Trajectory tracking control method for a seven-degree-of-freedom robotic arm based on a high-order all-wheel drive system approach

Abstract

The present invention provides a seven-degree-of-freedom manipulator trajectory tracking control method based on a high-order all-wheel drive system method, which includes the following steps: S1, determining a seven-degree-of-freedom manipulator system model, and giving target joint angular positions, angular velocities, and angular accelerations; S2, establishing a dynamic model of the seven-degree-of-freedom manipulator; S3, designing an extended state observer to observe the lumped disturbances formed by model parameter uncertainties and external interference; S4, converting the manipulator dynamics model into a high-order all-wheel drive system model, compensating the lumped disturbances into a controller, and designing a high-order all-wheel drive controller; S5, building the model and conducting simulation verification. This method has a simple model processing process, a simple and effective controller design, and combined with parametric design, the parameter solution process is numerically stable. Simulation verification proves that the trajectory tracking control scheme based on the high-order all-wheel drive system method with extended state observer compensation can achieve stable and rapid tracking within a limited time domain, and has high engineering value.

Description

Technical Field

[0001] The present invention relates to the field of robot arm trajectory tracking control, and in particular to a seven-degree-of-freedom robot arm trajectory tracking control method based on a high-order all-wheel drive system method. Background Art

[0002] Industrial robots, integrating advanced technologies such as mechanics, electronics, and control, are essential automation equipment in modern manufacturing. They have become widely used automation tools in automated factories, flexible manufacturing systems, and computer-integrated manufacturing systems. With the increasing demand for intelligent robots in modern industry, industrial robotic arms, due to their simple structure, dexterity, and low energy consumption, have been widely adopted across various industries. Multi-degree-of-freedom robotic arms, the most widely used industrial robots in the industrial sector, not only replace humans in repetitive and tedious tasks such as assembly, painting, and sorting, but also perform challenging tasks in harsh and dangerous environments, such as underwater, in space, and in radioactive environments. Multi-degree-of-freedom robotic arms are complex, high-order, nonlinear, and strongly coupled multi-input and multi-output systems. Factors such as parameter uncertainty and external disturbances can affect the system's control performance. Furthermore, increasingly complex control tasks place high demands on the manipulator's timing and tracking accuracy. Therefore, tracking control technology combining industrial robotic arm systems with high-precision control methods has attracted extensive attention and research. The seven-degree-of-freedom robotic arm is a highly nonlinear and redundant system. Because it has the same joint degrees of freedom and similar structure as the human arm, it has high flexibility and precision and can complete more complex tasks. It is widely used in working environments where it can replace human arms, such as medical orthopedic robots, surgical robots, and precision cutting and other high-precision fields.

[0003] The high-order all-wheel drive system approach is a descriptive form of control system and a system model oriented towards control design. Its physical characteristic is that each degree of freedom is directly driven. For example, each joint in a robotic arm is controlled by a motor, and each rotation direction of a satellite is controlled by a flywheel. In other words, the control variables completely correspond to the state variables. The direct parametric design method for high-order all-wheel drive systems involves appropriately selecting a nonlinear state feedback control law to transform the original linear open-loop system into a linear steady-state closed-loop system with the desired characteristic structure. This control law is then explicitly expressed, offering a strong advantage in handling nonlinear problems.

[0004] The dynamics of a seven-degree-of-freedom robot arm is itself a second-order full-drive form, and it is also a highly nonlinear, strongly coupled, complex redundant dynamic system. Traditional control methods are mostly model-free methods, such as PID and PD control. These control methods do not rely on system models, but to achieve higher control accuracy, a lot of time is required for parameter tuning; and control methods that rely on models generally use first-order state-space equation models as research objects. The control accuracy can meet system requirements, but due to the complexity of the seven-degree-of-freedom robot arm dynamics model, the control laws obtained by this type of method will be very complex, and may not completely eliminate the uncertainty of the system, and may not achieve better control effects. Summary of the Invention

[0005] In order to address the deficiencies of the above-mentioned prior art, the present invention provides a seven-degree-of-freedom manipulator trajectory tracking control method based on a high-order all-wheel drive system method. In response to the parameter uncertainty of its model and the interference of the external environment, an extended state observer is designed to observe the lumped disturbance of the system, which is compensated in the controller and verified by simulation, thereby achieving effective tracking of the joint angle under a predetermined trajectory and ensuring the control accuracy, stability and rapidity of the seven-degree-of-freedom manipulator trajectory tracking control. The present invention selects a high-order all-wheel drive system model to perform trajectory tracking control of the seven-degree-of-freedom manipulator. The nonlinear terms of the system are offset by the high-order all-wheel drive method to obtain a control law and convert it into a linear steady-state closed-loop system, thereby directly obtaining the control law. This not only eliminates the complex step of converting it into a first-order state space equation, but also obtains a better control effect through simulation verification.

[0006] To achieve the above objectives, the present invention discloses the following technical solutions:

[0007] Specifically, the present invention provides a seven-degree-of-freedom robot arm trajectory tracking control method based on a high-order all-wheel drive system method, which includes the following steps:

[0008] S1. Determine a system model of a seven-degree-of-freedom robotic arm, given the desired trajectory information of the robotic arm, where the desired trajectory information includes information on the desired angular positions of each joint of the robotic arm;

[0009] S2. Establish a dynamic model of the seven-degree-of-freedom manipulator, determine the nominal part and the lumped disturbance d; the model of the lumped disturbance d of the manipulator system is as follows:

[0010]

[0011] Among them, ΔM(q), and ΔG(q) correspond to the uncertainty of the kinetic parameters; τ d is the external disturbance to each joint of the unit to be controlled;

[0012] S3. Construct a third-order extended state observer and estimate modeling uncertainty and system internal and external disturbances. This includes the following sub-steps:

[0013] S31. Construct a second-order system with an extended state observer:

[0014]

[0015] Where x1 is the system state, x2 is the derivative of x1, f is a known smooth function, b is a smooth function that affects the control input, u is the control input, and d represents the lumped disturbance;

[0016] S32. Construct a third-order extended state observer: by introducing an estimate of the lumped disturbance d Expand the second-order system to state x3 to construct a third-order extended state observer, and define And θ is bounded, the state space form of its degree of freedom manipulator is described as follows:

[0017]

[0018] The third-order extended state observer is constructed as follows:

[0019]

[0020] Where x = [x1 x2 x3 x4 x5 x6 x7] T represents the state vector, u=

[0021] [τ1τ2τ3τ4τ5τ6τ7] T represents the control input, are the observed values ​​of states x1, x2, and x3 respectively, is the state observation error of the observer, β1, β2, and β3 are the observer gain vectors, which are determined by the bandwidth and affect the convergence speed of the observation error; M0, G0, and C0 are the nominal inertia matrix, gravity matrix, and nominal Coriolis force matrix of the control unit to be controlled, respectively;

[0022] S33. Determine the observer gain as follows:

[0023]

[0024] Where ω0 is a positive real number of the observer bandwidth;

[0025] S4. Construct a high-order all-wheel drive system controller and use the estimated value of the lumped disturbance d observed by the third-order extended state observer The compensation is then incorporated into the controller. The dynamic model of the seven-degree-of-freedom manipulator is converted into a high-order all-wheel drive form and an adaptive tracking control law is obtained. The trajectory tracking controller is then obtained and parameterized. The trajectory tracking controller is obtained in the following sub-steps:

[0026] S41. Convert the robot arm dynamics model into a high-order all-wheel drive system model:

[0027]

[0028] S42, the trajectory tracking controller is obtained as:

[0029]

[0030] S5. Apply the trajectory tracking controller to the seven-degree-of-freedom robotic arm and perform simulation verification to achieve joint angle tracking.

[0031] Preferably, step S2 specifically includes the following steps:

[0032] S21. Establish a dynamic model of a seven-degree-of-freedom robotic arm as shown below:

[0033]

[0034] Where, M(q)∈R 7×7 represents the system inertia matrix, Denotes the centrifugal force and Coriolis force terms, G(q)∈R 7 represents the gravity term, τ=[τ1,τ2,…,τ7] T represents the joint torque, q=

[0035] [q1,q2,…,q7] T is the joint rotation vector, is the joint angular velocity, is the joint angular acceleration, d represents the lumped disturbance synthesized by the uncertainty of system parameters and external interference;

[0036] S22. Express the uncertainty of system parameters as:

[0037]

[0038] S23. Change the 7-DOF manipulator dynamics model to a conventional dynamics model, as shown below:

[0039]

[0040] Among them, M(q), and G(q) correspond to the nominal part of the kinetic parameters, ΔM(q), and ΔG(q) correspond to the uncertainty parts of the kinetic parameters, respectively;

[0041] S24. The model of the lumped disturbance d of the manipulator system is as follows:

[0042]

[0043] Preferably, the specific steps of parameterized design in step S4 are:

[0044] Choose a matrix F with negative diagonal elements,

[0045]

[0046] Among them, a and b are two positive scalars;

[0047] Choose any parameter matrix Z,

[0048] Z=

[11]

[0049] get,

[0050]

[0051] satisfy:

[0052] det V(Z,F)≠0

[0053] Get the parameter matrix

[0054] A 0～1 =[180060]

[0055] For any choice of F∈R nr×nr , so that the matrix A 0～1 and a non-singular matrix V∈R nr×nr satisfy:

[0056] φ(A 0～1 )=VFV -1 .

[0057] Preferably, step S41 specifically includes the following sub-steps:

[0058] S411. Common high-end all-wheel drive systems are as follows:

[0059] z (n) =H(z (0～n-1) ,t)θ+q(z (0～n-1) ,t)+L(z (0～n-1) ,t)-(x * ) (n) ;

[0060] S412, the adaptive tracking control law is as follows:

[0061]

[0062] S413, the parameters are as follows:

[0063]

[0064] S414. Transform the robot arm dynamics model into a high-order all-wheel drive system model:

[0065]

[0066] Preferably, step S42 specifically includes the following sub-steps:

[0067] S421, let x * (t)∈R r is the reference signal x(t) is to be tracked, and is defined as

[0068]

[0069] S422, modeled as a second-order adaptive full-drive system:

[0070]

[0071] in

[0072]

[0073] Obviously, the all-wheel drive conditions are met:

[0074] det L≠0

[0075] When applied to the robotic arm model, θ is the uncertainty disturbance d inside and outside the model, which has been observed by the extended state observer.

[0076] S423, the trajectory tracking controller is obtained as:

[0077]

[0078] Preferably, in step S5, a controller program is written by Matlab / Simulink for simulation verification.

[0079] Compared with the prior art, the present invention has the following beneficial effects:

[0080] (1) The present invention aims at the trajectory tracking problem of a redundant seven-degree-of-freedom manipulator with high nonlinearity, complex system and difficulty in controlling. It proposes a seven-degree-of-freedom manipulator trajectory tracking controller based on a high-order all-wheel drive system method. The control object is a seven-degree-of-freedom manipulator system with parameter uncertainty and external interference. The whole method process is simple, easy to understand and highly robust. It can achieve good control effect when applied to the trajectory tracking control of a seven-degree-of-freedom manipulator.

[0081] (2) The method of the present invention uses an extended state observer to observe the parameter uncertainty of the system and the lumped disturbance d formed by external interference. It does not require prior information about external interference and can effectively overcome model uncertainty. The controller adopts a high-order full-drive method. Compared with the traditional robotic arm control method, it is no longer based on the state space model for analysis and design. The model processing process is simple and the performance is excellent.

[0082] (3) The structural design of the controller of the present invention is simple and effective. Combined with the parametric design method, the parameter solution process is simple and clear and has good numerical stability. It can also provide sufficient design freedom and can obtain controller parameters that meet the expected performance indicators of the system.

[0083] (4) The present invention has verified the effectiveness of the proposed trajectory tracking control scheme based on the extended state observer compensation and high-order full-drive system method through simulation, which can significantly reduce overshoot and achieve stable and fast tracking in a limited time domain. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 Schematic diagram of the overall workflow of the seven-degree-of-freedom robot arm trajectory tracking control method based on the high-order all-wheel drive system method of the present invention;

[0085] Figure 2 Schematic diagram of trajectory tracking control implemented by the present invention;

[0086] Figure 3(a)-Figure 3(g) : is a diagram of the simulation results of each joint when the present invention is applied to tracking a given expected signal, wherein: Figure 3(a)-Figure 3(g) These are the given schematic diagrams of joints 1 to 7 respectively. DETAILED DESCRIPTION

[0087] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings.

[0088] This invention provides a trajectory tracking control method for a seven-degree-of-freedom robotic arm based on a high-order all-wheel drive system approach. This method aims to achieve high-precision trajectory tracking control of a seven-degree-of-freedom robotic arm, even when the dynamic and kinematic mathematical models of the robotic arm control system are uncertain. The specific method involves designing a lumped disturbance extended state observer to obtain accurate observations of the robotic arm's mathematical model errors within a finite convergence time. Furthermore, a control law based on the high-order all-wheel drive approach is designed to control the joint angular positions of the robotic arm.

[0089] The present invention provides a seven-degree-of-freedom manipulator trajectory tracking control method based on a high-order full-drive system method, such as Figure 1 As shown, it includes the following steps:

[0090] S1. Given the expected trajectory information of the robotic arm, the expected trajectory information includes information on the expected angular positions of the joints of the robotic arm.

[0091] S2. Establish a dynamic model of the seven-degree-of-freedom manipulator and determine the nominal part and the lumped disturbance d.

[0092] S3. Construct an extended state observer to estimate modeling uncertainty and system internal and external disturbances, which includes the following sub-steps:

[0093] S31. Construct a second-order system with an extended state observer:

[0094]

[0095] Among them, x1 is the system state, x2 is the derivative of x1, f is a known smooth function, b is a smooth function that affects the control input, u is the control input, and d represents the lumped disturbance. When constructing the extended state observer, the extended state observer is used to observe the parameter uncertainty of the system and the lumped disturbance d formed by external interference. It does not require prior information about external interference and can effectively overcome model uncertainty. The controller adopts a high-order full-drive method. Compared with traditional robotic arm control methods, it is no longer based on state-space model analysis and design. The model processing process is simple and the performance is excellent.

[0096] S32. Construct a third-order extended state observer: by introducing an estimate of the lumped disturbance d Expand the second-order system to state x3 to construct an observer, and define And θ is bounded, the state space form of the robot arm is described as follows:

[0097]

[0098] Finally, the third-order extended state observer is constructed as follows:

[0099]

[0100] Where x = [x1 x2 x3 x4 x5 x6 x7] T represents the state vector, u=

[0101] [τ1τ2τ3τ4τ5τ6τ7] T represents the control input, are the observed values ​​of states x1, x2, and x3 respectively, are the state observation errors of the observer, β1, β2, and β3 are the observer gain vectors, which are determined by the bandwidth and directly affect the convergence speed of the observation error. M0, G0, and C0 are the nominal inertia matrix, gravity matrix, and nominal Coriolis force matrix of the control unit to be controlled, respectively.

[0102] S33. Determine the observer gain as follows:

[0103]

[0104] Where ω0 is a positive real number that represents the bandwidth of the observer.

[0105] The state error is defined as follows:

[0106]

[0107] The error state space equation is obtained as follows:

[0108]

[0109] The characteristic polynomial of the system is as follows:

[0110]

[0111] Afterwards, the convergence of the designed extended state observer is proved to prove its convergence. The observer estimation error dynamics can be expressed as:

[0112]

[0113] definition We can get:

[0114]

[0115] therefore:

[0116]

[0117] Solving the differential equation yields:

[0118]

[0119] Assume h is bounded: |h|≤δ

[0120] Discretize P(t):

[0121]

[0122] definition That is

[0123]

[0124] therefore

[0125]

[0126] We can get:

[0127]

[0128] It is proved that ESO converges to a constant. Thus, it is proved that the convergence of the state observer meets the requirements.

[0129] S4. Construct a high-order all-wheel drive system controller to estimate the lumped disturbance d observed by the extended state observer. Compensation is incorporated into the controller; the dynamic model of the seven-degree-of-freedom robotic arm is converted into a high-order all-wheel drive form, the control law is calculated, and effective tracking is achieved in a limited time domain.

[0130] S41. Convert the robot arm dynamics model into a high-order all-wheel drive system model:

[0131]

[0132] Preferably, step S41 specifically includes the following sub-steps:

[0133] S411. Common high-end all-wheel drive systems are as follows:

[0134] z (n) =H(z (0～n-1) ,t)θ+q(z (0～n-1) ,t)+L(z (0～n-1) ,t)-(x * ) (n) .

[0135] S412, the adaptive tracking control law is as follows:

[0136]

[0137] S413, the parameters are as follows:

[0138]

[0139] S414. Transform the robot arm dynamics model into a high-order all-wheel drive system model:

[0140]

[0141] S42, the trajectory tracking controller is obtained as:

[0142]

[0143] Step S42 specifically includes the following sub-steps:

[0144] S421, let x * (t)∈R ris the reference signal x(t) is to be tracked, and is defined as

[0145] Z=Xx * .

[0146] S422, modeled as a second-order adaptive full-drive system:

[0147]

[0148] in

[0149]

[0150] Obviously, the all-wheel drive conditions are met:

[0151] det L≠0

[0152] When applied to the robotic arm model, θ is the uncertainty disturbance d inside and outside the model, which has been observed by the extended state observer.

[0153] S423, the trajectory tracking controller is obtained as:

[0154]

[0155] S43. Perform parametric design and select a matrix F with negative diagonal elements.

[0156]

[0157] Where a and b are two positive scalars.

[0158] S44, select any parameter matrix Z,

[0159] Z=

[11]

[0160] get,

[0161]

[0162] satisfy:

[0163] det V(Z,F)≠0

[0164] Get the parameter matrix

[0165] A 0～1 =[180060]

[0166] For any choice of F∈R nr×nr , so that the matrix A 0～1 and a non-singular matrix V∈R nr×nr satisfy:

[0167] φ(A 0～1 )=VFV -1.

[0168] The robust stability of the controller is then proved:

[0169] After substituting the control law into the system equation, we can get the closed-loop system x (n) +A (0～n-1) =φ(x (0～n-1) ) Design Lyapunov function

[0170]

[0171] Derivative of it:

[0172]

[0173] Satisfy the conditions: Φ T P+PΦ<-μP

[0174] because

[0175]

[0176] Satisfy the condition: ‖Δf(x (0～n-1) )‖≤ρ(x (0～n-1) )

[0177] Solving the differential equation yields

[0178]

[0179] It is proved that the closed-loop system of the control converges to an ellipse.

[0180] S5. Apply the controller to a seven-degree-of-freedom robotic arm and perform simulation verification to achieve joint angle tracking. Specific embodiments

[0182] The entire working process of the present invention will be further described below with reference to specific examples:

[0183] S1. Given the expected trajectory information of the robotic arm, the expected trajectory information includes information on the expected angular positions of the joints of the robotic arm.

[0184] S2. Establish a dynamic model of the seven-degree-of-freedom manipulator and determine the nominal part and the lumped disturbance d.

[0185] S3. Construct an extended state observer and estimate modeling uncertainties and internal and external disturbances of the system.

[0186] S4. Construct a high-order all-wheel drive system controller and use the estimated value of the lumped disturbance d observed by the third-order extended state observer The compensation is incorporated into the controller; the dynamic model of the seven-degree-of-freedom robotic arm is converted into a high-order all-wheel drive form and the adaptive tracking control law is obtained, and then the trajectory tracking controller is obtained and parameterized.

[0187] S5. Apply the controller to the seven-degree-of-freedom robotic arm. The application process is shown in the following figure. Figure 2 As shown in the figure, simulation verification is carried out to achieve joint angle tracking. Figure 3(a)-Figure 3(g) They are respectively diagrams of simulation results of joints 1 to 7 when the present invention is applied to tracking given expected signals.

[0188] During the simulation, the simulation duration is set to 10 seconds and the simulation step is set to 0.01 seconds; the gravity matrix G is defined as 9.81m / s 2 , which is a 7×1 matrix. The joint angle trajectory tracking simulation results are shown in Figure 3. By applying the extended state observer (ESO) to observe the lumped parameter disturbances caused by internal joint friction and external interference in the robot arm, it can be seen that it can effectively enhance the system's anti-interference ability. At the beginning of the simulation, there is a slightly large overshoot of 0.3°. After the operation stabilizes, the steady-state error of each joint can be controlled within 0.05°.

[0189] The above simulation verification proves the effectiveness of the proposed trajectory tracking control scheme based on the high-order all-wheel drive system method with extended state observer compensation, which can significantly reduce overshoot and achieve stable and fast tracking in a limited time domain.

[0190] The embodiments described above are merely descriptions of preferred implementations of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should fall within the scope of protection determined by the claims of the present invention.

Claims

1. A seven-degree-of-freedom robot arm trajectory tracking control method based on a high-order all-wheel drive system method, characterized by: It includes the following steps: S1. Determine a system model of a seven-degree-of-freedom robotic arm, given the desired trajectory information of the robotic arm, where the desired trajectory information includes information on the desired angular positions of each joint of the robotic arm; S2. Establish the dynamic model of the seven-degree-of-freedom manipulator and determine the nominal part and the lumped disturbance ; Get the lumped disturbance of the manipulator system The model is as follows: ； in, and They correspond to the uncertainty parts of the kinetic parameters respectively; is the external disturbance to each joint of the unit to be controlled; S3. Construct a third-order extended state observer and estimate modeling uncertainty and system internal and external disturbances. This includes the following sub-steps: S31. Construct a second-order system with an extended state observer: ； in, is the system status, for The derivative of is a known smooth function, is a smooth function that affects the control input, is the control input, represents the lumped disturbance; S32. Constructing a third-order extended state observer: by introducing the lumped disturbance Estimated value of , expand the second-order system to the state Construct a third-order extended state observer and define ,and Bounded, the state space form of the robot arm with its degrees of freedom is described as follows: ； The third-order extended state observer is constructed as follows: ； in, represents the state vector, represents the control input, Status The observed value of is the state observation error of the observer, are the observer gain vectors, which are determined by the bandwidth and affect the convergence speed of the observation error; M0, G0, and C0 are the nominal inertia matrix, gravity matrix, and nominal Coriolis force matrix of the control unit to be controlled, respectively; S33. Determine the observer gain as follows: ； in, is a positive real number representing the bandwidth of the observer; S4. Construct a high-order all-wheel drive system controller to transform the lumped disturbance observed by the third-order extended state observer into Estimated value of The compensation is then incorporated into the controller. The dynamic model of the seven-degree-of-freedom manipulator is converted into a high-order all-wheel drive form and an adaptive tracking control law is obtained. The trajectory tracking controller is then obtained and parameterized. The trajectory tracking controller is obtained in the following sub-steps: S41. Convert the robot arm dynamics model into a high-order all-wheel drive system model: ； S42, the trajectory tracking controller is obtained as: ； S5. Apply the trajectory tracking controller to the seven-degree-of-freedom robotic arm and perform simulation verification to achieve joint angle tracking.

2. The seven-degree-of-freedom robot arm trajectory tracking control method based on the high-order all-wheel drive system method according to claim 1 is characterized by: Step S2 specifically includes the following steps: S21. Establish a dynamic model of a seven-degree-of-freedom robotic arm as shown below: ； Where, represents the system inertia matrix, represents the centrifugal force and the Coriolis force term, represents the gravity term, represents the joint torque, is the joint rotation vector, is the joint angular velocity, is the joint angular acceleration, Represents the lumped disturbance synthesized by the uncertainty of system parameters and external interference; S22. Express the uncertainty of system parameters as: ； S23. Change the 7-DOF manipulator dynamics model to a conventional dynamics model, as shown below: ； in, and correspond to the nominal part of the kinetic parameters, and They correspond to the uncertainty parts of the kinetic parameters respectively; S24, get the lumped disturbance of the manipulator system The model is as follows: 。 3. The seven-degree-of-freedom robot arm trajectory tracking control method based on the high-order all-wheel drive system method according to claim 1, characterized in that: The specific steps of parameterized design in step S4 are: Select a matrix with negative diagonal elements , ； in, and are two positive scalars; Choose any parameter matrix , ； get: ； satisfy: ； Get the parameter matrix: ； For any choice , so that the matrix and non-singular matrices satisfy: 。 4. The seven-degree-of-freedom robot arm trajectory tracking control method based on the high-order all-wheel drive system method according to claim 1, characterized in that: Step S41 specifically includes the following sub-steps: S411. Common high-end all-wheel drive systems are as follows: ； S412, the adaptive tracking control law is as follows: ； S413, the parameters are as follows: ； S414. Transform the robot arm dynamics model into a high-order all-wheel drive system model: 。 5. The seven-degree-of-freedom robot arm trajectory tracking control method based on the high-order all-wheel drive system method according to claim 1, characterized in that: Step S42 specifically includes the following sub-steps: S421、Set yes To track the reference signal, define ； S422, modeled as a second-order adaptive full-drive system: ； in ； Obviously, the all-wheel drive conditions are met: ； When applied to a robotic arm model, Interference from uncertainty inside and outside the model , observed by the extended state observer; S423, the trajectory tracking controller is obtained as: 。 6. The seven-degree-of-freedom robot arm trajectory tracking control method based on a high-order all-wheel drive system method according to claim 1, characterized in that: In step S5, a controller program is written using Matlab / Simulink for simulation verification.

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