Control method and system applied to mechanical arm of oil sample collection robot of converter station

Abstract

The invention provides a control method and system applied to a mechanical arm of a converter station oil sample collection robot. The method comprises the following steps: constructing a kinematics model, a dynamics model and a three-order cascade motor system model; designing a non-matching composite disturbance filter; designing a switching type preset finite time time-varying convergence function, and constructing a first control signal generation function according to the switching type preset finite time time-varying convergence function; designing a state constraint function based on a dynamic boundary, calculating to obtain a dynamic boundary parameter through a pole assignment method, and substituting the dynamic boundary parameter into the state constraint function to obtain the dynamic boundary; and according to the non-matching composite disturbance filtering function and the dynamic boundary, expected convergence is conducted on the first control signal generation function, a second control signal generation function is obtained, and a control signal of the mechanical arm is obtained according to the second control signal generation function. The device can meet the requirements of high-precision, stable and safe oil sample collection operation in the complex environment of the converter station.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of robot control, and in particular to a control method and system applied to a robot mechanical arm for oil sample collection in a converter station. BACKGROUND

[0002] As a key facility of a power system, the insulation oil state of equipment (such as a transformer and a reactor) in a converter station directly affects the safe operation of the system, and thus oil samples need to be collected regularly for detection. Traditional oil sample collection relies on manual operation, and thus has problems such as low efficiency, poor safety (high-voltage environment), and insufficient sampling precision (manual shaking). Therefore, it has become a trend to use a robot mechanical arm to realize automatic oil sample collection.

[0003] To meet the requirements of fast response and controllable time of a mechanical arm, existing technologies propose methods such as finite-time control, fixed-time control, and preset-time control. Finite-time control uses strategies such as a terminal sliding mode surface to make the system converge in finite time, but the convergence time depends on the initial posture and parameters, and it is difficult to ensure time controllability when the initial state varies. Fixed-time control makes the convergence time independent of the initial state by introducing odd-order terms, but the estimated stable time is usually large and is restricted by parameters, and it is difficult to match the industrial beat. The preset-time control allows the upper limit of the preset convergence time, but the control input fluctuates sharply when the preset time is approached, and the robustness decreases after the preset time, which makes it difficult to meet the continuous operation requirements. In terms of disturbance rejection, the traditional disturbance observer relies on the assumption that the disturbance derivative is bounded, and has poor effect on sudden impulse disturbances. The extended state observer has limited ability to handle non-matching disturbances. When adaptive control and intelligent algorithms are used to handle complex disturbances, the estimation accuracy is insufficient or the calculation complexity is high. In terms of state constraint control, the barrier Lyapunov function causes the control signal to fluctuate when the state approaches the boundary and is easy to break through the constraint due to sudden disturbances. The preset performance control has a fixed performance function, and is easy to fail under strong disturbance. The model predictive control has heavy calculation burden and poor real-time performance, and is difficult to use in high-speed operation scenarios. SUMMARY

[0004] The present application aims to provide a control method and system applied to a robot mechanical arm for oil sample collection in a converter station, and aims to solve the problems of insufficient controllability of control time, limited ability to suppress complex disturbances, and contradiction between state constraint and robustness in traditional technologies.

[0005] In a first aspect, the present application provides a control method applied to a robot mechanical arm for oil sample collection in a converter station, which comprises: successively constructing a kinematic model, a dynamic model, and a three-order cascaded motor system model of the robot mechanical arm for oil sample collection in the converter station; designing a non-matching complex disturbance filter according to the three-order cascaded motor system model; The switching preset finite time time-varying convergence function is designed, and a first control signal generation function is constructed according to the switching preset finite time time-varying convergence function; A state constraint function based on a dynamic boundary is designed, and a dynamic boundary parameter is calculated by a pole placement method, so as to substitute the dynamic boundary parameter into the state constraint function to obtain the dynamic boundary. The first control signal generation function is expected to converge according to the non-matching composite disturbance filter and the dynamic boundary, to obtain a second control signal generation function, and a control signal of the mechanical arm is obtained according to the second control signal generation function.

[0006] In some embodiments, the step of sequentially constructing the kinematics model, the dynamics model and the third-order cascaded motor system model of the converter station oil sample collection robot mechanical arm comprises: The kinematics model is constructed according to the following formula: ; Wherein, is a homogeneous transformation matrix, is the angle of the i th joint of the mechanical arm, is the torsion angle of the i th link of the mechanical arm, representing the included angle between the z axes (joint axes) in the coordinate systems of adjacent two links, is the offset distance of the i th link of the mechanical arm, representing the distance between the x axes in the coordinate systems of adjacent two links; The dynamics model is constructed according to the following formula: ; Wherein, is a vector of link joint angles, is an inertia matrix dependent on joint angles, is the sum of Coriolis force and centrifugal force, and are friction force vector and gravity torque vector respectively, is joint torque, is external force disturbance applied to the torque of the mechanical arm, is a vector of link joint angular acceleration, which is the second derivative of q with respect to time; The third-order cascaded motor system model is constructed according to the following formula: ; Wherein, is the torque constant of the motor, is the armature current of the motor, is the derivative of the armature current with respect to time, i.e. the current change rate, is the armature inductance, is the back electromotive force constant of the motor, This is the derivative of the motor's rotation angle with respect to time, i.e., the motor's angular velocity. For input control voltage, External environmental interference with voltage This is the circuit resistance.

[0007] In some embodiments, the step of designing an unmatched composite disturbance filter based on the three-stage cascaded motor system model includes: make , , , , The three-stage multistage motor system model then transforms into: ; Specifically, , , , , , , ; in, Let be the angular velocity of the robotic arm joint. The angular acceleration of the robotic arm joints, This is the product of the motor torque constant and the inverse of the inertia matrix, reflecting the coupling relationship between the motor torque constant and the inertial characteristics of the robotic arm. It is the reciprocal of the armature inductance, reflecting the dynamic response characteristics of the motor's electrical system to current. This is a nonlinear force term related to joint angular velocity, which comprehensively reflects the combined effects of nonlinear factors such as Coriolis force, centrifugal force, friction, and gravity on joint movement. This is a damping term in the electrical system related to the armature current, affecting the dynamic changes of the current. For the equivalent disturbance term related to external forces, For the equivalent disturbance term related to voltage interference, The inverse of the robotic arm's inertia matrix. The inertia matrix, The terms represent the Coriolis force and centrifugal force, reflecting the nonlinear coupling characteristics between joint movements. The friction term describes the frictional resistance experienced by the robotic arm's joints during movement. This is a gravity term, generated by the gravity of each link in the robotic arm; Design an unmatched composite perturbation filter based on the following formula: ; in, For the internal state of the observer Time derivative, a gain matrix for the observer, an internal state variable for the observer, a nonlinear function related to the state of the robot arm, an estimate of the disturbance, a further gain matrix, an internal state of the observer a time derivative of the internal state of the observer, a further internal state variable of the observer, an auxiliary more accurate tracking and estimation of the disturbance, a nonlinear function related to the state of the robot arm, an intermediate variable, a final disturbance compensation signal, a gain matrix converting the intermediate variable into a signal for compensating the disturbance of the robot arm.

[0008] In some embodiments, the step of designing the switching preset finite time time-varying convergence function and constructing the first control signal generation function according to the switching preset finite time time-varying convergence function comprises: designing the switching preset finite time time-varying convergence function according to the following formula: ; wherein, is the switching preset finite time time-varying convergence function, is a positive constant, is a preset time, and ; let when , , and when , ; wherein, is a dynamic characteristic of the adjusting system, is a core parameter of the system, is an internal state variable of the disturbance observer, is a derivative of the core parameter of the system, is a maximum allowable value.

[0009] In some embodiments, the method further comprises: in a phase, performing a time-varying transformation to define wherein, , ; constructing a first Lyapunov candidate function deriving a derivative thereof And the Young inequality is used to deal with the design of virtual control signal wherein, , , are all normal numbers, and I is the unit matrix. The second Lyapunov candidate function is constructed According to the derivative of , the first control signal generation function is constructed: ; wherein, , are all normal numbers. wherein, is the state variable after time-varying transformation in the first stage, is the state variable after time-varying transformation in the second stage, is 2+m, is 1+m, is the virtual control input designed subsequently, m>0 is an integer, is the gain matrix, is the control signal, is the nonlinear characteristic function of the system, is the derivative of the virtual control input designed subsequently, is the coefficient matrix of the actual controller, is the coefficient matrix of the virtual control signal, is the gain matrix. In the stage, the time-varying transformation is simplified as wherein, , ; the virtual control signal is designed as wherein are all normal numbers, and the first control signal generation function is: ; wherein, are all normal numbers, is the gain matrix, is the gain matrix, means that it is a diagonal matrix.

[0010] In some embodiments, the step of designing the state constraint function based on the dynamic boundary comprises: The state constraint function is designed according to the following formula: ; wherein, is the output variable of the state constraint function, which quantifies the satisfaction degree of the constraint of the system state (such as joint angle, angular velocity). is the third coefficient (a constant) of the state constraint function, increasing can avoid the state from exceeding the constraint due to excessive acceleration, is the second coefficient (a constant) of the state constraint function, increasing can enhance the inhibition of the state from deviating from the constraint, is the first coefficient (a constant) of the state constraint function, increasing can enhance the inhibition of and speed up the convergence to the negative interval, is the disturbance compensation coefficient (a constant) in the state constraint function, compensating for the influence of system disturbance on the state constraint, is the deviation variable in the state constraint function, describing the deviation of the actual state from the constraint boundary, is the constant term (a constant) of the state constraint function, setting the reference threshold of the constraint function, is the output variable of the state constraint function, is the first intermediate variable of the state constraint function, obtained by the first-order derivative of the state constraint function and the state deviation, so that the constraint function can accurately respond to the influence of the state deviation on the constraint, avoiding constraint lag, is the second intermediate variable of the state constraint function, obtained by the first-order derivative of the state constraint function and the derivative of the state deviation, further improving the response accuracy of the constraint function to the dynamic characteristics of the state, ensuring that the state meets the constraint in the dynamic process.

[0011] In some embodiments, the step of calculating the dynamic boundary parameter by the pole configuration method comprises: defining a time constant wherein is the ideal regulation time of selected according to actual needs; the dominant pole , the non-dominant pole and wherein, and are both constants not less than 5, is the dominant pole, is the non-dominant pole, is the non-dominant pole; obtaining the real characteristic polynomial from the filter system matrix, and making it equal to the corresponding expected characteristic polynomial, thereby determining the values of , , .

[0012] In some embodiments, the step of performing expected convergence on the first control signal generation function according to the non-matching compound disturbance filter and the dynamic boundary to obtain a second control signal generation function, and obtaining a control signal of the robot arm according to the second control signal generation function comprises: Definition , wherein, is a desired joint angle, is a virtual control input, is an actual joint angle; The second control signal generation function is obtained according to the following formula: ; wherein, is the k-th derivative of the desired joint angle, is a tracking error of the system or a disturbance observer internal state rate of change, is a tracking error of the system or a disturbance observer internal state, and k is an order.

[0013] In a second aspect, the present application provides a control system applied to a converter station oil sample collection robot arm, the system comprising: a model construction module for sequentially constructing a kinematics model, a dynamics model and a three-order cascade motor system model of the converter station oil sample collection robot arm; a filter design module for designing a non-matching compound disturbance filter according to the three-order cascade motor system model; a control signal generation module for designing a switching type preset finite time time-varying convergence function, and constructing a first control signal generation function according to the switching type preset finite time time-varying convergence function; a dynamic boundary calculation module for designing a state constraint function based on a dynamic boundary, and calculating a dynamic boundary parameter by a pole placement method to obtain a dynamic boundary by substituting the dynamic boundary parameter into the state constraint function; a control signal output module for performing expected convergence on the first control signal generation function according to the non-matching compound disturbance filter and the dynamic boundary to obtain a second control signal generation function, and obtaining a control signal of the robot arm according to the second control signal generation function.

[0014] In a third aspect, the present application provides a storage medium, the storage medium storing one or more programs, the programs being executed by a processor to implement the above-mentioned control method applied to a converter station oil sample collection robot arm.

[0015] In a fourth aspect, the present application provides an electronic device, the electronic device comprising a memory and a processor, wherein: The memory is used to store a computer program; The processor is used to execute the computer program stored on the memory to realize the control method applied to the robot mechanical arm for oil sample collection in a converter station.

[0016] Compared with the prior art, the application has the following advantages: 1. Synergistic optimization of time control accuracy and stability: The application adopts a switching type preset time varying convergence function, so that the convergence time of the mechanical arm is completely controllable (not affected by the initial state and parameters), and the system robustness is maintained through a smooth switching mechanism after the preset time, solving the contradiction between "insufficient time accuracy" and "weak anti-disturbance ability" in traditional preset time control.

[0017] 2. Accurate suppression of complex disturbance: The application designs a non-matching complex disturbance observer, which does not rely on the "bounded disturbance derivative" assumption, can estimate additive disturbance, state-coupled implicit disturbance and unknown multiplicative coefficient disturbance at the same time, and realizes asymptotic tracking combined with adaptive compensation technology, significantly improving the suppression ability of complex environmental disturbance.

[0018] 3. Flexible constraint of dynamic boundary: The application introduces a dynamic boundary filter based on pole placement, so that the state constraint boundary can be expanded or converged in real time according to the disturbance, which not only ensures that the mechanical arm movement does not exceed the boundary, but also avoids the problem of "incompatible constraint and robustness" in traditional fixed boundary control, improving the safety of the system under strong disturbance.

[0019] In summary, the application synergistically improves the time controllability, anti-disturbance accuracy and constraint robustness of the existing mechanical arm control method by integrating the three core technologies of "preset time switching control", "non-matching disturbance observation" and "dynamic boundary constraint", and is more suitable for complex scenes of robots. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 The flowchart of the control method applied to the robot mechanical arm for oil sample collection in a converter station is provided for an embodiment of the application; Figure 2 The schematic diagram of the non-matching disturbance estimation effect of the mechanical arm is provided; Figure 3 The schematic diagram of the adaptive law is provided; Figure 4 The schematic diagram of the joint angle tracking performance of the mechanical arm is provided; Figure 5 The structural schematic diagram of the control system applied to the robot mechanical arm for oil sample collection in a converter station is provided for an embodiment of the application.

[0021] The following specific embodiments will further illustrate the application in combination with the above drawings. DETAILED DESCRIPTION

[0022] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below. Obviously, the described embodiments are only some, but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application. Unless otherwise defined, the technical terms or scientific terms used herein should be understood as the common meanings thereof by those of ordinary skill in the art. The terms such as "comprise" and the like used herein are intended to cover the elements or objects recited in the terms and their equivalents, and do not exclude other elements or objects.

[0023] As shown in Figure 1 An embodiment of the present application provides a control method applied to a robot mechanical arm of an oil sample collection machine in a converter station, and the method comprises the following steps S101-S105. Step S101: sequentially constructing a kinematics model, a dynamics model and a third-order cascade motor system model of the robot mechanical arm of the oil sample collection machine in the converter station. It should be noted that in this step, the kinematics modeling of the robot mechanical arm of the oil sample collection machine in the converter station is performed by using a standard D-H parameter method. According to the structure of the mechanical arm, a coordinate system is established for each joint, and the D-H parameters of each joint are determined, and then the kinematics model is constructed according to the following formula: wherein, is a homogeneous transformation matrix, is an angle of the i th joint of the mechanical arm, is a torsion angle of the i th link of the mechanical arm, representing an included angle between z axes in coordinate systems of adjacent two links, is a offset distance of the i th link of the mechanical arm, representing a distance between x axes in coordinate systems of adjacent two links.

[0024] In addition, in some embodiments, the dynamics model of the mechanical arm is derived based on Newton-Euler principle. From the base end to the tool end, the velocity and acceleration at the center of mass of each link are determined in sequence by using the extrapolation method, and the inertia force and inertia torque acting on the center of mass are determined based on the Newton-Euler method. For example, the angular velocity vector of the link i is wherein, ; the angular acceleration vector of the link i is ​​etc. Then, from the tool end to the base end, the components of the joint torques about the axes (i.e. joint torques) are determined in sequence based on the transformation of forces and torques and the balance of forces and torques at the joints and the inertial forces and torques at the center of mass. Finally, the dynamics equation of the manipulator is obtained: ; where, is the vector of the joint angles of the links, is the inertia matrix dependent on the joint angles, is the sum of the Coriolis force and the centrifugal force, and are the friction force vector and the gravity torque vector respectively, is the joint torque, is the external force disturbance applied on the torque of the manipulator, is the vector of the joint angle acceleration of the links, and is the second derivative of q with respect to time.

[0025] Considering the influence of motor voltage disturbance on the motion accuracy of the manipulator, the motor model is introduced to expand the dynamics model of the manipulator into a third-order cascaded motor system model: ; where, is the torque constant of the motor, is the motor armature current, is the derivative of the armature current with respect to time, i.e. the current change rate, is the armature inductance, is the back electromotive force constant of the motor, is the derivative of the motor rotation angle with respect to time, i.e. the motor angular velocity, is the input control voltage, is the disturbance of the voltage from the external environment, is the circuit resistance.

[0026] Step S102: design a non-matching compound disturbance filter according to the third-order cascaded motor system model; It should be noted that let , , , , , the third-order cascaded motor system model is converted into: ; where, and are unknown constants, is a known constant, , is a known smooth function, and respectively represent the unmatched disturbance and the matched disturbance.

[0027] wherein, is the angular velocity of the joint of the robot arm, is the angular acceleration of the joint of the robot arm, is the product of the motor torque constant and the inverse of the inertia matrix, reflecting the coupling relationship between the motor torque constant and the inertia characteristics of the robot arm, is the reciprocal of the armature inductance, reflecting the dynamic response characteristics of the motor electrical system to the current, is the nonlinear force term related to the joint angular velocity, which comprehensively reflects the effects of the Coriolis force, centrifugal force, friction, gravity and other nonlinear factors on the joint motion, is the electrical system damping term related to the armature current, which affects the dynamic change of the current, is the equivalent disturbance term related to the external force disturbance, is the equivalent disturbance term related to the voltage disturbance, is the inverse of the inertia matrix of the robot arm, is the inertia matrix, is the Coriolis force and centrifugal force term, which reflects the coupling nonlinear characteristics between the joint motions, is the friction force term, which describes the frictional resistance when the robot arm moves, is the gravity term, which is generated by the gravity of each link of the robot arm.

[0028] Aiming at the various complex disturbances faced by the robot arm in the converter station, a non-matched compound disturbance observer is designed. The non-matched compound disturbance wherein, satisfies , is a known constant vector, is unmeasurable, is a known matrix, is a known function, is an unknown parameter vector, and m and x are unknown constants. Assuming that is input, the system is stable in the sense of input-state dynamics, and the observability of (X, Y) can be guaranteed. Since (X, Y) is observable, there exists a constant vector such that is a Hurwitz matrix. Introduce a positive definite symmetric matrix satisfying wherein represents an m-order unit matrix. Design a filter for estimating : ; wherein, is the internal state of the observer Time derivative, Here is the gain matrix of the observer. For the internal state variables of the observer, A nonlinear function related to the state of the robotic arm. This is an estimate of the disturbance. For another gain matrix, For the internal state of the observer Time derivative, For another internal state variable of the observer, auxiliary To track and estimate disturbances more accurately. A nonlinear function related to the state of the robotic arm. As an intermediate variable, For the final disturbance compensation signal, For the gain matrix, the intermediate variables are... It is converted into a signal used to compensate for disturbances in the robotic arm.

[0029] This observer can estimate the mismatched composite disturbances in the robotic arm system in real time, providing an accurate basis for subsequent control compensation.

[0030] Step S103: Design a switching preset finite-time time-varying convergence function, and construct a first control signal generation function based on the switching preset finite-time time-varying convergence function; In this step, to meet the strict time requirements of the converter station oil sampling task, a switching preset finite-time time-varying convergence function is designed: ; in, For switching, a preset finite-time time-varying convergence function is used. For positive integers, For preset time, and ; make ,when hour, ,and ;when hour, ; in, To adjust the dynamic characteristics of the system, These are the core parameters of the system. For the internal state variables of the perturbation observer, The derivatives of the system's core parameters. This is the maximum allowed value.

[0031] Furthermore, in some embodiments, a switching preset finite-time control method is designed in two time stages for robotic arm joint angle control. Phase, time-varying transformation is performed, defining wherein, , , is the virtual control input for subsequent design, L_{1}=2+m, L_{2}=1+m, m>0 is an integer; and then construct the first Lyapunov candidate function , derive its derivative , and use Young's inequality to process, design the virtual control signal wherein, , , are all normal numbers, and I is the unit matrix; construct the second Lyapunov candidate function , according to the derivative of the first control signal generation function is constructed: ; wherein, , are all normal numbers; wherein, is the state variable after time-varying transformation in the first phase, is the state variable after time-varying transformation in the second phase, is 2+m, is 1+m, is the virtual control input for subsequent design, m>0 is an integer, is the gain matrix, is the control signal, is the nonlinear characteristic function of the system, is the derivative of the virtual control input for subsequent design, is the coefficient matrix of the actual controller, is the coefficient matrix of the virtual control signal, is the gain matrix.

[0032] In the first phase, the time-varying transformation is simplified as wherein, , ; design the virtual control signal wherein are all normal numbers, and the first control signal generation function is: ; wherein, are all normal numbers, is the gain matrix, is the gain matrix, means that it is a diagonal matrix.

[0033] Through stability analysis, it can be proved that the switching preset finite time control method can ensure that all states in the closed-loop system converge to the desired state within the preset finite time, and the convergence process does not depend on the initial conditions and control parameters, and the system can continue to maintain stability after the preset time T, avoiding the common chattering phenomenon in traditional switching controllers.

[0034] Step S104: design a state constraint function based on a dynamic boundary, and calculate the dynamic boundary parameter by a pole placement method, to substitute the dynamic boundary parameter into the state constraint function to obtain the dynamic boundary; It should be noted that, in order to ensure that the mechanical arm does not collide with the converter station equipment and other dangerous situations during oil sample collection, a state constraint control method based on a dynamic boundary is designed. A dynamic boundary function is defined, which is obtained through a third-order dynamic boundary filter, and then a dynamic boundary is obtained: ; wherein, is an output variable of the state constraint function, quantifying the constraint satisfaction degree of the system state, is a third coefficient of the state constraint function, increasing can avoid the state from exceeding the constraint due to excessive acceleration, is a second coefficient of the state constraint function, increasing can enhance the suppression of the state from rapidly deviating from the constraint, is a first coefficient of the state constraint function, increasing can enhance the suppression ability of and speed up the convergence of to the negative interval, is a disturbance compensation coefficient in the state constraint function, compensating for the influence of system disturbance on the state constraint, is a deviation variable in the state constraint function, describing the deviation of the actual state from the constraint boundary, is a constant term of the state constraint function, setting the reference threshold of the constraint function, is an output variable of the state constraint function, is a first intermediate variable of the state constraint function, obtained by correlating the first-order derivative of with the state deviation, so that the constraint function can accurately respond to the influence of the state deviation on the constraint, avoiding constraint lag, is a second intermediate variable of the state constraint function, obtained by correlating the first-order derivative of with the state deviation derivative, further improving the response accuracy of the constraint function to the dynamic characteristics of the state, and ensuring that the state satisfies the constraint in the dynamic process.

[0035] Further, in some embodiments, the process of calculating the dynamic boundary parameter by the pole configuration method is: define a time constant , is selected according to actual needs ideal regulation time of dominant pole non-dominant pole and , and are constants not less than 5, is the dominant pole, is the non-dominant pole, is the non-dominant pole; According to the filter system matrix, the real characteristic polynomial is obtained, which is equal to the expected characteristic polynomial corresponding to it, so as to determine the value of , , .

[0036] Step S105: According to the non-matching complex disturbance filter, the dynamic boundary generates the expected convergence of the first control signal generation function, obtains the second control signal generation function, and obtains the control signal of the mechanical arm according to the second control signal generation function.

[0037] It should be noted that based on the above dynamic boundary, the state constraint control law is designed, and , , is the expected joint angle, is the virtual control input, is the actual joint angle.

[0038] The second control signal generation function (control law) is obtained according to the following formula: ; , is the k-th derivative of the expected joint angle, is the tracking error or disturbance observer internal state change rate of the system, is the tracking error or disturbance observer internal state of the system, and k is the order.

[0039] The control law can ensure that the tracking error , so that the mechanical arm always moves within the safe dynamic boundary, effectively avoiding the collision risk with the converter station equipment, while ensuring the stability and control accuracy of the system.

[0040] Through the design of the above four control strategies, the comprehensive use of multi-joint robot dynamics modeling, non-matching compound disturbance observation, preset finite time switching control and state constraint control based on dynamic boundary, etc. Methods can realize the high precision, high stability, high safety control of the converter station oil sample collection robot manipulator, and meet the oil sample collection operation demand in the complex environment of the converter station.

[0041] As shown in Figure 2 The designed non-matching disturbance observer can accurately track the actual disturbance in about 0.1s and the estimation error rapidly decreases to near zero when facing three working conditions of pulse disturbance, compound disturbance and time-varying disturbance. This shows that the observer has good estimation performance for the disturbance of the system and can capture the dynamic change trend of the disturbance in a very short time, providing a reliable basis for subsequent control compensation. In addition, under any type of disturbance condition, the output signal of the observer remains stable and does not appear to be fluctuating or oscillating, further verifying the robustness and adaptability of the designed observer.

[0042] As shown in Figure 3 Under three disturbance conditions, the adaptive law can quickly adjust to the appropriate parameter value and tend to be stable in a short time, indicating that the adaptive mechanism can effectively cope with external disturbances and system uncertainties, realize online estimation and real-time compensation of unknown disturbances. In addition, the adaptive law remains stable throughout the experiment without significant oscillation, fully demonstrating the adaptive ability and anti-interference performance of the proposed control strategy.

[0043] As shown in Figure 4 The tracking performance comparison results of the manipulator under the conditions of existing compound disturbance and no disturbance can be clearly seen from the figure, and the proposed control method shows good tracking ability and robustness under the two conditions. Specifically, whether there is compound disturbance or not, the joint angle of the manipulator can quickly converge to the expected trajectory, and the tracking speed is almost the same. At the same time, the tracking error is always kept in a very small range, and the error curves under the two conditions are almost coincided, indicating that the influence of external disturbance on the dynamic response of the system is effectively suppressed.

[0044] In summary, the control method applied to the converter station oil sample collection robot manipulator proposed by the application has the following advantages: The application brings significant technical effects by fusing three core technologies of "preset time switching control", "non-matching disturbance observation" and "dynamic boundary constraint". In terms of time control, the switching preset time time-varying convergence function is adopted, so that the convergence time of the mechanical arm is completely controllable and is not affected by the initial state and parameters. After the preset time, the system robustness is maintained through smooth switching, solving the contradiction between insufficient time accuracy and weak anti-disturbance ability in the traditional time control method, and meeting the demand for time accurate control and long-term stability in the scene of oil sample collection in the converter station. In terms of disturbance resistance, the designed non-matching compound disturbance observer does not need to rely on the "disturbance derivative bounded" assumption, and can estimate multiple complex disturbances at the same time. Combined with adaptive compensation technology, it realizes asymptotic tracking. Experiments show that it can accurately track within 0.1s under pulse, compound and time-varying disturbances, and the estimation error tends to zero, greatly improving the anti-disturbance ability in complex environments.

[0045] In terms of state constraint, the dynamic boundary filter based on pole placement allows the constraint boundary to be adjusted in real time according to the disturbance, which not only ensures that the mechanical arm does not exceed the boundary (such as avoiding collision with the equipment in the converter station), but also avoids signal jitter when approaching the boundary in traditional fixed boundary control. In the face of sudden disturbances, it can prevent the constraint from breaking through, improving the safety under strong interference. At the same time, through accurate kinematics and dynamics modeling, a reliable foundation is provided for the control strategy, and the time controllability, disturbance resistance accuracy and constraint robustness of the mechanical arm are synergistically improved. Experiments verify that it can quickly converge to the desired trajectory with or without compound disturbance, with minimal tracking error and stability, adapting to the high-precision, stable and safe oil sample collection operation demand in the complex environment of the converter station.

[0046] As shown in Figure 5 An embodiment of the application proposes a control system applied to a mechanical arm of a robot for oil sample collection in a converter station, the system comprising: A model construction module 10 is used to sequentially construct a kinematics model, a dynamics model and a three-order cascade motor system model of the mechanical arm of the robot for oil sample collection in the converter station; A filter design module 20 is used to design a non-matching compound disturbance filter according to the three-order cascade motor system model; A control signal generation module 30 is used to design a switching preset finite time time-varying convergence function, and construct a first control signal generation function according to the switching preset finite time time-varying convergence function; A dynamic boundary calculation module 40 is used to design a state constraint function based on a dynamic boundary, and calculate dynamic boundary parameters by a pole placement method, so as to substitute the dynamic boundary parameters into the state constraint function to obtain the dynamic boundary; The control signal output module 50 is configured to generate a second control signal generation function by performing expected convergence on the first control signal generation function based on the non-matching compound disturbance filter and the dynamic boundary, and obtain the control signal of the robot arm based on the second control signal generation function.

[0047] In another aspect, the application further provides a storage medium having one or more programs stored thereon, which, when executed by a processor, implement the control method for the robot arm of the converter station oil sample collection machine.

[0048] In another aspect, the application further provides an electronic device including a memory and a processor, wherein the memory is configured to store a computer program, and the processor is configured to execute the computer program stored in the memory to implement the control method for the robot arm of the converter station oil sample collection machine.

[0049] Those skilled in the art can understand that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a list of executable instructions for implementing the logic function, which can be specifically implemented in any computer readable medium for use by or in conjunction with an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor or other system that can fetch and execute instructions from the instruction execution system, device or apparatus) or in conjunction with these instruction execution systems, devices or apparatus. For the purpose of this specification, the "computer readable medium" can be any device that can contain a storage, communication, propagation or transmission of programs for use by or in conjunction with the instruction execution system, device or apparatus or in conjunction with these instruction execution systems, devices or apparatus.

[0050] More specific examples (non-exhaustive list) of the computer readable medium include the following: an electrical connection having one or more wires (electrical devices), a portable computer diskette (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CD ROM). In addition, the computer readable medium can even be paper or other suitable medium on which the program can be printed, as the program can be electronically obtained, for example, by optical scanning of the paper or other medium, followed by electronic conversion, interpretation or processing, if necessary, in other suitable ways, and then stored in a computer memory.

[0051] It should be understood that aspects of the application can be implemented in hardware, software, firmware or a combination of them. In the above embodiments, various steps or methods can be implemented in software or firmware which is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technologies, known in the art, or a combination thereof, can be used: a discrete logic circuit having logic gates for implementing logic functions upon an data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), and / or the like.

[0052] While the embodiments of the application have been illustrated and described in detail, it will be readily apparent to those skilled in the art that various modifications and changes can be made to the embodiments without departing from the scope and spirit of the application as described in the claims. Moreover, the application described is not limited to the particular embodiments described herein but extends to other embodiments and variations that will be apparent to those skilled in the art in view of the above descriptions.

Claims

1. A control method for a robotic arm used in oil sampling at a converter station, characterized in that, The method includes: The kinematic model, dynamic model and three-stage motor system model of the converter station oil sampling robot arm were constructed sequentially. Design an unmatched composite disturbance filter based on the three-stage coupled motor system model described above; Design a switching preset finite-time time-varying convergence function, and construct a first control signal generation function based on the switching preset finite-time time-varying convergence function; Design a state constraint function based on dynamic boundaries, and calculate the dynamic boundary parameters using the pole placement method. Substitute the dynamic boundary parameters into the state constraint function to obtain the dynamic boundary. The first control signal generation function is expected to converge based on the unmatched composite perturbation filter and dynamic boundary to obtain the second control signal generation function, and the control signal of the robotic arm is obtained based on the second control signal generation function.

2. The control method for the robotic arm used in oil sample collection at a converter station according to claim 1, characterized in that, The steps of sequentially constructing the kinematic model, dynamic model, and three-stage motor system model of the converter station oil sample collection robot arm include: Construct a kinematic model based on the following formula: ； in, It is a homogeneous transformation matrix. Let be the angle of the i-th joint of the robotic arm. Let be the torsion angle of the i-th link of the robotic arm, and let represent the angle between the z-axis of two adjacent links in the coordinate system. Let be the offset of the i-th link of the robotic arm, representing the distance between the x-axis in the coordinate system of two adjacent links; Construct a dynamic model based on the following formula: ； in, Let be a vector of the joint angles of the link. The inertia matrix depends on the joint angle. It is the sum of the Coriolis force and the centrifugal force. and These are the friction force vector and the gravitational torque vector, respectively. For joint torque, For external force disturbances applied to the torque of the robotic arm, Let be the vector of the joint angular acceleration of the link, and be the second derivative of q with respect to time; Construct a three-stage multi-stage motor system model based on the following formula: ； in, Let be the torque constant of the motor. For the motor armature current, This is the derivative of the armature current with respect to time, i.e., the rate of change of current. For armature inductance, Let be the back electromotive force constant of the motor. This is the derivative of the motor's rotation angle with respect to time, i.e., the motor's angular velocity. For input control voltage, External environmental interference with voltage This is the circuit resistance.

3. The control method for the robotic arm used in oil sample collection at a converter station according to claim 2, characterized in that, The steps for designing the unmatched composite disturbance filter based on the three-stage coupled motor system model include: make , , , , The three-stage multistage motor system model then transforms into: ； Specific location, , , , , , , ; in, Let be the angular velocity of the robotic arm joint. The angular acceleration of the robotic arm joints, This is the product of the motor torque constant and the inverse of the inertia matrix, reflecting the coupling relationship between the motor torque constant and the inertial characteristics of the robotic arm. It is the reciprocal of the armature inductance, reflecting the dynamic response characteristics of the motor's electrical system to current. This is a nonlinear force term related to joint angular velocity, which comprehensively reflects the combined effects of nonlinear factors such as Coriolis force, centrifugal force, friction, and gravity on joint movement. This is a damping term in the electrical system related to the armature current, affecting the dynamic changes of the current. For the equivalent disturbance term related to external forces, For the equivalent disturbance term related to voltage interference, The inverse of the robotic arm's inertia matrix. The inertia matrix, The terms represent the Coriolis force and centrifugal force, reflecting the nonlinear coupling characteristics between joint movements. The friction term describes the frictional resistance experienced by the robotic arm's joints during movement. This is a gravity term, generated by the gravity of each link in the robotic arm; Design an unmatched composite perturbation filter based on the following formula: ； in, For the internal state of the observer Time derivative, Here is the gain matrix of the observer. For the internal state variables of the observer, A nonlinear function related to the state of the robotic arm. This is an estimate of the disturbance. For another gain matrix, For the internal state of the observer Time derivative, For another internal state variable of the observer, auxiliary To track and estimate disturbances more accurately. A nonlinear function related to the state of the robotic arm. As an intermediate variable, For the final disturbance compensation signal, For the gain matrix, the intermediate variables are... It is converted into a signal used to compensate for disturbances in the robotic arm.

4. The control method for the robotic arm used in oil sample collection at a converter station according to claim 3, characterized in that, The steps of designing a switching preset finite-time time-varying convergence function and constructing a first control signal generation function based on the switching preset finite-time time-varying convergence function include: Design a switching preset finite-time time-varying convergent function based on the following formula: ； in, For switching, a preset finite-time time-varying convergence function is used. For positive integers, For preset time, and ; make ,when hour, ,and ;when hour, ; in, To adjust the dynamic characteristics of the system, These are the core parameters of the system. For the internal state variables of the perturbation observer, The derivatives of the system's core parameters. This is the maximum allowed value.

5. The control method for the robotic arm used in oil sample collection at a converter station according to claim 4, characterized in that, The method further includes: exist The stage involves time-varying transformation and is defined. ,in, , ; Construct the first Lyapunov candidate function Derive its derivative And using Young's inequality for processing, a virtual control signal is designed. ,in, , , All are positive constants, and I is the identity matrix; Constructing a second Lyapunov candidate function ,according to The derivative is used to construct the first control signal generation function: ； in, , All are positive numbers; in, These are the state variables after the first stage of time-varying transformation. These are the state variables after the second-stage time-varying transformation. It is 2+m. For 1+m, For subsequent virtual control inputs, m > 0 represents an integer. Here is the gain matrix. For control signals, The nonlinear characteristic function of the system, The derivative of the virtual control input for subsequent design. This is the coefficient matrix of the actual controller. This is the coefficient matrix of the virtual control signal. This is the gain matrix; exist The time-varying transformation is simplified to the following stages: ,in, , Design virtual control signals ,in All are positive numbers. Therefore, the first control signal generation function is: ； in, All are positive numbers. Here is the gain matrix. Here is the gain matrix. It means a diagonal matrix.

6. The control method for the robotic arm used in oil sample collection at a converter station according to claim 5, characterized in that, The steps for designing the state constraint function based on dynamic boundaries include: Design the state constraint function based on the following formula: ； in, The output variable of the state constraint function quantifies the degree to which the system state constraints are satisfied. The third coefficient of the state constraint function is increased. This can prevent the state from exceeding the constraint due to excessive acceleration. The second coefficient of the state constraint function is increased. This can enhance the suppression of constraints that allow for rapid deviations from the state. The first coefficient of the state constraint function is increased. Can enhance the ability to The inhibitory ability, accelerate The speed of convergence to the negative interval This is the disturbance compensation coefficient in the state constraint function, used to compensate for the impact of system disturbances on the state constraints. The deviation variable in the state constraint function describes the deviation between the actual state and the constraint boundary. For the constant term of the state constraint function, set the baseline threshold for the constraint function. For the output variables of the state constraint function, As the first intermediate variable of the state constraint function, by The first derivative is obtained by relating it to the state deviation, enabling the constraint function to accurately respond to the impact of the state deviation on the constraint and avoiding constraint lag. The second intermediate variable of the state constraint function is... The first derivative is obtained by relating it to the state deviation derivative, which further improves the response accuracy of the constraint function to the dynamic characteristics of the state and ensures that the state satisfies the constraints during the dynamic process.

7. The control method for the robotic arm used in oil sample collection at a converter station according to claim 6, characterized in that, The steps for calculating the dynamic boundary parameters using the pole placement method include: Define a time constant ,in It is selected based on actual needs. Ideal settling time; Dominant pole Non-dominant poles and ,in, and All are constants not less than 5. As the dominant pole, It is a non-dominant pole. It is a non-dominant pole; The true characteristic polynomial is obtained from the filter system matrix. This true characteristic polynomial is then equated with the expected characteristic polynomial to determine the true characteristic polynomial. , , The value of .

8. The control method for the robotic arm used in oil sample collection at a converter station according to claim 7, characterized in that, The step of performing expected convergence on the first control signal generation function based on the unmatched composite perturbation filter and dynamic boundary to obtain the second control signal generation function, and obtaining the control signal of the robotic arm based on the second control signal generation function includes: definition , ,in, For the desired joint angle, For virtual control input, This refers to the actual joint angle; The second control signal generation function is obtained according to the following formula: ； in, Let k be the k-th derivative of the desired joint angle. The tracking error of the system or the rate of change of the internal state of the disturbance observer, The tracking error or disturbance of the system represents the internal state of the observer, and k is the order.

9. A control system for a robotic arm used in oil sampling at a converter station, characterized in that, The system includes: The model building module is used to sequentially build the kinematic model, dynamic model, and three-stage motor system model of the converter station oil sample collection robot arm. The filter design module is used to design an unmatched composite disturbance filter based on the three-stage coupled motor system model. The control signal generation module is used to design a switching preset finite-time time-varying convergence function and to construct a first control signal generation function based on the switching preset finite-time time-varying convergence function. The dynamic boundary calculation module is used to design a state constraint function based on dynamic boundaries and calculate dynamic boundary parameters through the pole placement method. The dynamic boundary parameters are then substituted into the state constraint function to obtain the dynamic boundary. The control signal output module is used to perform expected convergence of the first control signal generation function based on the unmatched composite disturbance filter and dynamic boundary to obtain the second control signal generation function, and to obtain the control signal of the robotic arm based on the second control signal generation function.

10. A storage medium, characterized in that, The storage medium stores one or more programs that, when executed by a processor, implement the control method for the robotic arm used in oil sample collection at a converter station as described in any one of claims 1-8.

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