Unmanned aerial vehicle adaptive fuzzy tracking control method based on disturbance observer

Abstract

The invention relates to an unmanned aerial vehicle adaptive fuzzy tracking control method based on a disturbance observer, and the method comprises the following steps: 1, considering the uncertainty and external disturbance at the same time, and constructing an unmanned helicopter attitude and height nonlinear system model; and 2, designing an adaptive fuzzy algorithm by using the real-time attitude angle, the height constraint information and a preset expected tracking signal to obtain a safety expected tracking signal. And step 3, based on the fuzzy logic system constructed in the step 2 and the inverse optimal control method, according to the steps, the unmanned helicopter can complete tracking of a preset expected signal on the basis of ensuring system safety and stability under the condition that the unmanned helicopter considers the influence of a constant attitude angle, height constraint and unknown external interference according to the steps, so that the unmanned helicopter is enabled to complete tracking of the preset expected signal. Good market prospects and development space are realized.

Description

Technical Field

[0001] The invention relates to the military and civilian fields, and is an adaptive fuzzy tracking control scheme for an unmanned helicopter based on a disturbance observer. Background Art

[0002] With the development of science and technology, medium-sized unmanned helicopters (UAHs) have been widely used in various civil and military fields. And a lot of development results have been achieved. However, during the flight of UAVs, they will be disturbed by many factors, which will lead to the general system uncertainty of unmanned helicopter systems, which will have an adverse effect on the performance and even stability of UAHs. At the same time, it will also cause certain dangers to UAVs.

[0003] Therefore, in most flight missions, drone helicopters need to have certain optimal performance. For example, when conducting forest fire detection and geological exploration in narrow spaces, the flight path is complex and changeable, and it is necessary to constantly avoid obstacles and plan new flight paths. At the same time, the constantly changing external environment during the mission affects the stability of the drone helicopter. All of these have high requirements for the safety tracking performance of drones.

[0004] Based on the above discussion on the uncertainty and disturbance of UAV system, safe flight trajectory and other issues, we proposed an adaptive fuzzy tracking control scheme based on disturbance observer using FLS technology, second-order NDO technology, the newly developed flight boundary protection algorithm (FBPA), inverse optimal control method and DSC method. Summary of the invention

[0005] The present invention provides an adaptive fuzzy tracking control method for unmanned aerial vehicles based on a disturbance observer, which has the ability to enable the unmanned aerial vehicle system to adapt to the optimal capability when facing uncertainty and external interference. At the same time, a new FBPA is proposed to generate a safe flight trajectory considering the time-varying fpc of the unmanned helicopter; then an improved first-order filter is proposed to deal with the piecewise differentiability problem of the safe flight trajectory; and the generated safe flight trajectory is tracked for the unmanned helicopter system, and a fuzzy optimal tracking control method based on a disturbance observer is further proposed to ensure that the unmanned helicopter can avoid obstacles according to the generated optimal performance safe flight trajectory during flight.

[0006] The technical solution adopted by the present invention to solve the above technical problems is: an adaptive fuzzy tracking control method for unmanned aerial vehicles based on interference observer, comprising the following steps:

[0007] Step 1: Considering uncertainty and external interference, the unmanned helicopter attitude and highly nonlinear system model is constructed. The model is constructed using a fast clustering algorithm and an online self-labeling algorithm.

[0008] Step 2: Using the real-time attitude angle and altitude constraint information and the preset expected tracking signal, an adaptive fuzzy algorithm is designed to obtain a safe expected tracking signal;

[0009] Step 3: Based on the fuzzy logic system and inverse optimal control method constructed in step 2, an adaptive fuzzy tracking control method based on disturbance observer is designed;

[0010] Step 4. Combining the above steps, the unmanned helicopter can ensure the safety and stability of the system while considering the constant attitude angle and altitude constraints and the influence of unknown external interference, and complete the tracking of the preset expected signal.

[0011] As a further optimization scheme of the above-mentioned UAV adaptive fuzzy tracking control method based on disturbance observer, the process of establishing the anti-interference fast clustering algorithm and online self-labeling algorithm in step 1 is as follows:

[0012] a. Unmanned helicopter attitude and height nonlinear model,

[0013] When considering uncertainty and unknown external interference, combined with the aerodynamics and kinematics of the unmanned helicopter, the attitude and height nonlinear model is described as:

[0014]

[0015] Where P = [X, Y, Z] T ,v x =[v y ,v z ,v] T Respectively represent the position vector and velocity vector in the inertial system; and Ω=[p,q,r] T Respectively represent the attitude angle vector and angular velocity vector in the fixed body coordinate system; Δf v (P,v), Represent system uncertainty and external interference respectively; G=[0,0,m0g] T , where m0 is the Δ(ω) of the unmanned helicopter

[0016] mass, g is the acceleration due to gravity; represents a 3×3 skew-symmetric matrix, which is in the form of,

[0017]

[0018] b. Nonlinear model simplification,

[0019] J = diag{J xx ,J yy ,J zz} is the inertia matrix, R ebFrom the fixed system R e To inertial frame R b The r is expressed as

[0020]

[0021] The attitude motion matrix H is defined as,

[0022]

[0023] DSMT4

[0024] F=[0,0,-T mr ] T and Σ=[ x Σ, y Σ, z Σ] T are the control force vector and torque vector generated by the main rotor and tail rotor respectively;

[0025] c. Attitude and altitude constraints and external disturbance description,

[0026] Considering that unmanned helicopters are often subject to altitude constraints and attitude angle constraints, the upper and lower bounds of the system are defined as and Due to the unpredictability of constraints, when the expected tracking signal When the boundary value is exceeded, the unmanned helicopter control system will give priority to its own safety.

[0027] As a further optimization scheme of the above-mentioned UAV adaptive fuzzy tracking control method based on disturbance observer, the process of constructing a new safety expectation tracking signal in step 2 is as follows:

[0028] a. Fuzzy logic system,

[0029] Use fuzzy logic system to approximate any given unknown continuous function f(x);

[0030] b. Fuzzy logic system through single-case fuzzification, central average defuzzification and product reasoning,

[0031] It can be expressed as:

[0032]

[0033] in

[0034] By defining the fuzzy basis function as,

[0035]

[0036] FLS can be rewritten as,

[0037]

[0038] in and S(x)=[s1,...,s s ] T denote the adjustable parameter vector and the fuzzy basis function vector respectively;

[0039] c. Assume f(x) is defined on a bounded compact set A continuous function on . Then, for any constant ε * >0, there exists a fuzzy logic system such that,

[0040]

[0041] d. Inverse optimal control method,

[0042] Consider the following nonlinear affine system:

[0043]

[0044] in and Represent the state vector and input vector respectively; function and It is smooth;

[0045] Assume that the system (6) is stabilized by the following feedback controller:

[0046] u(x)=κ(x)=-R -1 (x)[L go V0(x)] T ,

[0047] in is a positive definite matrix-valued function, where V0(x) is a positive definite radially unbounded Lyapunov function of system (6); therefore, we have For all x≠0; then the controller:

[0048] u * =κ * (x)=ακ(x),α≥2,

[0049] To design an adaptive fuzzy tracking control scheme based on disturbance observer for an unmanned helicopter system with uncertainties, fps and external disturbances, the following assumptions are made:

[0050] Assumption 1: External disturbance d(t) = [d T v (t), d T Σ (t)]T and its first- and second-order derivatives are bounded for all t. That is, there exists an unknown positive constant ξ i (i=0,1,2), such that ‖d(i)‖≤ξ i ;

[0051] Assumption 2: For all t ≥ 0, the first and second derivatives of the desired flight trajectory y d (t), y d '(t) and y d ” (t) is bounded; more specifically, the desired flight trajectory can be given by a known positive constant ξ d The following set is described,

[0052]

[0053] As a further optimization scheme of the above-mentioned UAV adaptive fuzzy tracking control method based on disturbance observer, the design process of the adaptive fuzzy tracking control scheme in step 3 is as follows:

[0054] a. Calculation of safe flight trajectory,

[0055] In order to achieve safe tracking performance, the track constraint y up (t) and y low (t) Calculate the safe flight trajectory P s , to track the desired flight trajectory y d ; Based on k sampling points X k,z (t k )(t k = k·Δt s ), where z∈{up,low}, k=1,…,n,Δt s Represents the sampling period of the sensor, and obtains [t a ,t b ]=ζ×Δt s The b-spline curve inside;

[0056]

[0057] Where N k,s (t) is the kth fitting point X k,z (t k )The s-order B-spline basis function related to , can be recursively calculated as follows:

[0058]

[0059] Since the sensor has a detection range, the discrete sampling point X k,z (t k ) is bounded during flight; the trajectory constraint can be expressed by two known positive constants ξ and To describe:

[0060]

[0061] b. Calculation constraints c trajectory x, so that it is within the track constraints and as close as possible to the desired trajectory x;

[0062] X c (r)(r=0,1,2) can be expressed as,

[0063]

[0064] Where r = 0, 1, 2, represents the safety margin to be determined;

[0065] c. Introduce a first-order filter as a smoothing tool to process P c The piecewise differentiability of An unbiased estimate of in and Respectively represent P c and By defining P c The resulting first-order filter is:

[0066]

[0067] There is a positive definite diagonal matrix τ 11 , P s (0) = P c (0), γ s (0)=0.

[0068] As a further optimization scheme of the above-mentioned UAV adaptive fuzzy tracking control method based on disturbance observer, in step 1, Δf v (P,v)DSMT4, Represent system uncertainty and external disturbance respectively. Helicopter mass m0, gravity acceleration g.

[0069] As a further optimization scheme of the above-mentioned UAV adaptive fuzzy tracking control method based on disturbance observer, in the step 2, the safety tracking control problem of the attitude and altitude system of the unmanned helicopter is converted into an adaptive fuzzy tracking control problem through model conversion.

[0070] Compared with the prior art, the present invention has the following beneficial effects:

[0071] 1. The adaptive fuzzy tracking control scheme based on disturbance observer for unmanned helicopter is applied to the problem of safe adaptive tracking control flight trajectory of unmanned helicopter through FLS technology, second-order NDO technology, newly developed flight boundary protection algorithm (FBPA), inverse optimal control method and DSC method. It ensures the safety performance of the unmanned helicopter system during flight and the feasibility of the scheme to change its flight path according to different environments.

[0072] 2. The adaptive fuzzy tracking control scheme based on disturbance observer of the unmanned helicopter uses FLS with strong approximation ability to approximate system uncertainty, and proposes many disturbance observer-based control (DOBC) schemes. In order to achieve nonlinear optimal control performance, an adaptive fuzzy optimal controller is designed using the adaptive dynamic programming (ADP) method and fls. A novel online policy iteration method is used to solve the adaptive optimal control problem. For nonlinear systems affected by uncertainty and external disturbances, this scheme also proposes a composite optimal controller. At the same time, since the fps is time-varying and the details of the real-time fpc can be detected through some online environmental perception, the smooth curve of its boundary can be obtained through conventional fitting and difference methods, and the UAH is kept within the fps, thereby ensuring the safety performance and reducing the tracking error. The newly proposed FBPA in the scheme can be used to generate a safe flight trajectory considering the time-varying fps of the UAH, thereby achieving the invention purpose of adaptive fuzzy tracking. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a system control flow chart of the present invention.

[0074] Marked in the figure:. DETAILED DESCRIPTION

[0075] The specific implementation modes of the present invention are further described in detail below in conjunction with the accompanying drawings.

[0076] An adaptive fuzzy tracking control method for unmanned aerial vehicle based on disturbance observer comprises the following steps:

[0077] Step 1: Considering uncertainty and external interference, the unmanned helicopter attitude and highly nonlinear system model is constructed. The model is constructed using a fast clustering algorithm and an online self-labeling algorithm.

[0078] Step 2: Using the real-time attitude angle and altitude constraint information and the preset expected tracking signal, an adaptive fuzzy algorithm is designed to obtain a safe expected tracking signal;

[0079] Step 3: Based on the fuzzy logic system and inverse optimal control method constructed in step 2, an adaptive fuzzy tracking control method based on disturbance observer is designed;

[0080] Step 4. Combining the above steps, the unmanned helicopter can ensure the safety and stability of the system while considering the constant attitude angle and altitude constraints and the influence of unknown external interference, and complete the tracking of the preset expected signal.

[0081] The process of establishing the anti-interference fast clustering algorithm and online self-labeling algorithm in step 1 is as follows:

[0082] b. Unmanned helicopter attitude and height nonlinear model,

[0083] When considering uncertainty and unknown external interference, combined with the aerodynamics and kinematics of the unmanned helicopter, the attitude and height nonlinear model is described as:

[0084]

[0085] Where P = [X, Y, Z] T ,v x =[v y ,v z ,v] T Respectively represent the position vector and velocity vector in the inertial system; and Ω=[p,q,r] T Respectively represent the attitude angle vector and angular velocity vector in the fixed body coordinate system; Δf v (P,v), Represent system uncertainty and external interference respectively; G=[0,0,m0g] T , where m0 is the mass of the unmanned helicopter, g is the acceleration of gravity; Δ(ω) represents a3×3 skew-symmetric matrix, which is in the form of,

[0086]

[0087] b. Nonlinear model simplification,

[0088] J = diag{J xx ,J yy ,J zz} is the inertia matrix, R eb From the fixed system R e To inertial frame R b The r is expressed as

[0089]

[0090] The attitude motion matrix H is defined as,

[0091]

[0092] DSMT4

[0093] F=[0,0,-T mr ] T and Σ=[ x Σ, y Σ, z Σ] T are the control force vector and torque vector generated by the main rotor and tail rotor respectively;

[0094] c. Attitude and altitude constraints and external disturbance description,

[0095] Considering that unmanned helicopters are often subject to altitude constraints and attitude angle constraints, the upper and lower bounds of the system are defined as and Due to the unpredictability of constraints, when the expected tracking signal When the boundary value is exceeded, the unmanned helicopter control system will give priority to its own safety.

[0096] The process of constructing a new safety expectation tracking signal in step 2 is as follows:

[0097] e. Fuzzy logic system,

[0098] Use fuzzy logic system to approximate any given unknown continuous function f(x);

[0099] f. Fuzzy logic system through singleton fuzzification, central average defuzzification and product reasoning,

[0100] It can be expressed as:

[0101]

[0102] in

[0103] By defining the fuzzy basis function as,

[0104]

[0105] FLS can be rewritten as,

[0106]

[0107] in and S(x)=[s1,...,s s ] T denote the adjustable parameter vector and the fuzzy basis function vector respectively;

[0108] g. Assume f(x) is defined on a bounded compact set A continuous function on . Then, for any constant ε * >0, there exists a fuzzy logic system such that,

[0109]

[0110] h. Inverse optimal control method,

[0111] Consider the following nonlinear affine system:

[0112]

[0113] in and Represent the state vector and input vector respectively; function and It is smooth;

[0114] Assume that the system (6) is stabilized by the following feedback controller:

[0115] u(x)=κ(x)=-R -1 (x)[L go V0(x)] T ,

[0116] in is a positive definite matrix-valued function, where V0(x) is a positive definite radially unbounded Lyapunov function of system (6); therefore, we have For all x≠0; then the controller:

[0117] u * =κ * (x)=ακ(x),α≥2,

[0118] In order to design an adaptive fuzzy tracking control scheme based on disturbance observer for an unmanned helicopter system with uncertainties, FPC and external disturbances, the following assumptions are made:

[0119] Assumption 1: External disturbance d(t) = [d T v (t), d T Σ (t)] T and its first- and second-order derivatives are bounded for all t. That is, there exists an unknown positive constant ξ i (i=0,1,2), such that ||d(i)||≤ξ i ;

[0120] Assumption 2: For all t ≥ 0, the first and second derivatives of the desired flight trajectory y d (t), y d '(t) and y d ” (t) is bounded; more specifically, the desired flight trajectory can be given by a known positive constant ξ d The following set is described,

[0121]

[0122] The design process of the adaptive fuzzy tracking control scheme in step 3 is as follows: d. Calculation of safe flight trajectory,

[0123] In order to achieve safe tracking performance, the track constraint y up (t) and y low (t) Calculate the safe flight trajectory P s , to track the desired flight trajectory y d ; Based on k sampling points X k,z (t k )(t k = k·Δt s ), where z∈{up,low}, k=1,…,n,Δt s Represents the sampling period of the sensor, and obtains [t a ,t b ]=ζ×Δt s The b-spline curve inside;

[0124]

[0125] Where N k,s (t) is the kth fitting point X k,z (t k )The s-order B-spline basis function related to , can be recursively calculated as follows:

[0126]

[0127] Since the sensor has a detection range, the discrete sampling point X k,z (t k ) is bounded during flight; the trajectory constraint can be expressed by two known positive constants ξ and To describe:

[0128]

[0129] e. Calculation constraints c trajectory x, so that it is within the track constraints and as close as possible to the desired trajectory x;

[0130] X c (r)(r=0,1,2) can be expressed as,

[0131]

[0132] Where r = 0, 1, 2, represents the safety margin to be determined;

[0133] f. Introduce a first-order filter as a smoothing tool to process P c The piecewise differentiability of An unbiased estimate of in and Respectively represent P c and By defining P c The resulting first-order filter is:

[0134]

[0135] There is a positive definite diagonal matrix τ 11 , P s (0) = P c (0), γ s (0)=0.

[0136] In the step 1, Δf v (P,v)DSMT4, Represent system uncertainty and external disturbance respectively. Helicopter mass m0, gravity acceleration g.

[0137] In the step 2, the safety tracking control problem for the attitude and altitude system of the unmanned helicopter is converted into an adaptive fuzzy tracking control problem through model conversion.

[0138] Illustration Description:

[0139] This figure first describes the dynamics of the drone in the position loop, using a fuzzy logic system to approximate the functions in the dynamics. Starting from the safety expectation signal, the error and derivative error between the actual trajectory and the safety expectation trajectory are listed.

[0140] A virtual controller is designed, during which candidate Lyapunov functions can be selected. To estimate the composite disturbance, a second-order nonlinear disturbance observer is constructed, which is simplified, and a first-order filter is introduced to avoid repeated differentiation of the virtual control law. The controller is designed through the above steps.

[0141] In the adaptive fuzzy optimal tracking control system of the attitude loop, the expected signals of the main rotation angle force and the roll angle and pitch angle are obtained through the sub-position system, and the expected attitude angle of the unmanned helicopter can be directly obtained from the expected signal generator. First, the dynamics of the UAV system in the attitude loop are described by using another fuzzy logic system to approximate the unknown nonlinear function. Then the tracking error is defined, and then the design of the virtual controller and torque is started, taking into account the feedback control term that offsets the influence of uncertainty and interference in the normal stable attitude subsystem and the inverse optimal attitude control term that stabilizes the corresponding error system. After that, the feedback control term is designed, which includes a first-order filter, a fuzzy logic system and a second-order nonlinear disturbance observer.

[0142] The present invention provides an adaptive fuzzy tracking control method for unmanned aerial vehicles based on a disturbance observer, which has the ability to enable the unmanned aerial vehicle system to adapt to the optimal capability when facing uncertainty and external interference. At the same time, a new FBPA is proposed to generate a safe flight trajectory considering the time-varying FPC of the unmanned helicopter; an improved first-order filter is proposed to deal with the piecewise differentiability problem of the safe flight trajectory; and the unmanned helicopter system tracks the generated safe flight trajectory, and further proposes a fuzzy optimal tracking control method based on a disturbance observer to ensure that the unmanned helicopter can fly and avoid obstacles according to the generated optimal performance safe flight trajectory during flight. For the existing technology, it has a good market prospect and development space.

[0143] The preferred specific implementation modes and embodiments of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above implementation modes and embodiments, and various changes can be made within the knowledge scope of those skilled in the art without departing from the concept of the present invention.

Claims

1. An adaptive fuzzy tracking control method for unmanned aerial vehicles based on disturbance observer, characterized in that: The following steps are included: Step 1: Considering uncertainty and external interference, the unmanned helicopter attitude and highly nonlinear system model is constructed. The model is constructed using a fast clustering algorithm and an online self-labeling algorithm. Step 2: Using the real-time attitude angle and altitude constraint information and the preset expected tracking signal, an adaptive fuzzy algorithm is designed to obtain a safe expected tracking signal; Step 3: Based on the fuzzy logic system and inverse optimal control method constructed in step 2, an adaptive fuzzy tracking control method based on disturbance observer is designed; Step 4. Combining the above steps, the unmanned helicopter can ensure the safety and stability of the system while considering the constant attitude angle and altitude constraints and the influence of unknown external interference, and complete the tracking of the preset expected signal.

2. The method for adaptive fuzzy tracking control of a UAV based on a disturbance observer according to claim 1, characterized in that: The process of establishing the anti-interference fast clustering algorithm and online self-labeling algorithm in step 1 is as follows: a. Unmanned helicopter attitude and height nonlinear model, When considering uncertainty and unknown external interference, combined with the aerodynamics and kinematics of the unmanned helicopter, the attitude and height nonlinear model is described as: Where P = [X, Y, Z] T ,v x =[v y ,v z ,v] T Respectively represent the position vector and velocity vector in the inertial system; and Ω=[p,q,r] T Respectively represent the attitude angle vector and angular velocity vector in the fixed body coordinate system; Δfv(P,v), d v , Represent system uncertainty and external interference respectively; G=[0,0,m0g] T , where m0 is the mass of the unmanned helicopter, g is the acceleration of gravity; Δ(ω) represents a3×3 skew-symmetric matrix, which is in the form of, b. Nonlinear model simplification, J = diag{J xx ,J yy ,J zz } is the inertia matrix, R eb From the fixed system R e To inertial frame R b The r is expressed as The attitude motion matrix H is defined as, DSMT4 F=[0,0,-T mr ] T and Σ=[ x Σ, y Σ, z Σ] T are the control force vector and torque vector generated by the main rotor and tail rotor respectively; c. Attitude and altitude constraints and external disturbance description, Considering that unmanned helicopters are often subject to altitude constraints and attitude angle constraints, the upper and lower bounds of the system are defined as and Due to the unpredictability of constraints, when the expected tracking signal When the boundary value is exceeded, the unmanned helicopter control system will give priority to its own safety.

3. The method for adaptive fuzzy tracking control of unmanned aerial vehicle based on disturbance observer according to claim 1, characterized in that: The process of constructing a new safety expectation tracking signal in step 2 is as follows: a. Fuzzy logic system, Use fuzzy logic system to approximate any given unknown continuous function f(x); b. Fuzzy logic system through single-case fuzzification, central average defuzzification and product reasoning, It can be expressed as: in By defining the fuzzy basis function as, FLS can be rewritten as, in and denote the adjustable parameter vector and the fuzzy basis function vector respectively; c. Assume f(x) is defined on a bounded compact set A continuous function on . Then, for any constant ε * >0, there exists a fuzzy logic system such that, d. Inverse optimal control method, Consider the following nonlinear affine system: in and Represent the state vector and input vector respectively; function f0: and g0: It is smooth; Assume that the system (6) is stabilized by the following feedback controller: u(x)=κ(x)=-R -1 (x)[L go V0(x)] T , Where R(x): is a positive definite matrix-valued function, where V0(x) is a positive definite radially unbounded Lyapunov function of system (6); therefore, we have For all x≠0; Then the controller: you * =k * (x)=ακ(x),α≥2, To design an adaptive fuzzy tracking control scheme based on disturbance observer for an unmanned helicopter system with uncertainties, FPC and external disturbances, the following assumptions are made: Assumption 1: External disturbance d(t) = [d T v (t), d T Σ (t)] T and its first- and second-order derivatives are bounded for all t. That is, there exists an unknown positive constant ξ i (i=0,1,2), such that ||d(i)||≤ξ i ; Assumption 2: For all t ≥ 0, the first and second derivatives of the desired flight trajectory y d (t), y d '(t) and y d ” (t) is bounded; more specifically, the desired flight trajectory can be given by a known positive constant ξ d The following set is described, 4. The method for adaptive fuzzy tracking control of a UAV based on a disturbance observer as claimed in claim 1, characterized in that: The design process of the adaptive fuzzy tracking control scheme in step 3 is as follows: a. Calculation of safe flight trajectory, In order to achieve safe tracking performance, the track constraint y up (t) and y low (t) Calculate the safe flight trajectory P s , to track the desired flight trajectory y d ; Based on k sampling points X k,z (t k )(t k = k·Δt s ), where z∈{up,low}, k=1,…,n,Δt s Represents the sampling period of the sensor, and obtains [t a ,t b ]=ζ×Δt s The b-spline curve inside; Where N k,s (t) is the kth fitting point X k,z (t k )The s-order B-spline basis function related to , can be recursively calculated as follows: Since the sensor has a detection range, the discrete sampling point X k,z (t k ) is bounded during flight; the trajectory constraint can be expressed by two known positive constants ξ and To describe: b. Calculation constraints c trajectory x, so that it is within the track constraints and as close as possible to the desired trajectory x; X c (r)(r=0,1,2) can be expressed as, Where r = 0, 1, 2, represents the safety margin to be determined; c. Introduce a first-order filter as a smoothing tool to process P c The piecewise differentiability of An unbiased estimate of in and Respectively represent P c and By defining P c The resulting first-order filter is: There is a positive definite diagonal matrix τ 11 , P s (0) = P c (0), γ s (0)=0.

5. The method for adaptive fuzzy tracking control of unmanned aerial vehicle based on disturbance observer according to claim 1, characterized in that: In the step 1, Δf v (P,v)DSMT4, d v , Represent system uncertainty and external disturbance respectively. Helicopter mass m0, gravity acceleration g.

6. The method for adaptive fuzzy tracking control of a UAV based on a disturbance observer as claimed in claim 1, characterized in that: In the step 2, the safety tracking control problem for the attitude and altitude system of the unmanned helicopter is converted into an adaptive fuzzy tracking control problem through model conversion.

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