Textile mechanical arm visual servo trajectory tracking control method and system based on fuzzy observer

Abstract

The invention belongs to the technical field of textile mechanical arm trajectory tracking control, and discloses a textile mechanical arm visual servo trajectory tracking control method and system based on a fuzzy observer. In order to solve the problems that visual velocity information is difficult to obtain and a system model is uncertain, a fuzzy observer is designed to obtain a visual velocity estimated value, and a fuzzy logic system is utilized to approach unknown nonlinear dynamics. Besides, an instruction filtering technology is applied, the problem of calculation complexity of the visual servo system of the mechanical arm is solved, an error compensation mechanism is introduced to eliminate adverse effects caused by filtering errors, and the control effect of the visual servo system is improved; and meanwhile, the negative influence of the input dead zone on the system performance is also compensated. The method can effectively solve the problems that the visual speed information is difficult to obtain and the calculation is complex when the controller is designed, has a good control effect, and is suitable for a scene where the textile industry production robot has high control precision requirements.

Description

Technical Field

[0001] This invention belongs to the field of trajectory tracking and control technology for textile robotic arms, specifically relating to a visual servo trajectory tracking and control method and system for textile robotic arms based on a fuzzy observer. Background Technology

[0002] my country is a major exporter and producer of textiles. However, for large-scale production workshops, traditional manual operation methods are gradually failing to meet the ever-increasing production demands. Therefore, it is essential to achieve intelligent development of textile equipment to improve productivity. Multi-degree-of-freedom textile robotic arms, due to their ability to freely grasp textile raw materials, greatly save manpower and improve production process safety, have gradually become important production equipment in textile workshops. However, in unfamiliar working environments, robotic arms often cannot adapt to production requirements due to their lack of environmental awareness. To address this challenge, a visual servo control solution integrating visual perception and control technologies has emerged. Visual servo control utilizes image information as a real-time feedback signal, enabling real-time target perception and thus achieving precise positioning and flexible grasping of moving objects in complex environments. Currently, visual servo control has become an important control method in the industrial field and has enormous development potential.

[0003] Currently, the control methods for robotic arms in the textile industry mainly combine proportional-integral-derivative (PID) control with gravity terms. This control method often requires a precise robotic arm dynamics model. However, when the robotic arm is in an unknown working environment, establishing a precise robotic arm dynamics model is often very difficult. Research has found that fuzzy logic systems can effectively handle uncertainties and nonlinear terms in systems and are now widely used in nonlinear system control. On the other hand, the backstepping method is widely used to handle the control problems of rigid multi-joint robotic arms due to its ease of combination with fuzzy control and adaptive control. However, when designing a controller using the backstepping method, it is necessary to continuously differentiate the virtual control function, which greatly increases the computational load.

[0004] In existing research, most visual servo controllers require the acquisition of visual velocity measurements. However, in practice, visual velocity is often difficult to obtain, and most existing measurement schemes obtain visual velocity values ​​by taking the derivative of image position information. This method often contains significant measurement noise, which degrades system performance. Furthermore, dead zone is a typical nonlinear model, and the dead zone effect can severely impact system performance and may even compromise system stability. Summary of the Invention

[0005] The purpose of this invention is to propose a visual servo trajectory tracking control method for textile robotic arms based on a fuzzy observer, so as to achieve accurate trajectory tracking control of rigid multi-joint textile robotic arm systems.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A vision servo trajectory tracking control method for a textile robotic arm based on a fuzzy observer includes the following steps: Step 1. Establish a camera model and a dynamic model of a rigid multi-joint textile robotic arm with uncertainties and input dead zones; Based on the visual projection model, the relationship between the projection coordinates of the feature points on the image plane and their coordinates in the base coordinate system of the robotic arm is obtained. After differentiation, the relationship between the velocity of the feature points and the angular velocity of the robotic arm joints is obtained. Through coordinate transformation, the dynamic model of the rigid multi-joint textile robotic arm in the image space is obtained. Step 2. Design the control system for the robotic arm's vision servo system to obtain a fuzzy adaptive output feedback controller for the robotic arm under unknown vision speed information. The specific design process of the controller is as follows: A fuzzy observer is designed to obtain an estimate of the visual velocity. A fuzzy logic system is used to handle the unknown nonlinear dynamics in the robotic arm's visual servo system. Instruction filtering technology is used to solve the computational complexity problem. An error compensation mechanism is introduced to eliminate the influence of filtering errors. The impact of the input dead zone on system performance is also considered. Finally, a realistic control law is designed. Step 3. Use a fuzzy adaptive output feedback controller for the robotic arm to achieve trajectory tracking control of the rigid multi-joint textile robotic arm system.

[0007] Furthermore, based on the aforementioned fuzzy observer-based visual servo trajectory tracking control method for textile robotic arms, this invention also proposes a corresponding fuzzy observer-based visual servo trajectory tracking control system for textile robotic arms, which adopts the following technical solution: A vision servo trajectory tracking control system for a textile robotic arm based on a fuzzy observer includes the following modules: The model building module is used to build a camera model and a dynamic model of a rigid multi-joint textile robot arm with uncertainty and input dead zone; Based on the visual projection model, the relationship between the projection coordinates of the feature points on the image plane and their coordinates in the base coordinate system of the robotic arm is obtained. After differentiation, the relationship between the velocity of the feature points and the angular velocity of the robotic arm joints is obtained. Through coordinate transformation, the dynamic model of the rigid multi-joint textile robotic arm in the image space is obtained. The controller design module is used to design the control of the robotic arm's vision servo system, resulting in a fuzzy adaptive output feedback controller for the robotic arm under unknown vision speed information. The specific design process of the controller is as follows: A fuzzy observer is designed to obtain an estimate of the visual velocity. A fuzzy logic system is used to handle the unknown nonlinear dynamics in the robotic arm's visual servo system. Instruction filtering technology is used to solve the computational complexity problem. An error compensation mechanism is introduced to eliminate the influence of filtering errors. The impact of the input dead zone on system performance is also considered. Finally, a realistic control law is designed. And a trajectory tracking control module, used to achieve trajectory tracking control of the rigid multi-joint textile robotic arm system using a fuzzy adaptive output feedback controller for the robotic arm.

[0008] Furthermore, based on the aforementioned fuzzy observer-based visual servo trajectory tracking control method for textile robotic arms, this invention also proposes a computer device comprising a memory and one or more processors. Executable code is stored in the memory. When the processor executes the executable code, it implements the steps of the aforementioned fuzzy observer-based visual servo trajectory tracking control method for textile robotic arms.

[0009] Furthermore, based on the aforementioned fuzzy observer-based visual servo trajectory tracking control method for textile robotic arms, this invention also proposes a computer-readable storage medium storing a program that, when executed by a processor, implements the steps of the aforementioned fuzzy observer-based visual servo trajectory tracking control method for textile robotic arms.

[0010] The present invention has the following advantages: As described above, this invention proposes a visual servo trajectory tracking control method for a textile robotic arm based on a fuzzy observer. This method constructs a dynamic model of the robotic arm in image space by combining the dynamic model of the textile robotic arm with a visual projection model, and then designs the control law, eliminating the need for 3D reconstruction and avoiding the accumulation of errors generated during 3D reconstruction. Simultaneously, this invention also considers the difficulty in obtaining visual velocity information, designing a fuzzy observer to obtain a predicted value of visual velocity information for controller design, and using a fuzzy logic system to approximate the unknown nonlinear dynamics of the textile robotic arm system, solving the control problem of a rigid multi-joint textile robotic arm system under uncertain dynamics. Furthermore, this invention employs instruction filtering technology to solve the computational complexity problem in the controller design process and introduces an error compensation mechanism to eliminate the negative impact of filtering errors on control accuracy, thereby improving the system's control accuracy. In addition, the controller designed in this invention can effectively solve the input dead zone problem, thereby improving the system's control performance. Attached Figure Description

[0011] Figure 1 This is a flowchart of the visual servo trajectory tracking control method for a textile robotic arm based on a fuzzy observer, as described in an embodiment of the present invention. Figure 2This is a schematic diagram of the robotic arm vision servo system in an embodiment of the present invention; Figure 3 This is a schematic diagram of the visual projection model in an embodiment of the present invention; Figure 4 This is a control flowchart of the vision servo trajectory tracking control method for a textile robotic arm based on a fuzzy observer, as described in an embodiment of the present invention. Figure 5 This is a tracking trajectory diagram of the image projection plane obtained by the control method in this embodiment of the invention; Figure 6 The tracking trajectory of the image projection plane obtained by the control method of this invention. and A curve graph; Figure 7 The tracking trajectory of the image projection plane obtained by the control method of this invention. and A curve graph; Figure 8 The tracking error of the image projection plane obtained by the control method of this invention. and A curve graph; Figure 9 To obtain by using the control method of the present invention Axial vision speed and Axis vision speed estimation A curve graph; Figure 10 To obtain by using the control method of the present invention Axial vision speed and Axis vision speed estimation A curve graph; Figure 11 The virtual control function obtained using the control method of this invention and filter output The curve graph. Detailed Implementation

[0012] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments: Example 1 This embodiment 1 proposes a visual servo trajectory tracking control method for a textile robotic arm based on a fuzzy observer. This method obtains visual velocity estimates by designing a fuzzy observer, uses a fuzzy logic system to process the unknown nonlinear dynamics in the robotic arm's visual servo system, employs instruction filtering technology to solve the computational complexity problem, and introduces an error compensation mechanism to eliminate the influence of filtering errors. A fuzzy adaptive output feedback controller for the robotic arm is constructed to achieve accurate trajectory tracking control of a rigid multi-joint textile robotic arm system.

[0013] like Figure 1 As shown, the vision servo trajectory tracking control method for a textile robotic arm based on a fuzzy observer includes the following steps: Step 1. Establish a camera model and a dynamic model of a rigid multi-joint textile robotic arm with uncertainties and input dead zones; according to Figure 2 The visual projection model shown obtains the relationship between the projection coordinates of the feature points on the image plane and their coordinates in the base coordinate system of the robotic arm. The relationship between the velocity of the feature points and the angular velocity of the robotic arm joints is obtained by differentiation. The dynamic model of the robotic arm is mapped from the joint space to the image space by the coordinate transformation shown by formulas (11)-(12), and the dynamic model of the rigid multi-joint textile robotic arm in the image space is obtained.

[0014] like Figure 3 As shown, a coordinate system for the end effector of the robotic arm, a base coordinate system for the robotic arm, and a camera coordinate system are established to represent the motion of the end effector of the robotic arm and the relationship between the base coordinate system of the robotic arm and the camera coordinate system.

[0015] The coordinate axes of the robot arm's base coordinate system are , , The coordinate axes of the robotic arm end effector coordinate system are: , , The coordinate axes of the camera coordinate system are , , .

[0016] according to Figure 2 The visual projection model shown can be used to obtain the relationship between the coordinates of the feature points in the base coordinate system of the robotic arm and their projected coordinates on the image plane, as shown in formula (1).

[0017] Homogeneous coordinates of feature points projected onto the image plane for: (1) in, This indicates that the feature points are on the image projection plane. The value of the axis, This indicates that the feature points are on the image projection plane. The value of the axis, Indicates the depth of the feature points. This indicates the joint angles of the robotic arm. The coordinates of the feature point in the coordinate system of the end effector of the robotic arm are homogeneous. The external parameters of the camera are the homogeneous transformation matrix from the coordinate system of the robotic arm's end effector to the camera coordinate system. Let be the homogeneous transformation matrix from the robot arm's base coordinate system to the robot arm's end effector coordinate system. The coordinates of the feature point in the robot arm's base coordinate system are homogeneous. This represents a matrix determined by the camera's internal parameters.

[0018] use The perspective projection matrix is ​​expressed as follows: (2) The expression is as follows: (3) in, Represents the perspective projection matrix The third row vector.

[0019] Differentiating equation (1), we obtain the velocity relationship shown in equation (4): (4) in, ; Represents the perspective projection matrix The first row vector, Represents the perspective projection matrix The second row vector; This indicates the joint angular velocity of the robotic arm. It is a composite Jacobian matrix.

[0020] pseudo-reversal for: .

[0021] A variable with an input dead zone and degrees of freedom The dynamic model of the rigid multi-joint textile robotic arm is as follows: (5) in, Represents the inertia matrix of the robotic arm. This represents the Coriolis force and centripetal force matrix of the robotic arm. This represents the gravity vector of the robotic arm. Right now , The number of joints, or degrees of freedom, in a robotic arm system; This represents the joint torque of a robotic arm with an input dead zone. The input value for the dead zone characteristic is the output value of the robotic arm controller. The output value of the dead zone characteristic is Furthermore, the input and output of the dead zone satisfy the following relationship: (6) in and The left and right slopes represent the dead zone characteristics. and The left and right critical points representing the dead zone characteristics, indicated by the subscript of the parameter. Represents the first parameter vector. One element, Represents the output vector of the robotic arm controller The Each element.

[0022] Parameters in this embodiment , All are known bounded normal values, and the slopes of the dead zones are the same on both sides, i.e. , and Unknown and bounded.

[0023] The dead zone characteristic, i.e., formula (6), can then be transformed into the following form: (7) in, and The definition is as follows: (8) (9) Due to parameters , , and Both are bounded, thus obtaining It is also bounded, that is , Represents any positive integer.

[0024] (10) in, , , , express The One element, express The One element, Represents a diagonal matrix. express The Each element.

[0025] After the following coordinate transformation: (11) (12) Substituting equations (11) and (12) into equation (5), we obtain the dynamic model of the rigid multi-joint textile robot arm as follows: (13) in, , , ; ; , for ; , , These represent the feature point's position, velocity, and acceleration, respectively. (Definition) and For ease of description, the following variable substitutions are made: Definition for , for , for , for ; Then formula (13) can be rewritten as: (14) in, for , for , for , for .

[0026] Step 2. Design the control system for the robotic arm's vision servo system to obtain a fuzzy adaptive output feedback controller for the robotic arm under unknown vision speed information; the specific design process of the controller is as follows: A fuzzy observer is designed to obtain an estimate of the visual velocity. A fuzzy logic system is used to handle the unknown nonlinear dynamics in the robotic arm's visual servo system. Instruction filtering technology is used to solve the computational complexity problem. An error compensation mechanism is introduced to eliminate the impact of filtering errors. The impact of the input dead zone on system performance is also considered. Finally, a true control law is designed.

[0027] The instruction filter is constructed as follows: ; in, , , The output signal of the instruction filter. , It is a constant; This is the input signal for the instruction filter; , These represent the internal state variables of the filter and their derivative values, respectively.

[0028] Initial state of the instruction filter , ;in for initial value, for initial value, for The initial value.

[0029] when When the input signal satisfies , ,in , If is a constant, then for any ,exist and , making ,and , , Both are bounded; in , .

[0030] The fuzzy observer is constructed as follows: ; in, This is a state estimate; The estimation error is defined as: ; For positive design parameters, ; This is a vector estimate; express The estimated value; express The estimated value.

[0031] For the definition in compact set Continuous functions in There is always a fuzzy logic system Make: ; in, Represents the weight matrix. , Represents fuzzy basis functions; It is the approximation error, and , This indicates that any given positive number is used.

[0032] For functions and any positive number The following inequalities hold: ; in, It is the vector 2 norm.

[0033] Therefore, we can conclude that: (15) in, , , , Therefore, we get: , This represents the approximation error.

[0034] Suppose there is a symmetric matrix There will always exist a symmetric matrix. ,satisfy .

[0035] Define the error variable: (16) in, , Represents the error variable. This represents the preset desired signal. This represents the output signal of the filter.

[0036] The compensation tracking error variable is defined as follows: (17) in, , This represents the variable used to compensate for tracking error. , This is the signal for filtering error compensation.

[0037] Select function for: ; right Differentiation yields: (18) According to Young's inequality: (19) (20) Substituting formulas (19) and (20) into formula (18), we get: (twenty one) Select function for: ; right Differentiation yields: (twenty two) According to Young's inequality: (twenty three) Design virtual control law and error compensation signal for: (twenty four) in, For system control gain, and .

[0038] Substituting formulas (23) and (24) into formula (22), we get: (25) Select function for: ; in, It is a positive definite matrix. The weight matrix represents the first... Column vector estimation error Let be the dimension of the fuzzy basis function vector.

[0039] right Differentiation yields: (26) in, This indicates a signal to compensate for tracking errors. This represents the adaptive law.

[0040] According to Young's inequality: (27) Design a real control law Compensation signal Adaptive Law for: (28) in, For system control gain, and ; It is a positive number.

[0041] Substituting formulas (27) and (28) into formula (26), we get: (29) in The weight matrix represents the first... Column vector.

[0042] The fuzzy adaptive output feedback controller for the robotic arm is designed as follows: .

[0043] This invention solves the problem of difficulty in obtaining visual velocity information by designing an observer to acquire visual velocity estimates, and avoids the interference of measurement noise on control performance. Furthermore, this invention employs command filtering technology to effectively reduce computational complexity, and simultaneously mitigates the negative impact of filtering errors on control accuracy by combining it with filtering error compensation technology.

[0044] After completing the design of the fuzzy adaptive output feedback controller for the robotic arm, the Lyapunov function of the rigid multi-joint textile robotic arm system was selected for derivation, proving that the Lyapunov function of the rigid multi-joint textile robotic arm system controlled by the fuzzy adaptive output feedback controller is stable.

[0045] The specific process of stability analysis for a rigid multi-joint textile robotic arm system controlled by a fuzzy adaptive output feedback controller is as follows: Select function : (30) Differentiating formula (30) and substituting formulas (24), (28), and (29) into it, we get: (31) in: ; in, , These represent the minimum and maximum values ​​of the matrix trace, respectively. From formula (31): (32) in, express exist Moment function value, express exist Moment function value, ; Formula (32) shows and Both belong to compact clustering , This represents the weight matrix estimation error; therefore, all signals in the closed-loop system are bounded. The error compensation signal for the command filter is... satisfy , can be obtained It is bounded; because ,and If it is bounded, then the tracking error It is also bounded.

[0046] Step 3. Use a fuzzy adaptive output feedback controller for the robotic arm to achieve trajectory tracking control of the rigid multi-joint textile robotic arm system.

[0047] Specifically, based on the relationship between feature point velocity and robotic arm joint angular velocity, the robotic arm dynamics model is mapped to image space through coordinate transformation to obtain the robotic arm dynamics model in image space. Addressing the difficulty in obtaining visual velocity information and the presence of unknown nonlinear dynamics in the robotic arm system, a fuzzy observer is designed to obtain an estimate of the visual velocity information, and a fuzzy logic system is used to approximate the mathematical model of the rigid multi-joint textile robotic arm system. Command filtering technology is employed, and an error compensation mechanism is introduced to solve the computational complexity problem and eliminate filtering errors, thereby achieving trajectory tracking control of the rigid multi-joint textile robotic arm system. Figure 4 A control flowchart of the control method of the present invention is shown.

[0048] To verify the effectiveness of the control method of this invention, the rigid multi-joint textile robotic arm control system was simulated using MATLAB / Simulink with the following parameters: The link offset of the robotic arm is set to 0m, and the initial joint angle is set to... The initial joint angular velocities are all 0. (Camera's...) shaft and The scale factor of each axis is 1500, and the included angle between the two axes is set to... The principal point coordinates are (640, 512).

[0049] The link parameters of the robotic arm are shown in Table 1: Table 1. Joint parameters of the robotic arm

[0050] in, Indicates the length of the link. Indicates the mass of the connecting rod. It represents the moment of inertia.

[0051] The initial feature point coordinates are selected as follows: .

[0052] Select the controller parameters as follows: , , , .

[0053] Select the filter parameters as follows: , .

[0054] Select the observer parameters as follows: , .

[0055] The extrinsic parameter matrix is ​​chosen as follows: .

[0056] The selected tracking curve is: .

[0057] Select the dead zone parameter as follows: , .

[0058] Figure 5 The image shows the projection tracking trajectory of feature points on the image projection plane. It can be seen that the rigid multi-joint robotic arm system controlled by the controller designed by the control method of this invention has a good tracking effect.

[0059] Figure 6 and Figure 7 It is the tracking trajectory of the image projection plane obtained by the control method of the present invention, wherein This indicates the trajectory of feature points on the image projection plane. The value of the axis, Represents the desired trajectory The value of the axis, This indicates the trajectory of feature points on the image projection plane. The value of the axis, Represents the desired trajectory The value of the axis. Figure 8 The tracking error is obtained using the control method of this invention, wherein express Shaft error, express Shaft error. Figures 6 to 8 The unit of the vertical axis is pixels. Figures 6 to 8 It can be seen that the rigid multi-joint robotic arm system controlled by the controller designed by the control method of the present invention has advantages such as fast convergence speed and small tracking error.

[0060] Figure 9 and Figure 10 This is the observer tracking curve obtained using the control method of this invention, wherein... and These are feature points Shaft speed and Shaft speed, and They are feature points Axis velocity observations and Axis velocity observations, from Figure 9 and Figure 10 It can be seen that the observer designed in this paper has good observation results.

[0061] Figure 11 This is a graph of the input and output curves of the instruction filter, where Right now Representing virtual control law The first element in express The first element in Right now Representing virtual control law The second element in express The second element in Figure 11 The instruction filtering demonstrates that it has a good filtering effect.

[0062] Simulation results demonstrate that this invention effectively tracks and controls the target trajectory, exhibiting not only small tracking errors but also robustness in handling uncertainties within the system model. The method addresses the difficulty in obtaining visual velocity information by designing a fuzzy observer to acquire a predicted visual velocity estimate. Furthermore, it utilizes a fuzzy logic system to approximate unknown nonlinear dynamics in the model, thereby effectively controlling the robotic arm to reach the desired position. The controller employs instruction filtering technology and introduces an error compensation mechanism, resolving computational complexity issues and eliminating the adverse effects of filtering errors. This convergence of errors in the state variables, desired signal, and filtered signal results in superior control performance.

[0063] This invention proposes a visual servo trajectory tracking control method for rigid multi-joint textile robotic arms, based on a fuzzy observer. This method constructs a dynamic model of the robotic arm in image space by combining the robotic arm's dynamic model with a visual projection model. The invention obtains the predicted visual velocity through a fuzzy observer and then designs the controller, while utilizing a fuzzy logic system to handle unknown nonlinear dynamics in the system. Furthermore, the invention employs instruction filtering technology and introduces an error compensation mechanism to solve the computational complexity problem and eliminate the adverse effects of filtering errors. In addition, the convergence of all variables in the rigid multi-joint textile robotic arm control system is proven using Lyapunov control principles. The proposed visual servo-based fuzzy adaptive output feedback control strategy for textile robotic arms can achieve tracking control of the desired trajectory of a rigid multi-joint textile robotic arm system, exhibiting good control performance, smaller trajectory tracking errors, less redundant motion, and stronger system robustness. It is suitable for scenarios in textile industrial production robots where high control precision is required.

[0064] Example 2 This embodiment 2 describes a textile robotic arm visual servo trajectory tracking control system based on a fuzzy observer. This system is based on the same inventive concept as the textile robotic arm visual servo trajectory tracking control method based on a fuzzy observer in embodiment 1 above.

[0065] A vision servo trajectory tracking control system for a textile robotic arm based on a fuzzy observer includes the following modules: The model building module is used to build a camera model and a dynamic model of a rigid multi-joint textile robot arm with uncertainty and input dead zone; Based on the visual projection model, the relationship between the projection coordinates of the feature points on the image plane and their coordinates in the base coordinate system of the robotic arm is obtained. After differentiation, the relationship between the velocity of the feature points and the angular velocity of the robotic arm joints is obtained. Through coordinate transformation, the dynamic model of the rigid multi-joint textile robotic arm in the image space is obtained. The controller design module is used to design the control of the robotic arm's vision servo system, resulting in a fuzzy adaptive output feedback controller for the robotic arm under unknown vision speed information. The specific design process of the controller is as follows: A fuzzy observer is designed to obtain an estimate of the visual velocity. A fuzzy logic system is used to handle the unknown nonlinear dynamics in the robotic arm's visual servo system. Instruction filtering technology is used to solve the computational complexity problem. An error compensation mechanism is introduced to eliminate the influence of filtering errors. The impact of the input dead zone on system performance is also considered. Finally, a realistic control law is designed. And a trajectory tracking control module, used to achieve trajectory tracking control of the rigid multi-joint textile robotic arm system using a fuzzy adaptive output feedback controller for the robotic arm.

[0066] It should be noted that any content not mentioned in the above-described functional modules of the system described in Embodiment 2 can be referred to the step description of the corresponding method in Embodiment 1 above, and will not be repeated in detail here.

[0067] Example 3 This embodiment 3 describes a computer device including a memory and one or more processors. Executable code is stored in the memory. When the processor executes the executable code, it implements the steps of the fuzzy observer-based visual servo trajectory tracking control method for a textile robotic arm described in embodiment 1 above.

[0068] Example 4 This embodiment 4 describes a computer-readable storage medium storing a program that, when executed by a processor, is used to implement the steps of the textile robotic arm visual servo trajectory tracking control method based on a fuzzy observer in embodiment 1 above.

[0069] The computer-readable storage medium can be an internal storage unit of any device or apparatus with data processing capabilities, such as a hard disk or memory, or an external storage device of any device with data processing capabilities, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc.

[0070] Of course, the above description is only a preferred embodiment of the present invention. The present invention is not limited to the above-described embodiments. It should be noted that any equivalent substitutions or obvious modifications made by those skilled in the art under the guidance of this specification fall within the scope of this specification and should be protected by the present invention.

Claims

1. A visual servo trajectory tracking control method for a textile robotic arm based on a fuzzy observer, characterized in that, Includes the following steps: Step 1. Establish a camera model and a dynamic model of a rigid multi-joint textile robotic arm with uncertainties and input dead zones; Based on the visual projection model, the relationship between the projection coordinates of the feature points on the image plane and their coordinates in the base coordinate system of the robotic arm is obtained. After differentiation, the relationship between the velocity of the feature points and the angular velocity of the robotic arm joints is obtained. Through coordinate transformation, the dynamic model of the rigid multi-joint textile robotic arm in the image space is obtained. Step 2. Design the control system for the robotic arm's vision servo system to obtain a fuzzy adaptive output feedback controller for the robotic arm under unknown vision speed information. The specific design process of the controller is as follows: A fuzzy observer is designed to obtain an estimate of the visual velocity. A fuzzy logic system is used to handle the unknown nonlinear dynamics in the robotic arm's visual servo system. Instruction filtering technology is used to solve the computational complexity problem. An error compensation mechanism is introduced to eliminate the influence of filtering errors. The impact of the input dead zone on system performance is also considered. Finally, a realistic control law is designed. Step 3. Use a fuzzy adaptive output feedback controller for the robotic arm to achieve trajectory tracking control of the rigid multi-joint textile robotic arm system.

2. The visual servo trajectory tracking control method for a textile robotic arm based on a fuzzy observer according to claim 1, characterized in that, Step 1 specifically involves: Establish a coordinate system for the end effector of the robotic arm, a base coordinate system for the robotic arm, and a camera coordinate system to represent the motion of the end effector of the robotic arm and the relationship between the base coordinate system of the robotic arm and the camera coordinate system. Homogeneous coordinates of feature points projected onto the image plane for: (1) in, This indicates that the feature points are on the image projection plane. The value of the axis, This indicates that the feature points are on the image projection plane. The value of the axis, Indicates the depth of the feature points. This indicates the joint angles of the robotic arm. The coordinates of the feature point in the coordinate system of the end effector of the robotic arm are homogeneous. The external parameters of the camera are the homogeneous transformation matrix from the coordinate system of the robotic arm's end effector to the camera coordinate system. Let be the homogeneous transformation matrix from the robot arm's base coordinate system to the robot arm's end effector coordinate system. These are the homogeneous coordinates of the feature points in the robot arm's base coordinate system. This represents a matrix determined by the camera's internal parameters; use The perspective projection matrix is ​​expressed as follows: (2) The expression is as follows: (3) in, Represents the perspective projection matrix The third row vector; Differentiating equation (1), we obtain the velocity relationship shown in equation (4): (4) in, ; in Represents the perspective projection matrix The first row vector, Represents the perspective projection matrix The second row vector; This indicates the joint angular velocity of the robotic arm; It is a composite Jacobian matrix; pseudo-reversal for: ; A variable with an input dead zone and degrees of freedom The dynamic model of the rigid multi-joint textile robotic arm is as follows: (5) in, Represents the inertia matrix of the robotic arm. This represents the Coriolis force and centripetal force matrix of the robotic arm. This represents the gravity vector of the robotic arm. Right now ; This represents the joint torque of a robotic arm with an input dead zone. The input value for the dead zone characteristic is the output value of the robotic arm controller. The output value of the dead zone characteristic is Furthermore, the input and output of the dead zone satisfy the following relationship: (6) in and The left and right slopes represent the dead zone characteristics. and The left and right critical points represent the dead zone characteristics. Represents the output vector of the robotic arm controller The One element; Known parameters , All are bounded normal values, and the slopes of the dead zones are the same on both sides, i.e. ; and If the condition is unknown and bounded, then the dead zone characteristic, i.e., formula (6), is transformed into: (7) in and The definition is as follows: (8) (9) Due to parameters , , and Both are bounded, thus obtaining It is also bounded, that is , Represent any positive constant; (10) in, , , , express The One element, express The One element, Represents a diagonal matrix. express The One element; After the following coordinate transformation: (11) (12) Substituting equations (11) and (12) into equation (5), we obtain the dynamic model of the rigid multi-joint textile robotic arm as follows: (13) in, , , ; ; , for ; , , These are the feature point position, velocity, and acceleration, respectively; defined. and For ease of description, the following variable substitutions are made: Definition for , for , for , for ; Then formula (13) can be rewritten as: (14) 。 3. The visual servo trajectory tracking control method for a textile robotic arm based on a fuzzy observer according to claim 2, characterized in that, Step 2 specifically involves: The instruction filter is constructed as follows: ； in, , , The output signal of the instruction filter. , It is a constant; This is the input signal for the instruction filter; , These represent the internal state variables of the filter and their derivative values, respectively. Initial state of the instruction filter , ; in for initial value, for initial value, for The initial value; when When the input signal satisfies , ,in , If is a constant, then for any ,exist and , making ,and , , Both are bounded; in , ; The fuzzy observer is constructed as follows: ； in, This is a state estimate; The estimation error is defined as: ; For positive design parameters, ; This is a vector estimate; express The estimated value; express The estimated value; For the definition in compact set Continuous functions in There is always a fuzzy logic system Make: ;in, Represents the weight matrix. , Representing fuzzy basis functions; It is the approximation error, and , Represents positive numbers; For functions and any positive number The following inequalities hold: ； in, It is the vector norm 2; Therefore, we can conclude that: (15) in , , , Therefore, we get: ; Indicates the approximation error; There is a symmetric matrix There will always exist a symmetric matrix. ,satisfy ; Define the error variable: (16) in, , Represents the error variable. This represents the preset desired signal. This represents the filter output signal; The compensation tracking error variable is defined as follows: (17) in, , This represents the variable used to compensate for tracking error. , This is the signal for filtering error compensation. Select function for: ； right Differentiation yields: (18) According to Young's inequality: (19) (20) Substituting formulas (19) and (20) into formula (18), we get: (21) Select function for: ； right Differentiation yields: (22) According to Young's inequality: (23) Design virtual control law and error compensation signal for: (24) in, For system control gain, and Substituting formulas (23) and (24) into formula (22), we get: (25) Select function for: ； in, It is a positive definite matrix. The weight matrix represents the first... Column vector estimation error Let be the dimension number of the fuzzy basis function vector; right Differentiation yields: (26) in, This indicates a signal to compensate for tracking errors. This represents the adaptive law; According to Young's inequality: (27) Design a real control law Compensation signal Adaptive Law for: (28) in, For system control gain, and ; It is a positive number; Substituting formulas (27) and (28) into formula (26), we get: (29) in The weight matrix represents the first... Column vector; The fuzzy adaptive output feedback controller for the robotic arm is designed as follows: 。 4. The visual servo trajectory tracking control method for a textile robotic arm based on a fuzzy observer according to claim 1, characterized in that, In step 2, after completing the design of the fuzzy adaptive output feedback controller for the robotic arm, a stability analysis is performed on the rigid multi-joint textile robotic arm system controlled by the fuzzy adaptive output feedback controller.

5. The visual servo trajectory tracking control method for a textile robotic arm based on a fuzzy observer according to claim 4, characterized in that, In step 2, the specific process of performing stability analysis is as follows: Select function : (30) Differentiating formula (30) and substituting formulas (24), (28), and (29) into it, we get: (31) in: ； in, , These represent the minimum and maximum values ​​of the matrix trace, respectively. From formula (31): (32) in, express exist Moment function value, express exist Moment function value, ; Formula (32) shows and Both belong to compact clustering , This represents the weight matrix estimation error; therefore, all signals in the closed-loop system are bounded. The error compensation signal for the command filter is... satisfy , can be obtained It is bounded; because ,and If it is bounded, then the tracking error It is also bounded.

6. The visual servo trajectory tracking control method for a textile robotic arm based on a fuzzy observer according to claim 1, characterized in that, Step 3 specifically involves: Based on the relationship between feature point velocity and robotic arm joint angular velocity, the robotic arm dynamics model is mapped to image space through coordinate transformation, resulting in the robotic arm dynamics model in image space. Addressing the difficulty in obtaining visual velocity information and the presence of unknown nonlinear dynamics in the robotic arm system, a fuzzy observer is designed to obtain an estimate of the visual velocity information, and a fuzzy logic system is used to approximate the mathematical model of the rigid multi-joint textile robotic arm system. Command filtering technology and an error compensation mechanism are employed to solve the computational complexity problem and eliminate filtering errors, thereby achieving trajectory tracking control of the rigid multi-joint textile robotic arm system.

7. The visual servo trajectory tracking control method for a textile robotic arm based on a fuzzy observer according to claim 1, characterized in that, In step 1, the dynamic model of the rigid multi-joint textile robotic arm is constructed as follows: Mathematical model of the dynamic system of a rigid articulated robotic arm; whereby... The number of joints, or degrees of freedom, of a robotic arm system.

8. A vision servo trajectory tracking control system for a textile robotic arm based on a fuzzy observer, characterized in that, Includes the following modules: The model building module is used to build a camera model and a dynamic model of a rigid multi-joint textile robot arm with uncertainty and input dead zone; Based on the visual projection model, the relationship between the projection coordinates of the feature points on the image plane and their coordinates in the base coordinate system of the robotic arm is obtained. After differentiation, the relationship between the velocity of the feature points and the angular velocity of the robotic arm joints is obtained. Through coordinate transformation, the dynamic model of the rigid multi-joint textile robotic arm in the image space is obtained. The controller design module is used to design the control of the robotic arm's vision servo system, resulting in a fuzzy adaptive output feedback controller for the robotic arm under unknown vision speed information. The specific design process of the controller is as follows: A fuzzy observer is designed to obtain an estimate of the visual velocity. A fuzzy logic system is used to handle the unknown nonlinear dynamics in the robotic arm's visual servo system. Instruction filtering technology is used to solve the computational complexity problem. An error compensation mechanism is introduced to eliminate the influence of filtering errors. The impact of the input dead zone on system performance is also considered. Finally, a realistic control law is designed. And a trajectory tracking control module, used to achieve trajectory tracking control of the rigid multi-joint textile robotic arm system using a fuzzy adaptive output feedback controller for the robotic arm.

9. A computer device, comprising a memory and one or more processors; characterized in that, The memory stores executable code, which, when executed by the processor, is used to implement the steps of the textile robotic arm visual servo trajectory tracking control method based on a fuzzy observer as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a program stored thereon; characterized in that, When executed by the processor, the program is used to implement the steps of the fuzzy observer-based visual servo trajectory tracking control method for textile robotic arms as described in any one of claims 1 to 7.

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