Four-rotor unmanned aerial vehicle obstacle avoidance tracking control method and system under limited environment

Abstract

The invention discloses a four-rotor unmanned aerial vehicle obstacle avoidance tracking control method and system under a limited environment, and belongs to the technical field of industrial process control. The method comprises the following steps: constructing a four-rotor unmanned aerial vehicle dynamic system model; establishing a state environment limited model according to the state error; estimating the nonlinear state of the quad-rotor unmanned aerial vehicle; a command filter and a virtual control rate model are constructed, and the problem of derivation complexity explosion is avoided; and constructing a four-rotor unmanned aerial vehicle state controller model, and performing real-time control on the state of the four-rotor unmanned aerial vehicle. According to the invention, environment limited control is introduced, so that unmanned aerial vehicle control is realized efficiently and smoothly under the condition that constraints are applied to different external conditions or hardware physical limitation changes; meanwhile, a potential energy function is introduced under the condition that the state is limited, and collision and obstacle avoidance under the condition that the environment is limited is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of industrial process control, and particularly relates to an obstacle avoidance and tracking control method and system for a quadrotor UAV under limited environment. Background Technique

[0002] Quadrotor aircraft belong to a type of rotor UAV, which has the characteristics of vertical takeoff and landing, low requirements for the external environment, strong mobility, high stability, and convenient operation. Currently, it is highly favored in both military and civilian fields and has high application value. At present, quadrotor aircraft have characteristics such as coupling, underactuation, and nonlinearity. In order to achieve stable flight, high requirements are imposed on technologies in various aspects such as microprocessors, sensors, mechanical design, navigation algorithms, and control methods. Therefore, the research on quadrotor aircraft provides a convenient and effective platform for the development of the above technical fields, and is of great significance.

[0003] Since the current mission scenarios of quadrotor UAVs are constantly expanding and the requirements for the adaptability and sensitivity of quadrotors are continuously increasing, the requirements for UAV control systems are also continuously increasing. Currently, more and more mission scenarios require UAVs to work under very different environmental constraints. Traditional control designs are often based on fixed environmental conditions for setting, and by introducing time-varying conditional functions for variable environmental constraint control, it can not only make the operation of UAVs more efficient and flexible, but also reduce potential safety hazards.

[0004] When a UAV operates according to time during a mission, it will encounter different obstacles and complex environments. How to efficiently identify obstacles and avoid them, and safely complete the mission and reach the destination in a complex environment has become the key to its mission. Applying the artificial potential field algorithm for collision avoidance and obstacle avoidance operations has important practical significance, which can enable the UAV to effectively and safely plan a path and reach the destination.

[0005] Therefore, how to solve the problem of planning an obstacle avoidance path under limited environmental conditions and complete the flight mission of the expected path without violating the condition constraints is the technical problem that the present invention wants to solve. Summary of the Invention

[0006] The purpose of the present invention is to provide an obstacle avoidance and tracking control method and system for a quadrotor UAV under limited environment to solve the problems raised in the above background technique.

[0007] The purpose of the present invention is achieved as follows: An obstacle avoidance and tracking control method for a quadrotor UAV under limited environment, characterized in that: the method includes the following steps:

[0008] Step S1: Construct a dynamic system model of a quadrotor UAV, define the expected values of the quadrotor UAV states, and obtain the actual values of the quadrotor UAV states;

[0009] Step S2: Construct the state error of the quadrotor UAV and establish a state environment constraint model based on the state error;

[0010] Step S3: Construct a nonlinear state estimation model based on a radial basis function neural network and estimate the nonlinear states of the quadrotor UAV;

[0011] Step S4: Define the obstacle positions and construct a potential energy function to generate a repulsive potential field;

[0012] Step S5: Construct a command filter and a virtual control rate model to avoid the problem of derivative complexity explosion;

[0013] Step S6: Construct a state controller model of the quadrotor UAV and perform real-time control on the states of the quadrotor UAV.

[0014] Preferably, in step S1, constructing the dynamic system model of the quadrotor UAV specifically includes:

[0015] The position state space equation of the quadrotor UAV is:

[0016] where, u χ is the position state controller, is the first-order nonlinear position state, F χ is the position system dynamics, satisfying is the second-order nonlinear position state;

[0017] The attitude state space equation of the quadrotor UAV is:

[0018] where, u P is the attitude state controller, is the first-order nonlinear attitude state, F P is the attitude system dynamics, satisfying is the second-order nonlinear attitude state.

[0019] Preferably, in step S1, defining the expected values of the quadrotor UAV states and obtaining the actual values of the quadrotor UAV states specifically includes:

[0020] Define the expected position χ 1d =[x d y d z d T and the expected attitude function: ​

[0021] Among them, x d is the desired trajectory in the x direction, y d is the desired trajectory in the y direction, z d is the desired trajectory in the z direction; is the desired pitch attitude angle, θ d is the desired roll attitude angle, Ψ d is the desired yaw attitude angle, which is determined by the following formula:

[0022] Q x =(cosφsinθcosψ + sinφsinψ)u x

[0023] Q y =(cosφsinθsinψ - sinφsinψ)u y ;

[0024] Q z =(cosφcosθ)u z

[0025]

[0026] Among them, Q = [Q x Q y Q z T is the position and attitude conversion variable, P1 = [Φ θ ψ] T is the real-time attitude state of the quadrotor UAV, u x is the position controller in the x direction, u y is the position controller in the y direction, u z is the position controller in the z direction.

[0027] Preferably, in step S2, the state error of the quadrotor UAV is constructed, and a state environment constraint model is established according to the state error, specifically:

[0028] Step S2-1: Define the state error S = x s1 - x sd , where x s1 is the real-time state, and x sd is the desired state;

[0029] Step S2-2: Establish a state constraint model according to the obstacle Lyapunov function The state constraint model is:

[0030]

[0031] Step S2-3: Construct a state constraint operation unit related to the controller expression​

[0032] Among them, d is a time-varying environment constraint parameter, satisfying d > 0; ξ is a constraint condition gain, satisfying ξ > 0; c is a constraint control gain, satisfying c > 0; k c is a time-varying constraint condition parameter, k d is a time-varying constraint control parameter, and their relationship satisfies:

[0033]

[0034] Preferably, in the step S3, constructing a non-linear state estimation model based on a radial basis function neural network is specifically as follows:

[0035]

[0036] Among them, Φ i is the activation function value of the radial basis function in the i-th training, κ is the width parameter of the Gaussian function, l is the center parameter of the Gaussian function, and m is the center point step size of the Gaussian function; W i is the updated weight in the i-th training, η is the updated weight decay coefficient, γ is the weight update learning rate, and Δt is the training step size, is the non-linear state estimation value of the quadrotor UAV.

[0037] Preferably, in the step S4, defining the obstacle position and constructing a potential energy function to generate a repulsive potential field is specifically as follows:

[0038] Preset the three-dimensional space coordinates X of the obstacle on the i-th expected path b,i =[x b,i y b,i z b,i T , calculate the Euclidean distance z between the UAV and the i-th obstacle i =X1 - X b,i , and construct the repulsive potential field of the i-th obstacle

[0039] Further calculate the potential field repulsive force of the i-th obstacle

[0040] Among them, X1 is the real-time position state of the quadrotor UAV; is the influence radius of the obstacle repulsive potential field to be set, r is the minimum safety distance between the UAV and the obstacle to be set, usually satisfying ρ is the repulsive force coefficient.

[0041] Preferably, in the step S5, constructing a command filter and a virtual control rate model is specifically as follows:

[0042] ​

[0043] Among them, α is the virtual control rate, satisfying R1 is the first-order state error gain, satisfying R1>0; β is the filtering virtual control unit, ω is the bounded command filter state; τ is the filtering coefficient, satisfying τ>0, and ε is the filter gain, satisfying ε>0.

[0044] Preferably, in step S6, the quadrotor UAV state controller model is constructed as follows:

[0045] The quadrotor UAV position state controller u χ =[u x u y u z T , and its expression is as follows:

[0046] Among them, is the first-order position state error, is the second-order state position error, is the second-order position error gain, is the second-order position nonlinear state estimated value, β χ is the position filtering control unit, and Ε1 is the position environment constraint condition;

[0047] The quadrotor UAV attitude state controller Its expression is as follows:

[0048] Among them, is the first-order attitude state error, is the second-order attitude state error, is the second-order attitude error gain, is the second-order attitude nonlinear state estimated value, β P is the attitude filtering control unit, and Ε2 is the attitude environment constraint condition.

[0049] An obstacle avoidance and tracking control system for a quadrotor UAV under environmental constraints, the control system includes:

[0050] Quadrotor UAV dynamic system model establishment module: used to determine the state space equation of the position and attitude of the quadrotor UAV and determine the real-time state of the UAV;

[0051] Environmental constraint condition module: On the premise of tracking and controlling the real-time state of the quadrotor UAV, the real-time state of the UAV is limited under the environmental constraint conditions;

[0052] ​Neural network non-linear state estimation module: used to estimate the non-linear state in the state space equation of the quadrotor UAV in real time and determine the real-time state of the UAV;

[0053] Obstacle avoidance module: used to effectively avoid collisions with obstacles when the quadrotor UAV encounters obstacles;

[0054] Quadrotor UAV controller module: used to control the position and attitude state of the quadrotor UAV, realizing the obstacle avoidance function and the tracking control of the expected position; at the same time, a command filter is added to this module to avoid the problem of derivative complexity explosion during the control process of the controller.

[0055] Compared with the prior art, the present invention has the following improvements and advantages:

[0056] 1. By introducing a time-varying obstacle Lyapunov function, time-varying environment-constrained control is realized, enabling efficient and smooth UAV control under different external conditions or changes in hardware physical limitations; at the same time, a potential energy function is introduced under state constraints to achieve more accurate avoidance based on known obstacles in the case of environment constraints and obstacle blockages.

[0057] 2. Through the design of an adaptive neural network under state constraints, the approximation performance of the neural network is improved by using a radial basis function design, avoiding the problem of denominator singularity of the obstacle Lyapunov function and enhancing the robustness of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a schematic structural diagram of the quadrotor position state controller of the present invention.

[0059] Figure 2 It is a schematic diagram of the motion trajectory of the quadrotor aircraft and the obstacle avoidance effect.

[0060] Figure 3 It is a schematic diagram of the artificial potential field potential energy function curve when the quadrotor aircraft encounters an obstacle.

[0061] Figure 4 It is a schematic diagram of the position control input curve in three directions in three-dimensional space.

[0062] Figure 5 It is a schematic diagram of the obstacle avoidance curve effect of the quadrotor aircraft on obstacles under environment constraints. DETAILED DESCRIPTION OF THE INVENTION

[0063] The following further outlines the present invention in conjunction with the accompanying drawings.

[0064] As Figure 1 shown, a method for obstacle avoidance and tracking control of a quadrotor UAV under environment constraints, the method comprising the following steps:

[0065] Step S1: Construct a dynamic system model of a quadrotor UAV, define the expected values of the quadrotor UAV states, and obtain the actual values of the quadrotor UAV states;

[0066] Constructing the dynamic system model of the quadrotor UAV specifically is as follows:

[0067] The position state space equation of the quadrotor UAV is:

[0068] where u χ is the position state controller, is the first-order non-linear position state, F χ is the position system dynamics, satisfying is the second-order non-linear position state;

[0069] The attitude state space equation of the quadrotor UAV is:

[0070] where u P is the attitude state controller, is the first-order non-linear attitude state, F P is the attitude system dynamics, satisfying is the second-order non-linear attitude state.

[0071] Defining the expected values of the quadrotor UAV states and obtaining the actual values of the quadrotor UAV states specifically are as follows:

[0072] Define the expected position χ 1d = [x d y d z d T and the expected attitude function:

[0073] where x d is the expected trajectory in the x direction, y d is the expected trajectory in the y direction, z d is the expected trajectory in the z direction; is the expected pitch attitude angle, θ d is the expected roll attitude angle, Ψ d is the expected yaw attitude angle, which is determined by the following formula:

[0074]

[0075] where Q = [Q x Q y Q z T is the position attitude conversion variable, P1 = [Φ θ ψ]​​T is the real - time attitude state of the quadrotor UAV, \(u\) x is the position controller in the x - direction, \(u\) y is the position controller in the y - direction, \(u\) z is the position controller in the z - direction;

[0076] In step S2, construct the state error of the quadrotor UAV and establish a state - environment - constrained model according to the state error. Specifically:

[0077] Step S2 - 1: Define the state error \(S=x\) s1 \(-x\) sd , where \(x\) s1 is the real - time state, \(x\) sd is the desired state;

[0078] Step S2 - 2: Establish a state - constrained model according to the obstacle Lyapunov function The state - constrained model is:

[0079]

[0080] Step S2 - 3: Construct a state - constrained operation unit related to the controller expression

[0081] where \(d\) is a time - varying environment - constrained parameter, satisfying \(d>0\); \(\xi\) is a constrained - condition gain, satisfying \(\xi>0\); \(c\) is a constrained - control gain, satisfying \(c>0\); \(k\) c is a time - varying constrained - condition parameter, \(k\) d is a time - varying constrained - control parameter, and the relationship between the two satisfies:

[0082]

[0083] In step S3, construct a non - linear state - estimation model based on a radial - basis - function neural network. Specifically:

[0084]

[0085] where \(\varPhi\) i is the activation - function value of the radial - basis function for the \(i\) - th training, \(\kappa\) is the width parameter of the Gaussian function, \(l\) is the center parameter of the Gaussian function, \(m\) is the center - point step of the Gaussian function; \(W\) i is the updated weight for the \(i\) - th training, \(\eta\) is the update - weight decay coefficient, \(\gamma\) is the weight - update learning rate, \(\Delta t\) is the training step, is the non - linear state - estimation value of the quadrotor UAV.

[0086] In step S4, define the obstacle position and construct a potential - energy function to generate a repulsive potential field. Specifically:

[0087] Preset the three-dimensional spatial coordinates χ of the obstacle on the i-th expected path b,i =[x b,i y b,i z b,i T , calculate the Euclidean distance z between the UAV and the i-th obstacle i =χ1 - X b,i , construct the repulsive potential field of the i-th obstacle

[0088] Further calculate the potential field repulsive force of the i-th obstacle

[0089] where X1 is the real-time position state of the quadrotor UAV; is the influence radius of the obstacle repulsive potential field to be set, r is the minimum safety distance between the UAV and the obstacle to be set, usually satisfying ρ is the repulsive force coefficient.

[0090] In step S5, construct the command filter and the virtual control rate model, specifically:

[0091]

[0092] where α is the virtual control rate, satisfying R1 is the first-order state error gain, satisfying R1 > 0; β is the filtering virtual control unit, ω is the bounded command filter state; τ is the filtering coefficient, satisfying τ > 0, ε is the filter gain, satisfying ε > 0,

[0093] In step S6, construct the quadrotor UAV state controller model, specifically:

[0094] The quadrotor UAV position state controller u X =[u x u y u z T , and its expression is as follows:

[0095] where, is the first-order position state error, is the second-order state position error, is the second-order position error gain, is the second-order position nonlinear state estimation value, βX is the position filtering control unit, Ε1 is the position environment limitation condition;

[0096] The quadrotor UAV attitude state controller Its expression is as follows: ​​

[0097] Among them, is the first-order attitude state error, is the second-order attitude state error, is the second-order attitude error gain, is the second-order attitude non-linear state estimation value, β P is the attitude filtering control unit, and Ε2 is the attitude environment constraint condition.

[0098] First-order position state error The input of is the real-time position state X1 of the quadrotor UAV output by the calculation result of the position state space equation and the preset desired position X 1d ; the input of the state error S is the real-time state x s1 output by the calculation of the quadrotor UAV state space equation and the preset desired state x sd ; the input of the state constraint model is the output S of the state error; the input of the non-linear state estimation model is the output S of the state error, the real-time state x s1 of the quadrotor UAV output by the calculation result of the state space equation, and the updated weight W i fed back and output by the non-linear state estimation model; the calculation input of the Euclidean distance z i is the real-time position state X1 of the quadrotor UAV output by the calculation result of the position state space equation and the preset obstacle coordinates X b,i ; the calculation input of the potential field repulsive force λ i is the output z of the Euclidean distance calculation; the input of the virtual control rate α is the output of the non-linear state estimation model i the output S of the state error and the real-time state x of the quadrotor UAV; the input of the command filter is the virtual control rate α; the second-order position state error s1 ; the input of is the output filtering virtual control unit β of the command filter and the second-order position state χ2 of the quadrotor UAV output by the calculation result of the position state space equation; the quadrotor UAV position state controller u ; the input of is the first-order position error χ the output position environment constraint condition Ε1 of the state constraint model and the position state constraint operation unit k , the second-order position state error i,χ the output second-order position non-linear state estimation value of the non-linear state estimation model, the output position filtering control unit β of the command filter, and the output λ χ of the potential field repulsive force calculation i .

[0099] First-order attitude state error The input is the first-order attitude state P1 output from the calculation result of the attitude state space equation and the expected attitude P output from the position attitude conversion calculation formula 1d ; The input for the position expected attitude conversion calculation is the output u of the quadrotor UAV position state controller χ ; The second-order attitude state error The input is the output filtering virtual control unit β of the command filter and the second-order attitude state P2 of the quadrotor UAV output from the calculation result of the attitude state space equation; the quadrotor UAV attitude state controller u P The input is the first-order attitude error The output attitude environmental constraint condition Ε2 of the state-constrained model and the attitude state constrained operation unit k i,P and the second-order attitude state error The output second-order attitude non-linear state estimation value of the non-linear state estimation model and the output attitude filtering control unit β of the command filter P .

[0100] An obstacle avoidance tracking control system for a quadrotor UAV under environmental constraints, the control system includes: a quadrotor UAV dynamic system model establishment module: used to determine the state space equation of the position and attitude of the quadrotor UAV and determine the real-time state of the UAV;

[0101] The environmental constraint condition module: on the premise of tracking and controlling the real-time state of the quadrotor UAV, the real-time state of the UAV is limited under the environmental constraint conditions;

[0102] The neural network non-linear state estimation module: used to estimate the non-linear state in the state space equation of the quadrotor UAV in real time and determine the real-time state of the UAV;

[0103] The obstacle avoidance module: used to effectively avoid collisions with obstacles when the quadrotor UAV encounters obstacles;

[0104] The quadrotor UAV controller module: used to control the position and attitude state of the quadrotor UAV, realize the obstacle avoidance function and the tracking control of the expected position; at the same time, a command filter is added to this module to avoid the problem of derivative complexity explosion during the control process of the controller.

[0105] Parameter selection of the quadrotor aircraft position controller model: κ1 = 1, κ2 = 1, l1 = 2, l2 = 2, m1 = 0.1, m2 = 0.1; η1 = 0.005, η2 = 0.005, γ1 = 15, γ2 = 15; K1 = 20, K2 = 20; τ1 = 0.1, ε1 = 20, ξ1 = 20, c1 = 20; In the potential field repulsive force generation unit, the repulsive force coefficient ρ = 2, and the obstacle coordinate χ b,i = [-4, -1.5, 0] T (m), the influence range of the obstacle The minimum safety distance r = 0.3 (m).

[0106] Parameter selection for the attitude controller model of the quadrotor aircraft: κ3 = 1, κ4 = 1, l3 = 2, l4 = 2, m3 = 0.1, m4 = 0.1; η3 = 0.005, η4 = 0.005, γ3 = 15, γ4 = 15, K3 = 20, K4 = 20; τ2 = 0.1, ε2 = 20, ξ2 = 20, c2 = 20;

[0107]

[0108] Set the two-dimensional expected position trajectory x 1d = 5sin(0.2t) (m), y 1d = cos(0.2t) - 1 (m);

[0109] As Figure 2 shown, at the coordinate (-3, -1.5) when the system runs for 19.5 s, the obstacle enters the detection range of the UAV (m). At this time, the potential energy function unit in the control input starts to take effect. The UAV then calculates the repulsive force according to the repulsive force unit and acts on the controller unit, causing the UAV to start avoiding the obstacle. During this process, the UAV and the obstacle are limited to outside the minimum safety distance r = 0.3 (m); at the coordinate (-4.75, -0.6) when the system running time is 21.8 s, the obstacle leaves the detection range of the UAV, and the UAV leaves the influence range of the obstacle and continues to move forward normally along the predetermined trajectory until the task is completed.

[0110] As Figure 3 shown, during the obstacle avoidance of the UAV from 19.3 s to 24.8 s, the potential energy function does not show a sudden jump and changes smoothly, which is an important guarantee for the safety of the system to execute tasks.

[0111] As Figure 4 shown, when the system dynamics change, there is still a good control effect on the UAV.

[0112] As Figure 5 shown, Figure 5 The figure shows the relationship between the position and time in the x direction within the environmental restricted interval [-7, 7]. It can be seen that the obstacle avoidance function can be well realized under the condition of environmental restriction.

[0113] The above are only embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, various modifications and variations can be made to the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the scope of the claims of the present invention.

Claims

1. A method for obstacle avoidance and tracking control of a quadrotor UAV under environmental constraints, characterized in that: The method includes the following steps: Step S1: Construct a dynamic system model of a quadrotor UAV, define the expected values of the quadrotor UAV states, and obtain the actual values of the quadrotor UAV states; Step S2: Construct the state error of the quadrotor UAV, and establish a state environment constraint model based on the state error; Step S3: Construct a non-linear state estimation model based on a radial basis function neural network, and estimate the non-linear states of the quadrotor UAV; Step S4: Define the positions of obstacles, and construct a potential energy function to generate a repulsive potential field; Step S5: Construct a command filter and a virtual control rate model to avoid the problem of derivative complexity explosion; Step S6: Construct a state controller model of the quadrotor UAV to perform real-time control of the quadrotor UAV states.

2. The method for obstacle avoidance and tracking control of a quadrotor UAV under environmental constraints according to claim 1, wherein: In step S1, constructing the dynamic system model of the quadrotor UAV is specifically as follows: The position state space equation of the quadrotor UAV is as follows: Among them, u χ is the position status controller, is the first-order non-linear position status, F χ is the position system dynamics, satisfying is the second-order non-linear position status; The attitude state space equation of a quadrotor UAV is as follows: Among them, u P is the attitude state controller, is the first-order non-linear attitude state, F P is the attitude system dynamics, satisfying is the second-order non-linear attitude state.

3. The obstacle avoidance and tracking control method for a quadrotor UAV under environmental constraints according to claim 1, wherein: In step S1, defining the expected values of the quadrotor UAV states and obtaining the actual values of the quadrotor UAV states are specifically as follows: Define the desired position χ 1d = [x d y d z d T and the desired attitude function: ​ where, x d is the desired trajectory in the x direction, y d is the desired trajectory in the y direction, z d is the desired trajectory in the z direction; is the desired pitch attitude angle, θ d is the desired roll attitude angle, Ψ d is the desired yaw attitude angle, and is determined by the following formula: Q x = (cosφsinθcosψ + sinφsinψ)u x Q y = (cosφsinθsinψ - sinφsinψ)u y ; Q z = (cosφcosθ)u z where Q = [Q x Q y Q z T is the position and attitude conversion variable, P1 = [Φ θ ψ] T is the real-time attitude state of the quadrotor UAV, u x is the position controller in the x direction, u y is the position controller in the y direction, u z is the position controller in the z direction.​ 4. A method for obstacle avoidance and tracking control of a quadrotor UAV under environmental constraints according to claim 1, characterized in that: In step S2, constructing the state error of the quadrotor UAV and establishing a state environment constraint model based on the state error are specifically as follows: Step S2-1: Define the state error S = x s1 - x sd , where x s1 is the real-time state, and x sd is the desired state; Step S2-2: Based on the barrier Lyapunov function Establish a state-constrained model, and the state-constrained model is: Step S2-3: Construct a state-limited operation unit related to the controller expression wherein, d is a time-varying environment limited parameter, satisfying d > 0; ξ is a limited condition gain, satisfying ξ > 0; c is a limited control gain, satisfying c > 0; k c is a time-varying limited condition parameter, k d is a time-varying limited control parameter, and their relationship satisfies:

5. A method for obstacle avoidance and tracking control of a quadrotor UAV under environmental constraints according to claim 1, characterized in that: In step S3, constructing a non-linear state estimation model based on a radial basis function neural network is specifically as follows: Among them, Φ i is the activation function value of the radial basis function in the i-th training, κ is the width parameter of the Gaussian function, l is the center parameter of the Gaussian function, and m is the center point step size of the Gaussian function; W i is the updated weight in the i-th training, η is the updated weight decay coefficient, γ is the weight update learning rate, Δt is the training step size, is the non-linear state estimation value of the quadrotor UAV.

6. A method for obstacle avoidance and tracking control of a quadrotor UAV under environmental constraints according to claim 1, characterized in that: In step S4, defining the positions of obstacles and constructing a potential energy function to generate a repulsive potential field are specifically as follows: Preset the three-dimensional space coordinates χ of the obstacle on the i-th expected path b,i =[x b,i y b,i z b,i T , calculate the Euclidean distance z between the UAV and the i-th obstacle i =χ1 - χ b,i , construct the repulsive potential field of the i-th obstacle ​ Further calculate the repulsive force of the potential field of the i-th obstacle Among them, χ1 is the real-time position state of the quadrotor UAV; is the influence radius of the repulsive force potential field of the obstacle to be set, r is the minimum safe distance between the UAV to be set and the obstacle, and usually satisfies ρ is the repulsive force coefficient.

7. A method for obstacle avoidance and tracking control of a quadrotor UAV under environmental constraints according to claim 1, characterized in that: In step S5, constructing a command filter and a virtual control rate model are specifically as follows: where α is the virtual control rate, satisfying R1 is the first-order state error gain, satisfying R1 > 0; β is the filtered virtual control unit, ω is the bounded command filter state; τ is the filtering coefficient, satisfying τ > 0, and ε is the filter gain, satisfying ε > 0.

8. A method for obstacle avoidance and tracking control of a quadrotor UAV under environmental constraints according to claim 1, characterized in that: In step S6, constructing a state controller model of the quadrotor UAV is specifically as follows: Quadrotor UAV position state controller u χ = [u x u y u z T , and its expression is as follows: ​ Among them, is the first-order position state error, is the second-order state position error, is the second-order position error gain, is the second-order position nonlinear state estimated value, β χ is the position filtering control unit, and Ε1 is the position environment limitation condition; Quadrotor UAV Attitude State Controller Its expression is as follows: wherein, is the first-order attitude state error, is the second-order attitude state error, is the second-order attitude error gain, is the second-order attitude nonlinear state estimate value, β P is the attitude filtering control unit, and Ε2 is the attitude environment limited condition.

9. A system composed of an obstacle avoidance and tracking control method for a quadrotor UAV under environmental constraints according to any one of claims 1-8, characterized in that: The control system includes: Quadrotor UAV dynamic system model establishment module: used to determine the state space equation of the position and attitude of the quadrotor UAV, and determine the real-time state of the UAV; Environment constraint condition module: on the premise of performing tracking control on the real-time state of the quadrotor UAV, realize restricting the real-time state of the UAV under the environment constraint conditions; Neural network non-linear state estimation module: used to estimate the non-linear states in the state space equation of the quadrotor UAV in real time, and determine the real-time state of the UAV; Obstacle avoidance module: used to effectively avoid collisions with obstacles when the quadrotor UAV encounters obstacles; Quadrotor UAV controller module: used to control the position and attitude states of the quadrotor UAV, realize the obstacle avoidance function and the tracking control of the expected position; meanwhile, a command filter is added to this module to avoid the problem of derivative complexity explosion during the control process of the controller.

Images