Active compliance control method for power transmission line live maintenance mechanical device

Abstract

The invention discloses an active compliance control method for a power transmission line live-line maintenance mechanical device, belongs to the technical field of power detection equipment, and aims to solve the problem that safety accidents are caused by the fact that an existing control method does not fully consider influence of strong wind interference working conditions on movement of the mechanical device. According to the method, the strong wind interference working conditions are accurately divided by establishing a multi-modal dynamic model of a normal working condition model and a plurality of strong wind interference working condition models, model parameters under each working condition are determined in combination with wind tunnel experimental data and kinematics and dynamic characteristics of the mechanical device, behaviors of the mechanical device under different working conditions are accurately described, and the working conditions of the mechanical device are accurately determined. When the mechanical device is interfered by complex and changeable strong wind, a control decision can be made, the accuracy and adaptability of control are greatly improved, the influence of the strong wind on maintenance operation is effectively reduced, and safe interaction between the mechanical device and power transmission line equipment in the maintenance process is guaranteed.

Description

Technical Field

[0001] The invention relates to the technical field of electric power detection equipment, and in particular to an active compliance control method for a live maintenance mechanical device for a power transmission line. Background Art

[0002] In the field of live maintenance of transmission lines, with the continuous development of power systems and the increasing requirements for power supply reliability, live maintenance operations are becoming more frequent and important. Traditional control methods for maintenance mechanical devices have many limitations when facing complex operating environments, especially when encountering strong wind interference.

[0003] The existing control method is based on a single dynamic model and does not fully consider the impact of strong wind interference conditions on the movement of mechanical devices. Strong winds are common near transmission lines, and the changes in their intensity and direction are uncertain, which will cause the mechanical devices to be subjected to unpredictable external forces during the maintenance process. For example, when the robotic arm is performing operations such as replacing insulators or repairing wires, strong winds may cause the end effector of the robotic arm to deviate from the predetermined position, increasing the risk of collision with transmission line equipment. It will also affect the control accuracy of the contact force, which may cause damage to the line equipment and even cause safety accidents. Summary of the invention

[0004] The purpose of the present invention is to provide an active compliant control method for a live transmission line maintenance mechanical device, which solves the problem that the existing control method in the background technology does not fully consider the impact of strong wind interference conditions on the movement of the mechanical device and thus causes safety accidents.

[0005] To achieve the above object, the present invention provides the following technical solution: an active compliance control method for a live maintenance mechanical device for a power transmission line, comprising the following steps:

[0006] S1. Multimodal model construction: Establish a multimodal dynamic model of the mechanical device, including a normal working condition model and multiple strong wind interference working condition models. The strong wind interference working condition model divides the working conditions according to the relationship between the strong wind intensity level and wind direction and the mechanical device;

[0007] S2. Predictive control implementation: Predictive control is performed based on sensor data and the current operating condition model to obtain the optimal control sequence;

[0008] S3. Modal switching execution: Modal switching judgment and execution are performed according to the state of the mechanical device and environmental information to achieve smooth transition control.

[0009] Furthermore, the establishment of the multi-modal dynamic model of the mechanical device in step S1 specifically includes the following steps:

[0010] S11. Determination of normal operating model: Determine the normal operating dynamic equation as follows: Among them, Mn (q)∈R n×n is the inertia matrix under normal working conditions, is the matrix of Coriolis and centrifugal force terms, G n (q)∈R n is the gravity term vector, τ∈R n is the joint torque vector, q∈R n is the joint angle vector, is the joint angular velocity vector; the initial parameter values ​​of each matrix and vector are optimized by calculating the design parameters of the mechanical device and combining the no-load experiment;

[0011] S12. Construction of strong wind interference working condition model: For strong wind interference working conditions, according to the strong wind intensity level s and the relative relationship between wind direction and mechanical device θ, multiple working conditions are divided, such as light wind s 1 , stroke is s 2 , strong wind 3 , the front wind is θ 1 , the side wind is θ 2 , for the i-th strong wind disturbance condition, determine the dynamic equation in and They are the inertia matrix, Coriolis force and centrifugal force matrix, and gravity vector under strong wind conditions, respectively. si,θi ∈R 6 is the wind force vector received by the end effector of the mechanical device under the strong wind condition, including force and torque; the wind force data received by each joint of the mechanical device under different wind speeds v and wind directions θ are obtained through wind tunnel experiments, and the wind force vector calculation formula is fitted. For example, for a certain joint j under a specific strong wind condition, the wind torque is the experimental determination coefficient, A j is the equivalent frontal area of ​​joint j. The relevant parameters are calculated based on the kinematic and dynamic characteristics of the mechanical device under strong wind interference, and are verified and corrected using the experimental data in a simulated strong wind environment.

[0012] Furthermore, the predictive control based on the sensor collected data and the current operating condition model in step S2 specifically includes the following steps:

[0013] S21. Prediction model initialization: In each control cycle k, the position sensor and force sensor installed on the mechanical device collect and filter the joint angle q(k) and angular velocity of the current mechanical device. As well as the contact force F(k) and contact force change rate of the end effector Select the corresponding dynamic model as the basis of the prediction model according to the current working conditions;

[0014] S22. Position and force prediction: Using the current joint angle q(k) and angular velocity And the selected dynamic model, the joint angle sequence in the next N sampling periods is predicted by the fourth-order Runge-Kutta method and the angular velocity series Then, the position prediction sequence of the end effector is obtained through the kinematic model of the robot arm. According to the current contact force F(k) and the contact force change rate And the contact force prediction sequence for the next sampling period under strong wind interference conditions

[0015] S23. Construction and solution of optimization objective function: Set the desired end effector position as x d , the expected contact force is F d , construct the objective function Among them, α, β, γ are weight coefficients, P s,θ (k+i|k) is the uncertainty penalty term based on the strong wind intensity s and wind direction θ. For example, it is determined by the predicted probability of strong wind change according to the Markov chain model. s,θ (k+i|k)=p(s(k+i),θ(k+i)|s(k),θ(k)), where p is the transition probability. The sequential quadratic programming algorithm is used to solve the minimum value of the objective function J and obtain the optimal joint torque control sequence.

[0016] Furthermore, the mode switching judgment and execution according to the state of the mechanical device and the environmental information includes:

[0017] S31. State monitoring and judgment: Real-time monitoring of joint forces T of mechanical devices with high-frequency sampling rate j , the position deviation of the end effector e x =||xx d ||, movement speed v x and acceleration a x State coefficient, as well as the strength s and wind direction θ of the strong wind; the joint force T j Compared with the preset joint force threshold T th Compare wind speed v and wind speed v th Compare and determine whether the wind direction θ meets the specific strong wind direction condition; if max(T j )>T th And v>v th If θ satisfies the specific strong wind direction condition, the mode switching judgment process is triggered;

[0018] S32. Mode switching decision: determine the target switching mode according to the current working condition and strong wind information; if the current working condition is normal and the strong wind interference is detected to meet the conditions of a certain strong wind interference working condition model, determine to switch from the normal mode to the corresponding strong wind interference mode;

[0019] S33. Smooth transition execution: When deciding to change from the current mode m 1 Switch to target mode 2 For the control part, according to the formula Modify the position control gain matrix, where M is the number of transition cycles, is the position control gain matrix in the current forward mode, is the position control gain matrix in the target mode; for the force control part, according to the formula Modify the force control gain matrix K f , where j = 0, 1, …, M-1; during the transition period, the joint torque control input is calculated according to the modified gain matrix to achieve smooth switching control.

[0020] Furthermore, in step S12, the wind force data of each joint of the mechanical device under different wind speeds and wind directions are obtained through a wind tunnel experiment, and the fitting wind vector calculation formula also includes: accurately controlling the wind speed v to change within different levels during the experiment, and adjusting the relative angle between the wind direction θ and each joint of the mechanical device, and measuring the wind force and torque of each joint; using a multivariate linear regression or nonlinear fitting method to determine the coefficients in the wind vector calculation formula according to the measured data Ensure that the formula accurately reflects the relationship between wind force and wind speed, wind direction and joint equivalent windward area.

[0021] Furthermore, the step S23 of using an optimization algorithm to solve the minimum value of the objective function also includes:

[0022] When using the sequential quadratic programming algorithm, reasonable initial iteration point, convergence accuracy and upper limit parameters of iteration times are set; according to the dynamic characteristics and control requirements of the mechanical device, the constraints in the sequential quadratic programming algorithm are accurately set, including joint torque limit and end effector motion range limit, to ensure that the optimal control sequence obtained meets the actual application needs.

[0023] Furthermore, determining the target switching mode according to the current operating condition and strong wind information in step S32 also includes: pre-establishing a database of correspondence between operating conditions and strong wind information, storing typical characteristic parameter ranges under different operating conditions and strong wind conditions; matching real-time monitoring data with the parameter ranges in the database, quickly and accurately determining the target switching mode, and improving the efficiency and accuracy of the mode switching decision.

[0024] The calculation of the joint torque control input according to the modified gain matrix during the transition period in step S33 also includes: during the transition period, real-time monitoring of the motion state and control effect of the mechanical device, and dynamically adjusting the modification parameters of the gain matrix, such as the change rate of λ, according to actual conditions to ensure the best smooth transition effect; recording the control data during the transition process for subsequent analysis and optimization of the transition strategy to improve the stability and reliability of the system during the mode switching process.

[0025] Furthermore, the entire control process also includes real-time monitoring and fault diagnosis of the operating status of each component of the mechanical device, specifically: installing temperature sensors, vibration sensors and other monitoring equipment on the key components of the mechanical device to collect the temperature and vibration operating parameters of the components in real time; establishing a fault diagnosis model to determine whether there are hidden dangers of failure in the components by analyzing the changing trends and characteristics of the operating parameters; if a fault is detected, issuing an alarm in time and taking corresponding protective measures to ensure the safe conduct of live maintenance operations on the transmission line.

[0026] Furthermore, it also includes adaptive adjustment of the parameters in the control method according to the maintenance task requirements and environmental changes. Specifically, when the maintenance task changes from a simple line inspection to a complex component replacement, the weight coefficients α, β, and γ in the predictive control are automatically adjusted to adapt to the changes in the requirements for position control and force control accuracy of different tasks; according to the changes in ambient temperature and humidity factors, the relevant parameters in the strong wind interference working condition model, such as the coefficients in the wind vector calculation formula, are dynamically adjusted. Ensure that the model can accurately reflect the stress conditions of the mechanical device in the actual environment and improve the adaptability and effectiveness of the control method.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] The active compliant control method for live transmission line maintenance mechanical devices provided by the present invention establishes a multimodal dynamic model including normal operating conditions and multiple strong wind interference operating condition models, accurately divides the strong wind interference operating conditions, and determines the model parameters under each operating condition by combining wind tunnel test data and the kinematic and dynamic characteristics of the mechanical device. This method can accurately describe the behavior of the mechanical device under different operating conditions, so that the mechanical device can make control decisions based on a suitable model when facing complex and changeable strong wind interference, greatly improving the accuracy and adaptability of the control, effectively reducing the impact of strong winds on maintenance operations, and ensuring the safe interaction between the mechanical device and the transmission line equipment during the maintenance process. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the overall process of the present invention;

[0030] Figure 2 It is a detailed schematic diagram of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] In order to solve the technical problem that the existing control method does not fully consider the impact of strong wind interference conditions on the movement of mechanical devices and thus cause safety accidents, such as Figure 1-Figure 2 As shown, the following preferred technical solutions are provided:

[0033] The active compliance control method of a live maintenance mechanical device for a power transmission line comprises the following steps:

[0034] S1. Establish a multi-modal dynamic model of the mechanical device, including a normal working condition model and multiple strong wind interference working condition models. The strong wind interference working condition model divides the working conditions according to the relationship between the strong wind intensity level and wind direction and the mechanical device;

[0035] S2. Perform predictive control based on sensor data and the current operating condition model to obtain the optimal control sequence;

[0036] S3. Perform mode switching judgment and execution according to the state of the mechanical device and environmental information to achieve smooth transition control.

[0037] The step S1 of establishing the multi-modal dynamic model of the mechanical device specifically includes the following steps:

[0038] S11. Determination of normal operating model: Determine the normal operating dynamic equation as follows: Among them, M n (q)∈R n×n is the inertia matrix under normal working conditions, is the matrix of Coriolis and centrifugal force terms, G n (q)∈R n is the gravity term vector, τ∈R n is the joint torque vector, q∈R n is the joint angle vector, is the joint angular velocity vector; the initial parameter values ​​of each matrix and vector are optimized by calculating the design parameters of the mechanical device and combining the no-load experiment;

[0039] S12. Construction of strong wind interference working condition model: For strong wind interference working conditions, according to the strong wind intensity level s and the relative relationship between wind direction and mechanical device θ, multiple working conditions are divided, such as light wind s 1 , stroke is s 2, strong wind 3 , the front wind is θ 1 , the side wind is θ 2 , for the i-th strong wind disturbance condition, determine the dynamic equation in and They are the inertia matrix, Coriolis force and centrifugal force matrix, and gravity vector under strong wind conditions, respectively. si,θi ∈R 6 is the wind force vector received by the end effector of the mechanical device under the strong wind condition, including force and torque; the wind force data received by each joint of the mechanical device under different wind speeds v and wind directions θ are obtained through wind tunnel experiments, and the wind force vector calculation formula is fitted. For example, for a certain joint j under a specific strong wind condition, the wind torque is the experimental determination coefficient, A j is the equivalent frontal area of ​​joint j. The relevant parameters are calculated based on the kinematic and dynamic characteristics of the mechanical device under strong wind interference, and are verified and corrected using the experimental data in a simulated strong wind environment.

[0040] The predictive control based on the sensor collected data and the current operating condition model in step S2 specifically includes the following steps:

[0041] S21. Prediction model initialization: In each control cycle k, the position sensor and force sensor installed on the mechanical device collect and filter the joint angle q(k) and angular velocity of the current mechanical device. As well as the contact force F(k) and contact force change rate of the end effector Select the corresponding dynamic model as the basis of the prediction model according to the current working conditions;

[0042] S22. Position and force prediction: Using the current joint angle q(k) and angular velocity And the selected dynamic model, the joint angle sequence in the next N sampling periods is predicted by the fourth-order Runge-Kutta method and the angular velocity series Then, the position prediction sequence of the end effector is obtained through the kinematic model of the robot arm. According to the current contact force F(k) and the contact force change rate And the contact force prediction sequence for the next sampling period under strong wind interference conditions

[0043] S23. Construction and solution of optimization objective function: Set the desired end effector position as x d , the expected contact force is F d , construct the objective function Among them, α, β, γ are weight coefficients, P s,θ(k+i|k) is the uncertainty penalty term based on the strong wind intensity s and wind direction θ. For example, it is determined by the predicted probability of strong wind change according to the Markov chain model. s,θ (k+i|k)=p(s(k+i),θ(k+i)s(k),θ(k)), where p is the transition probability. The sequential quadratic programming algorithm is used to solve the minimum value of the objective function J and obtain the optimal joint torque control sequence.

[0044] The mode switching judgment and execution based on the mechanical device status and environmental information include:

[0045] S31. State monitoring and judgment: Real-time monitoring of joint forces T of mechanical devices with high-frequency sampling rate j , the position deviation of the end effector e x =||xx d ||, movement speed v x and acceleration a x State coefficient, as well as the strength s and wind direction θ of the strong wind; the joint force T j Compared with the preset joint force threshold T th Compare wind speed v and wind speed v th Compare and determine whether the wind direction θ meets the specific strong wind direction condition; if max(T j )>T th And v>v th If θ satisfies the specific strong wind direction condition, the mode switching judgment process is triggered;

[0046] S32. Mode switching decision: determine the target switching mode according to the current working condition and strong wind information; if the current working condition is normal and the strong wind interference is detected to meet the conditions of a certain strong wind interference working condition model, determine to switch from the normal mode to the corresponding strong wind interference mode;

[0047] S33. Smooth transition execution: When deciding to change from the current mode m 1 Switch to target mode 2 For the control part, according to the formula Modify the position control gain matrix, where M is the number of transition cycles, is the position control gain matrix in the current forward mode, is the position control gain matrix in the target mode; for the force control part, according to the formula Modify the force control gain matrix K f , where j = 0, 1, …, M-1; during the transition period, the joint torque control input is calculated according to the modified gain matrix to achieve smooth switching control.

[0048] In step S12, wind force data of each joint of the mechanical device under different wind speeds and wind directions are obtained through wind tunnel experiments. The wind force vector calculation formula is fitted, and the following steps are further included: accurately controlling the wind speed v to change within different levels during the experiment, and adjusting the relative angle between the wind direction θ and each joint of the mechanical device, and measuring the wind force and torque of each joint; using multiple linear regression or nonlinear fitting method to determine the coefficients in the wind vector calculation formula according to the measured data. Ensure that the formula accurately reflects the relationship between wind force and wind speed, wind direction and joint equivalent windward area.

[0049] The use of an optimization algorithm to solve the minimum value of the objective function in step S23 also includes: when using the sequential quadratic programming algorithm, setting reasonable initial iteration points, convergence accuracy and upper limit parameters of the number of iterations; according to the dynamic characteristics and control requirements of the mechanical device, accurately setting the constraints in the sequential quadratic programming algorithm, including joint torque limits and end effector motion range limits, to ensure that the optimal control sequence obtained by solving meets the actual application requirements.

[0050] Determining the target switching mode according to the current operating condition and strong wind information in step S32 also includes: pre-establishing a database of corresponding relationships between operating conditions and strong wind information, storing typical characteristic parameter ranges under different operating conditions and strong wind conditions; matching real-time monitoring data with the parameter ranges in the database to quickly and accurately determine the target switching mode, thereby improving the efficiency and accuracy of the mode switching decision.

[0051] Calculating the joint torque control input according to the modified gain matrix during the transition period in step S33 also includes: monitoring the motion state and control effect of the mechanical device in real time during the transition period, dynamically adjusting the modification parameters of the gain matrix, such as the rate of change of λ, according to actual conditions to ensure the best smooth transition effect; recording the control data during the transition process for subsequent analysis and optimization of the transition strategy to improve the stability and reliability of the system during the mode switching process.

[0052] The entire control process also includes real-time monitoring and fault diagnosis of the operating status of each component of the mechanical device. Specifically, temperature sensors, vibration sensors and other monitoring equipment are installed on the key components of the mechanical device to collect the temperature and vibration operating parameters of the components in real time; a fault diagnosis model is established to determine whether there are hidden dangers of failure in the components by analyzing the changing trends and characteristics of the operating parameters; if a fault is detected, an alarm is issued in time and corresponding protective measures are taken to ensure the safe conduct of live maintenance operations on the transmission line.

[0053] Adaptively adjust the parameters in the control method according to maintenance task requirements and environmental changes. Specifically, when the maintenance task changes from simple line inspection to complex component replacement, automatically adjust the weight coefficients α, β, and γ in the predictive control to adapt to the changes in the requirements for position control and force control accuracy of different tasks; dynamically adjust the relevant parameters in the strong wind interference working condition model according to changes in ambient temperature and humidity factors, such as the coefficients in the wind vector calculation formula Ensure that the model can accurately reflect the stress conditions of the mechanical device in the actual environment and improve the adaptability and effectiveness of the control method.

[0054] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0055] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An active compliance control method for a live maintenance mechanical device for a power transmission line, characterized in that: The following steps are involved: S1. Multimodal model construction: Establish a multimodal dynamic model of the mechanical device, including a normal working condition model and multiple strong wind interference working condition models. The strong wind interference working condition model divides the working conditions according to the relationship between the strong wind intensity level and wind direction and the mechanical device; S2. Predictive control implementation: Predictive control is performed based on sensor data and the current operating condition model to obtain the optimal control sequence; S3. Modal switching execution: Modal switching judgment and execution are performed according to the state of the mechanical device and environmental information to achieve smooth transition control.

2. The active compliance control method for a live transmission line maintenance mechanical device according to claim 1, characterized in that: The step S1 of establishing the multi-modal dynamic model of the mechanical device specifically includes the following steps: S11. Determination of normal operating model: Determine the normal operating dynamic equation as follows: Among them, M n (q)∈R n×n is the inertia matrix under normal working conditions, is the matrix of Coriolis and centrifugal force terms, G n (q)∈R n is the gravity term vector, τ∈R n is the joint torque vector, q∈R n is the joint angle vector, is the joint angular velocity vector; the initial parameter values ​​of each matrix and vector are optimized by calculating the design parameters of the mechanical device and combining the no-load experiment; S12. Construction of strong wind interference working condition model: For strong wind interference working conditions, multiple working conditions are divided according to the strong wind intensity level s and the relative relationship between wind direction and mechanical device θ. For the i-th strong wind interference working condition, the dynamic equation is determined in and They are the inertia matrix, Coriolis force and centrifugal force matrix, and gravity vector under strong wind conditions, respectively. si,θi ∈R 6 It is the wind force vector received by the end effector of the mechanical device under the strong wind condition, including force and torque. The wind force data received by each joint of the mechanical device under different wind speeds v and wind directions θ are obtained through wind tunnel experiments, and the wind force vector calculation formula is fitted. The relevant parameters are calculated in combination with the kinematic and dynamic characteristics of the mechanical device under strong wind interference, and then verified and corrected using the experimental data in a simulated strong wind environment.

3. The active compliance control method for a live transmission line maintenance mechanical device according to claim 1, characterized in that: The predictive control based on the sensor collected data and the current operating condition model in step S2 specifically includes the following steps: S21. Prediction model initialization: In each control cycle k, the position sensor and force sensor installed on the mechanical device collect and filter the joint angle q(k) and angular velocity of the current mechanical device. As well as the contact force F(k) and contact force change rate of the end effector Select the corresponding dynamic model as the basis of the prediction model according to the current working conditions; S22. Position and force prediction: Using the current joint angle q(k) and angular velocity And the selected dynamic model, the joint angle sequence in the next N sampling periods is predicted by the fourth-order Runge-Kutta method and the angular velocity series Then, the position prediction sequence of the end effector is obtained through the kinematic model of the robot arm. According to the current contact force F(k) and the contact force change rate And the contact force prediction sequence for the next sampling period under strong wind interference conditions S23. Construction and solution of optimization objective function: Set the desired end effector position as x d , the expected contact force is F d , construct the objective function Among them, α, β, γ are weight coefficients, P s,θ (k+ik) is the uncertainty penalty term based on the strong wind intensity s and wind direction θ. The sequential quadratic programming algorithm is used to solve the minimum value of the objective function J to obtain the optimal joint torque control sequence 4. The active compliance control method for live transmission line maintenance mechanical device according to claim 1, characterized in that: The mode switching judgment and execution according to the state of the mechanical device and the environmental information includes: S31. State monitoring and judgment: Real-time monitoring of joint forces T of mechanical devices with high-frequency sampling rate j , the position deviation of the end effector e x =||xx d ||, movement speed v x and acceleration a x State coefficient, as well as the strength s and wind direction θ of the strong wind; the joint force T j Compared with the preset joint force threshold T th Compare wind speed v and wind speed v th Compare and determine whether the wind direction θ meets the specific strong wind direction condition; if max(T j )>T th And v>v th If θ satisfies the specific strong wind direction condition, the mode switching judgment process is triggered; S32. Mode switching decision: determine the target switching mode according to the current working condition and strong wind information; if the current working condition is normal and the strong wind interference is detected to meet the conditions of a certain strong wind interference working condition model, determine to switch from the normal mode to the corresponding strong wind interference mode; S33. Smooth transition execution: When it is decided to switch from the current mode m1 to the target mode m2, for the control part, according to the formula Modify the position control gain matrix, where j = 0, 1, ..., M-1, M is the number of transition cycles, is the position control gain matrix in the current forward mode, is the position control gain matrix in the target mode; for the force control part, according to the formula Modify the force control gain matrix K f , where j = 0, 1, …, M-1; during the transition period, the joint torque control input is calculated according to the modified gain matrix to achieve smooth switching control.

5. The active compliance control method for a live transmission line maintenance mechanical device according to claim 2, characterized in that: In step S12, wind tunnel experiments are performed to obtain wind force data on each joint of the mechanical device at different wind speeds and wind speeds, and the calculation formula for the fitted wind force vector also includes: During the experiment, the wind speed v was precisely controlled to change within different levels, and the relative angles between the wind direction θ and each joint of the mechanical device were adjusted to measure the wind force and torque on each joint. Use multiple linear regression or nonlinear fitting method to determine the coefficient k in the wind vector calculation formula based on the measured data si,θi , ensuring that the formula can accurately reflect the relationship between wind force and wind speed, wind direction and joint equivalent windward area.

6. The active compliance control method for live transmission line maintenance mechanical device according to claim 3, characterized in that: The step S23 in which the optimization algorithm is used to solve the minimum value of the objective function also includes: When using the sequential quadratic programming algorithm, set reasonable initial iteration point, convergence accuracy and upper limit parameters of iteration times; According to the dynamic characteristics and control requirements of the mechanical device, the constraints in the sequential quadratic programming algorithm are precisely set, including joint torque limitations and end effector motion range limitations.

7. The active compliance control method for a live transmission line maintenance mechanical device according to claim 4, characterized in that: Determining the target switching mode according to the current working condition and the strong wind information in step S32 also includes: A database of corresponding relations between working conditions and strong wind information is established in advance to store the typical characteristic parameter ranges under different working conditions and strong wind conditions; Real-time monitoring data is matched with parameter ranges in the database.

8. The active compliance control method for a live transmission line maintenance mechanical device according to claim 4, characterized in that: The calculation of the joint torque control input according to the modified gain matrix during the transition period in step S33 also includes: During the transition period, the motion state and control effect of the mechanical device are monitored in real time, and the modification parameters of the gain matrix are dynamically adjusted according to the actual situation to ensure the best smooth transition effect; Record control data during the transition process.

9. The active compliance control method for a live transmission line maintenance mechanical device according to claim 1, characterized in that: The entire control process also includes real-time monitoring and fault diagnosis of the operating status of each component of the mechanical device, specifically: Install temperature sensors, vibration sensors and other monitoring equipment on key components of mechanical devices to collect temperature and vibration operating parameters of components in real time; Establish a fault diagnosis model to determine whether there are hidden faults in components by analyzing the changing trends and characteristics of operating parameters; if a fault is detected, issue an alarm in time and take corresponding protective measures.

10. The active compliance control method for a live transmission line maintenance mechanical device according to claim 9, characterized in that: It also includes adaptive adjustment of the parameters in the control method according to maintenance task requirements and environmental changes, specifically: When the maintenance task changes from simple line inspection to complex component replacement, the weight coefficients α, β, and γ in the predictive control are automatically adjusted; According to the changes in ambient temperature and humidity factors, the relevant parameters in the strong wind interference working condition model are dynamically adjusted.

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