitch angle , pitch angular velocity to form a loop; the longitudinal motion dynamics model of the tilt-rotor UAV is as follows:

[0084] (1),

[0085] where is the pitch angle, is the velocity vector, is the velocity in the axis direction in the body coordinate system, is the velocity in the axis direction in the body coordinate system, is the pitch angular velocity, , are respectively and the first derivatives of, , is the nacelle inclination angle; is the control input (controller), is the thrust generated by the rotor, is the longitudinal cyclic pitch angle, is the elevator deflection angle; is the modeling uncertainty function of the aerodynamic coupling between the rotor and the wing, are all unknown nonlinear functions. and are respectively the modeling known nonlinear function and the control gain, expressed as follows:

[0086] , ,

[0087] wherein is the acceleration due to gravity, is the weight of the tilt-rotor UAV, is the moment of inertia, is the mast height, is a constant coefficient, and are the component force coefficients of the aerodynamic force coefficients decomposed in the body coordinate system axis direction and axis direction respectively, is the pitching moment coefficient generated by the aerodynamic force; is the pitching moment coefficient generated by the elevator deflection angle , and are the component force coefficients generated by the elevator deflection angle in the body coordinate system axis direction and axis direction respectively.

[0088] According to the change of the nacelle inclination angle of the tilt-rotor UAV in the transition section, the longitudinal motion model of the transition section is divided into several nonlinear subsystems, and the switched nonlinear system model of the tilt-rotor UAV in the transition section is obtained, which is expressed as follows:

[0089] (2),

[0090] wherein represents is a right-continuous switching signal, is the total number of subsystems. When , it means that the th subsystem is being activated. and and represent the known nonlinear function, control gain and unknown uncertain function of the system corresponding to the switching signal at the moment respectively.

[0091] (2) Establish a tilt corridor for the multi-constraint conditions of the tilt-rotor UAV in the transition section. On this basis, construct an asymmetric barrier Lyapunov function to confine the system state within the range that satisfies the tilt corridor constraints.

[0092] During the rotor tilting process, the gravity of the airframe is jointly balanced by the aerodynamic lift and the rotor thrust. The magnitude of the aerodynamic lift is jointly affected by the speed and angle of attack of the UAV, while the magnitude of the rotor thrust is limited by the nacelle inclination angle and the maximum thrust that the rotor can provide. The constraint conditions of the tilt-rotor UAV in the transition section are designed as follows in this embodiment:

[0093] (3),

[0094] where and are the resultant external forces in the -axis direction and -axis direction in the body coordinate system respectively, is the lift generated by the rotor, is the nacelle inclination angle, is the weight of the tilt-rotor UAV, is the gravitational acceleration, is the pitch angle, and are the aerodynamic lift and drag respectively, is the angle of attack of the tilt-rotor UAV, and are the angle of attack for zero lift and the critical stall angle of attack respectively, is the maximum lift provided by the rotor, is the nacelle inclination angle at time and the low-speed segment speed .

[0095] According to the limitations of the high-speed and low-speed segments brought by the tilt corridor, and to ensure that the attitude angle remains within a reasonable range during the tilting process, thus achieving safe flight in the transition segment, the system state needs to be constrained within a reasonable range. Therefore, the system state constraints are defined as follows:

[0096] (4),

[0097] where and are the upper and lower bounds of the system state constraints respectively, is the state of the system.

[0098] Construct an asymmetric logarithmic barrier Lyapunov function as follows:

[0099] (5),

[0100] where is the tracking error of the system state, is a piecewise function, and are respectively abbreviated as and , and the expressions are as follows:

[0101] (6),

[0102] where is the desired tracking instruction.

[0103] (3) Design a neural network system to estimate the unknown modeling uncertainty caused by the aerodynamic coupling between the rotor and the wing, and obtain an approximation value of the unknown modeling uncertainty.

[0104] Lemma 1: For any continuous function defined on the compact set there exists a neural network such that

[0105] (7),

[0106] where is the ideal weight vector of the neural network, is the number of nodes of the neural network, is the basis vector of the neural network, is the approximation error of the neural network satisfying and is an unknown positive constant, usually a Gaussian function is chosen.

[0107] (8),

[0108] where is the center vector, is the width of the Gaussian function.

[0109] According to Lemma 1, the neural network system is designed as:

[0110] (9),

[0111] where is the modeling unknown uncertainty function corresponding to the th subsystem, is a block diagonal matrix, is the ideal weight matrix, is the component of the ideal weight matrix, , is the number of nodes of the neural network, is the vector composed of basis functions, is the th component of the vector is the Gaussian basis function, is the approximation error of the neural network.

[0112] (4) Design the flight control law and adaptive law, and use the output signal of the neural network system obtained in step (3), i.e., the approximation value of the unknown modeling uncertainty, to compensate for the unknown modeling uncertainty.

[0113] First, define the following error variables:

[0114] (10),

[0115] where is the pitch angle tracking error, is the tracking error of the velocity in the axis direction of the body coordinate system, is the tracking error of the velocity in the axis direction of the body coordinate system, is the tracking error of the pitch angular velocity, is the filtering error, is the adaptive parameter tracking error, , , are respectively , , of the desired tracking commands, is the virtual control law, is the adaptive parameter, , is 's estimated value, is the output of the following first-order nonlinear switching filter:

[0116] (11),

[0117] where is the switching filtering time constant, , are respectively the parameters to be designed and satisfy , is the virtual control law to be designed, is 's first derivative.

[0118] Considering the error variable , design the following Lyapunov function:

[0119] (12),

[0120] where , are respectively abbreviated as , , and are defined as follows: , 、 are respectively the upper and lower bounds of the state constraint, and are defined as follows: . .

[0121] According to Eqs. (1) and (10), the derivative with respect to time is as follows:

[0122] (13),

[0123] where .

[0124] Based on Eq. (13), an adaptive flight control law, i.e., the virtual control law is designed as follows:

[0125] (14),

[0126] where 、 、 are positive constants to be designed, 、 are parameters to be designed and satisfy , is the pitch angle desired command 's first derivative, is the state constraint gain, and its expression is as follows:

[0127] (15),

[0128] where is a parameter to be designed such that ; 、 are respectively 、 's first derivatives.

[0129] Substituting Eq. (14) into Eq. (13) gives:

[0130] (16),

[0131] Secondly, when the th subsystem is activated, considering the error variables 、 , the following Lyapunov function is designed:

[0132] (17),

[0133] According to Equations (1) and (10), the derivative with respect to time is as follows:

[0134] (18),

[0135] Based on Equation (18), the controller and the adaptation law are designed as follows:

[0136] (19),

[0137] (20),

[0138] where is the control input, is the inverse matrix of the control gain matrix, is the desired velocity command 's first derivative, is a diagonal matrix composed of adaptation parameters, represents the known nonlinear function of the th subsystem for modeling, , , are respectively positive definite diagonal matrices to be designed, is the state constraint gain diagonal matrix, is the velocity error variable, , , are respectively positive constants to be designed, , ; is the adaptation parameter, is 's first derivative, , and are respectively positive constants to be designed, , , 's expressions are as follows:

[0139] (21),

[0140] where is a parameter to be designed such that ; , are respectively , 's first derivatives.

[0141] (5) Construct a closed-loop multi-Lyapunov function, design and analyze the parameters of the flight control law, and keep the states of the transition section system of the tilt-rotor UAV within the constraints at all times to ensure the stability of the transition section of the tilt-rotor UAV.

[0142] According to Eqs. (17), (18), (19), and (20), The derivative with respect to time is as follows:

[0143] (22),

[0144] Consider the filter error variable , and design the following Lyapunov function:

[0145] (23),

[0146] According to Eq. (11), and denote the upper bound of as , The derivative with respect to time is as follows:

[0147] (24),

[0148] where is the filter time constant.

[0149] When the -th subsystem is activated, design the multi-Lyapunov function of the entire closed-loop system as follows:

[0150] (25),

[0151] where , , are the Lyapunov functions constructed considering the error variables pitch angle tracking error , speed tracking error and filter error , respectively.

[0152] Lemma 2: For any positive constant , the following inequality holds:

[0153] ,

[0154] According to Lemma 2, it can be obtained that:

[0155] (26),

[0156] According to equations (16), (22), (24), and (26), the derivatives with respect to time are as follows:

[0157] (27),

[0158] where , . is the upper bound of the desired command derivative, is a positive constant, is the filtering time constant, is the upper bound of the neural network approximation error.

[0159] According to equation (10), we have , which holds. When the following inequality can be obtained:

[0160] (28),

[0161] where .

[0162] According to equations (25) and (28), the relationship between multiple Lyapunov functions can be obtained as follows:

[0163] (29),

[0164] where .

[0165] Next, it is proved that the switched system is stable. When , and represent the switching times of the -th subsystem and the -th subsystem respectively. Integrating equation (27) gives the following inequality:

[0166] (30),

[0167] Substituting equation (29) into equation (30) gives the following inequality:

[0168] (31),

[0169] where represents the left limit value at , represents the function value of at time .

[0170] When ​, for formula (31) from to , denotes the total number of switches that occur in the system within time. Through iteration, the following inequality can be obtained:

[0171] (32),

[0172] where is abbreviation.

[0173] From , denotes the total number of switches that occur in the system within time, it can be obtained that:

[0174] (33),

[0175] Design the average dwell time , as the normal value, is any value in the interval, it can be obtained that:

[0176] (34),

[0177] where is the chatter boundary, denotes the total number of switches that occur in the system within time, denotes any moment less than the time .

[0178] Substitute formula (34) into formula (33) to obtain:

[0179] (35),

[0180] Similarly, according to the above analysis, it can be obtained that:

[0181] (36),

[0182] where .

[0183] Substitute formulas (35) and (36) into formula (33) to obtain the following inequality relationship:

[0184] (37),

[0185] where is the multi-Lyapunov function of the system, is the multi-Lyapunov function of the system at time The initial value of is the vibration boundary, and is the bounded value. Therefore, when the average dwell time

[0186] When , the following equation can be obtained:

[0187] (38),

[0188] When , similarly, we can get: , .

[0189] In summary, we can get:

[0190] (39),

[0191] Substituting Equation (6) into Equation (39), we can get:

[0192] (40),

[0193] That is, the state variables of the system are all within the constraint range, and the proof is completed.

[0194] Next, it is proved that the system state tracking error can achieve fixed-time convergence. Considering the error variable , the Lyapunov function is designed as follows:

[0195] (41),

[0196] Substituting Equations (14) and (26) into Equation (41), the following inequality can be obtained:

[0197] (42),

[0198] Considering the error variable , the Lyapunov function is designed as follows:

[0199] (43),

[0200] Substituting Equations (19) and (20) into Equation (43), the following inequality can be obtained:

[0201] (44),

[0202] According to Equation (26), let and , , and , the following inequality can be obtained:

[0203] (45),

[0204] In summary, the system state error will be at time converge to the set inside.

[0205] (46),

[0206] where is a positive constant, is the tracking error of the system state.

[0207] Adjust the control parameters , , , , , , , to reduce the tracking errors of the attitude angle and speed of the tilt-rotor UAV.

[0208] To verify the fixed-time control method for the transition section of the tilt-rotor UAV considering state constraints in the present invention, numerical simulation is carried out based on the controlled object (Equation (2)) and the simulation results are given.

[0209] According to Equation (4), select the system state constraints as follows:

[0210] ,

[0211] where , are respectively the upper and lower bounds of the pitch angle constraint, , are respectively the upper and lower bounds of the pitch angular velocity constraint, , are respectively the upper and lower bounds of the velocity in the axis direction in the body coordinate system, , are respectively the upper and lower bounds of the velocity in the axis direction in the body coordinate system.

[0212] According to the virtual control law (Equation (14)), select the control parameters as: , , , , .

[0213] According to the controller (Equation (19)), the control parameters are selected as follows: , , .

[0214] According to the adaptation law (Equation (20)), the control parameters are selected as follows: , , .

[0215] Regarding the neural network system, design

[0216] , , ;

[0218] The simulation results are as shown in Figures 2 to 9 . As shown in Figure 2 and Figure 3 , during the entire tilt transition section of the UAV, the flight speed of the UAV and can well track the desired command and always remain within the boundaries of the state constraints, meeting the flight speed under the tilt corridor constraints. Figure 4 and Figure 5 show that the pitch angle and the pitch angular velocity of the UAV can well track the desired command and always remain within the boundaries of the state constraints, ensuring that the attitude of the UAV can be well maintained stable during the entire tilt transition section. Figures 6 - 8 The response curve of the adaptation parameter is given. Figure 9 is the curve of the switching signal .

[0219] It can be seen from the simulation results that in the presence of state constraints and strong modeling uncertainties, a fixed-time control method for the transition section of a tilt-rotor UAV considering state constraints proposed by the present invention can still effectively guarantee the speed and attitude tracking performance of the UAV, so that the tilt-rotor UAV can smoothly complete the transition from the helicopter mode to the fixed-wing mode.

[0220] The above embodiments are only used to illustrate the technical idea of the present invention, and the protection scope of the present invention cannot be limited thereby. Any changes made on the basis of the technical solution according to the technical idea proposed by the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for controlling the fixed time of transition section of a tilt-rotor UAV considering state constraints, characterized in that: The steps include: Step 1: Establish a switching nonlinear system model based on the longitudinal motion characteristics of the tilt-rotor UAV; Step 2: Establish a tilt-rotor corridor based on the multi-constraint conditions of the transition section of the tilt-rotor UAV, and construct an asymmetric obstacle Lyapunov function on this basis to constrain the system state within the range that satisfies the tilt-rotor corridor constraints; Step 3: Design a neural network system to estimate the unknown modeling uncertainty caused by the aerodynamic coupling between the rotor and the wing, and obtain an approximate value of the unknown modeling uncertainty; Step 4: Design the flight control law and adaptive law, and use the output signal of the neural network system obtained in step 3, that is, the approximate value of the unknown modeling uncertainty, to compensate for the unknown modeling uncertainty; Step 5: Construct a closed-loop multi-Lyapunov function, and design and analyze the parameters of the flight control law so that the state of the tilt-rotor UAV transition phase system always remains within the constraints and achieves stability within a fixed time.

2. A method for controlling the fixed time of a transition section of a tilt-rotor UAV considering state constraints according to claim 1, characterized in that: Step 1 Establish the nonlinear system dynamics model of the longitudinal switching of the tilt-rotor UAV, including the Axis speed and Axis speed , Pitch angle , pitch angular velocity The circuit composed of The longitudinal motion dynamics model of the tilt-rotor UAV is as follows: ， in is the pitch angle, is the velocity vector, For machine system The speed in the axis direction, For machine system The speed in the axis direction, is the pitch angular velocity, , They are and The first derivative of , is the nacelle inclination angle; is the control input, is the thrust generated by the rotor, is the longitudinal periodic margin angle, is the elevator deflection angle; is the uncertainty function for modeling the aerodynamic coupling between the rotor and the wing, They are all unknown nonlinear functions; and To model known nonlinear functions and control gains respectively, they are expressed as follows: ， ， in is the acceleration due to gravity, is the weight of the tilt-rotor UAV, is the moment of inertia, is the mast height, is a constant coefficient, , The aerodynamic coefficients are decomposed into the body coordinate system Axis direction and The force coefficient in the axial direction, is the pitching moment coefficient produced by aerodynamic forces; Generates deflection angle for elevator The pitching moment coefficient, , Elevator deflection angle In the body coordinate system Axis direction and The force coefficient generated in the axial direction; According to the inclination angle of the nacelle in the transition section of the tilt-rotor UAV The longitudinal motion model of the transition section is divided into several nonlinear subsystems according to the change of , and the switching nonlinear system model of the transition section of the tilt-rotor UAV is obtained, which is expressed as follows: ， in express is the right continuous switching signal, is the total number of subsystems, when When subsystems are being activated; , , Respectively represent switching signals exist The modeling of known nonlinear functions, control gains and modeling of unknown uncertain functions corresponding to the system at each moment.

3. The method for controlling the fixed time of the transition section of a tilt-rotor UAV considering state constraints according to claim 2 is characterized in that: Step 2 Design the tilt-rotor UAV transition section tilt corridor calculation method as follows: Determine the transition constraints of the tilt-rotor UAV including: ， in , In the body coordinate system Axis direction and The resultant external force in the axial direction is is the thrust generated by the rotor, is the nacelle inclination angle, is the weight of the tilt-rotor UAV, is the acceleration due to gravity, is the pitch angle, , are the aerodynamic lift and drag, is the angle of attack of the tilt-rotor UAV, , are the zero lift angle of attack and the critical stall angle of attack, respectively. The maximum thrust provided to the rotor, for The nacelle inclination angle at the moment; By solving the multiple constraints in the above formula, the high-speed section speed of the transition section tilt corridor is obtained. and low speed ; Define the system state restrictions as follows: ， in , are the upper and lower bounds of the system state constraints, respectively. The status of the system.

4. The method for controlling the fixed time of the transition section of a tilt-rotor UAV considering state constraints according to claim 3 is characterized in that: Constructing an asymmetric logarithmic barrier Lyapunov function as follows: ， in is the tracking error of the system state, is a piecewise function, , They are respectively , , the expression is as follows: ， in is the desired tracking instruction.

5. A method for controlling the fixed time of transition section of a tilt-rotor UAV considering state constraints according to claim 4, characterized in that: The neural network system described in step 3 is designed as follows: ， in For the The modeling unknown uncertain function corresponding to each subsystem, is a block diagonal matrix, is the ideal weight matrix, are the components of the ideal weight matrix, , is the number of nodes in the neural network, is a vector composed of basis functions, For vector The weight, is the Gaussian basis function, is the neural network approximation error.

6. A method for controlling the fixed time of transition section of a tilt-rotor UAV considering state constraints according to claim 5, characterized in that: Step 4 designs the fixed-time adaptive flight control law and the adaptive law. The output signal of the neural network system obtained in step 3, i.e., the approximate value of the unknown modeling uncertainty, is used to compensate for the unknown modeling uncertainty. The following error variables are defined: ， in is the pitch angle tracking error, In the body coordinate system Axis speed The tracking error, In the body coordinate system Axis speed The tracking error, is the tracking error of the pitch angular velocity, is the filtering error, is the adaptive parameter tracking error, , , They are , , The expected tracking instructions, is the virtual control law, is the adaptive parameter, , for The estimated value of is the output of the following first-order nonlinear switching filter: ， in To switch the filter time constant, , are the parameters to be designed and satisfy , is the virtual control law to be designed, for The first derivative of ; The fixed-time adaptive flight control law and the adaptive law are designed as follows: ， ， ， in , , , , and are the normal numbers to be designed, , is the state constraint gain, is the desired pitch angle command The first derivative of ; is the control input, is the inverse matrix of the control gain matrix, Speed ​​​​desired command The first derivative of is the diagonal matrix of adaptive parameters, is the adaptive parameter, for The first derivative of Indicates The modeling of the subsystems corresponds to known nonlinear functions, , , are the positive definite diagonal matrices to be designed, is the state constraint gain diagonal matrix, is the error variable of speed, , , , , , The expression is as follows: ， in , , The definition is as follows: , , , Status The upper and lower bounds of the constraint.

7. A method for controlling the fixed time of transition section of a tilt-rotor UAV considering state constraints according to claim 6, characterized in that: Construct a closed-loop multi-Lyapunov function as follows: When When a subsystem is activated, a closed-loop multi-Lyapunov function is selected as follows: ， in , , The error variables are respectively considered as the pitch angle tracking error , speed tracking error and filtering error The constructed Lyapunov function is expressed as follows: ， in , They are respectively , , defined as follows: , , Status The upper and lower bounds of the constraints, The definition is as follows: ; Multiple Lyapunov functions Derivatives with respect to time as follows: ， in , , is the upper bound of the expected instruction derivative, is a normal number, is the filtering time constant, It is the upper bound of the neural network approximation error; Design average dwell time ,in is the normal value, for For any value in the interval, we get: ， in is the multi-Lyapunov function of the system, is the system of multiple Lyapunov functions in time The initial value of is the vibration boundary, is a bounded value, that is, the closed-loop switching system is bounded, and the system states are all within the safe range of the state constraints.

8. The method for controlling the fixed time of the transition section of a tilt-rotor UAV considering state constraints according to claim 7 is characterized in that: Considering the system state error variable, the public Lyapunov function is designed as follows: ， Public Lyapunov function Derivatives with respect to time The following conditions must be met: ， in , , , , , that is, the system state error at time Converge to the set Inside, the expression is as follows: ， in is a normal number, is the tracking error of the system state; By adjusting the control parameters , , , , , , , , control the tracking error of the attitude angle and velocity of the tilt-rotor UAV.

Images