Fuzzy self-adaptive visual servo control method and system for multi-joint mechanical arm system for placing bobbins in textile workshop

Abstract

The invention belongs to the technical field of position tracking control of rigid multi-joint mechanical arms, and discloses a fuzzy self-adaptive visual servo control method and system for a multi-joint mechanical arm system for placing bobbins in a textile workshop, and the method comprises the steps: constructing an image error model according to the positions of feature points on an image plane; the fuzzy logic system is used for processing uncertain items in the system, the instruction filtering and fuzzy self-adaption technology is used for achieving tracking control over the expected trajectory of the system, the problem of calculation complexity is solved, and adverse effects caused by filtering errors are eliminated. In the process of changing the bobbins, the bobbins are placed on a creel through a multi-degree-of-freedom robotic arm, a clamping jaw at the tail end of the robotic arm is required to accurately grab the bobbins for feeding and discharging, and the requirement for accurate and rapid control of the robotic arm is met, so that research on visual servo control of the robotic arm is of great significance. The method has a good control effect, the problem of calculation complexity in the design process of the controller is solved, the trajectory tracking error is smaller, and the system robustness is higher.

Description

Technical Field

[0001] The present invention belongs to the technical field of position tracking control of rigid multi-joint robotic arms, and particularly relates to a fuzzy adaptive visual servo control method and system for a multi-joint robotic arm system in the context of yarn bobbin placement in a textile workshop. Background Art

[0002] China is a major textile country. The textile industry belongs to the civilian production industry, and the intelligent development of textile equipment is of strategic significance for the upgrading of the manufacturing industry. In the textile production system, the yarn bobbin is the core carrier for storing yarn. In a textile workshop, the use of yarn bobbins greatly facilitates the management and operation of yarn by workers. The yarn rack is a device specifically designed for placing yarn bobbins, and it is usually designed with multiple hooks or brackets so that multiple yarn bobbins can be hung. It is worth noting that the traditional textile workshop still generally adopts the manual operation mode to complete the yarn bobbin picking and placing operations. This labor-intensive production method not only has an efficiency bottleneck, but also is prone to operation stability problems in high-intensity repetitive operation scenarios. Therefore, with the development of technology and the innovation of production methods, textile enterprises introduce automated machinery and intelligent systems to achieve rapid and accurate picking and placing of yarn bobbins. When the working environment is complex, the motion trajectory of the target object is difficult to obtain in advance, which makes the traditional trajectory tracking control scheme often no longer applicable.

[0003] To address this problem, a robotic arm visual servo control scheme that integrates visual perception and control technology has emerged. The robotic arm visual servo control can perceive the target in real time, and then achieve precise positioning and flexible grasping of moving objects in a complex environment. Applying it to fields such as textile production and precision assembly can effectively cope with dynamic changes and uncertain environments, reduce manual intervention, and improve efficiency and safety. Therefore, the research on robotic arm visual servo control has profound engineering significance.

[0004] However, when the robotic arm is in an unknown working environment, the nonlinearity and parameter uncertainty of the robotic arm model cannot be ignored. From the perspective of control, it is difficult to ensure the dynamic performance and stability of robot control by ignoring the nonlinearity in kinematic control. To solve this problem, the fuzzy logic system, as an effective method for dealing with uncertainties and nonlinear terms in the system, has been widely applied to the adaptive control of nonlinear systems.

[0005] On the other hand, the backstepping control method is a nonlinear system control method based on the recursive Lyapunov function, which is widely used in dealing with the control problems of rigid multi-joint manipulators. However, when designing the controller using the backstepping method, it is necessary to continuously differentiate the virtual control function. The traditional backstepping method has computational complexity problems, which will greatly increase the computational amount, resulting in great limitations in the application of the backstepping control strategy for rigid multi-joint manipulators. To overcome this limitation, the instruction filtering technology is combined with the error compensation technology to reduce the interference of the filtering error on the control accuracy and solve the computational complexity problem.

[0006] In the existing research, the research on the instruction filtering fuzzy adaptive control of multi-joint manipulators based on vision is still in a blank state, and there has been no relevant report yet. Therefore, it is of great significance to design a fuzzy adaptive visual servo control method for a rigid multi-joint manipulator system with uncertain dynamics. Summary of the Invention

[0007] The purpose of the present invention is to propose a fuzzy adaptive visual servo control method for a multi-joint manipulator system for placing yarn bobbins in a textile workshop, so as to achieve precise position tracking control of the rigid multi-joint manipulator system.

[0008] In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0009] A fuzzy adaptive visual servo control method for a multi-joint manipulator system for placing yarn bobbins in a textile workshop includes the following steps:

[0010] Step 1. Establish a camera model to analyze and process the feature points at the end of the manipulator, and use it for the subsequent controller design;

[0011] Step 2. Establish a mathematical model of a rigid multi-joint manipulator system with uncertain dynamics;

[0012] Step 3. Based on the mathematical model of the rigid multi-joint manipulator system with uncertain dynamics constructed in Step 2, design a fuzzy adaptive visual servo controller for the rigid multi-joint manipulator system with uncertain dynamics according to the instruction filtering and backstepping method;

[0013] Step 4. Use the fuzzy adaptive visual servo controller of the rigid multi-joint manipulator system with uncertain dynamics to achieve position tracking control of the rigid multi-joint manipulator system.

[0014] In addition, based on the fuzzy adaptive visual servo control method for the multi-joint manipulator system for placing yarn bobbins in a textile workshop, the present invention also proposes a corresponding fuzzy adaptive visual servo control system for the multi-joint manipulator system for placing yarn bobbins in a textile workshop, and its technical solution is as follows:

[0015] A fuzzy adaptive visual servo control system for a multi-joint robotic arm system for placing yarn bobbins in a textile workshop, comprising the following modules:

[0016] A model construction module for establishing a camera model and a mathematical model of a rigid multi-joint robotic arm system with uncertain dynamics;

[0017] A signal analysis and processing module for analyzing and processing the feature points at the end of the robotic arm;

[0018] A controller design module for designing a fuzzy adaptive visual servo controller for a rigid multi-joint robotic arm system with uncertain dynamics based on the mathematical model of the rigid multi-joint robotic arm system with uncertain dynamics according to command filtering and backstepping;

[0019] And a position tracking control module for using the fuzzy adaptive visual servo controller of the rigid multi-joint robotic arm system with uncertain dynamics to achieve position tracking control of the rigid multi-joint robotic arm system.

[0020] In addition, based on the above-mentioned fuzzy adaptive visual servo control method for the multi-joint robotic arm system for placing yarn bobbins in a textile workshop, the present invention also proposes a computer device, which includes a memory and one or more processors.

[0021] An executable code is stored in the memory, and when the processor executes the executable code, it is used to implement the steps of the above-mentioned fuzzy adaptive visual servo control method for the multi-joint robotic arm system for placing yarn bobbins in a textile workshop.

[0022] In addition, based on the above-mentioned fuzzy adaptive visual servo control method for the multi-joint robotic arm system for placing yarn bobbins in a textile workshop, the present invention also proposes a computer-readable storage medium, on which a program is stored. When the program is executed by a processor, it is used to implement the steps of the above-mentioned fuzzy adaptive visual servo control method for the multi-joint robotic arm system for placing yarn bobbins in a textile workshop.

[0023] The present invention has the following advantages:

[0024] As described above, the present invention relates to a fuzzy adaptive visual servo control method for a multi-joint robotic arm system for placing yarn bobbins in a textile workshop. This method constructs an image error model using the position of the feature points at the end of the robotic arm on the image plane, and then designs a control law, thus eliminating the need for three-dimensional reconstruction. This not only simplifies the system design but also avoids error accumulation during the three-dimensional reconstruction process. In addition, the present invention combines an instruction filtering technique and an error compensation mechanism to solve the computational complexity problem during the controller design process, and eliminates the influence of filtering errors, without the need to consider the requirement of high-order differentiability of the desired signal. While achieving a better tracking effect, it can also improve the control accuracy of the system. Additionally, the method of the present invention also considers the uncertain factors in the rigid multi-joint robotic arm system, and uses a fuzzy logic system to approximate the dynamic model of the system to solve the control problem of the rigid multi-joint robotic arm system under uncertain dynamics, making the rigid multi-joint robotic arm system controlled by the method of the present invention have a smaller tracking error. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flowchart of the fuzzy adaptive visual servo control method for the multi-joint robotic arm system for placing yarn bobbins in a textile workshop in an embodiment of the present invention.

[0026] Figure 2 It is a schematic diagram of the robotic arm visual servo system in an embodiment of the present invention.

[0027] Figure 3 It is a system block diagram of the fuzzy adaptive visual servo control method for the multi-joint robotic arm system for placing yarn bobbins in a textile workshop in an embodiment of the present invention.

[0028] Figure 4 It is a schematic diagram of the three-dimensional trajectory of the end effector of the robotic arm obtained by using the control method of the present invention.

[0029] Figure 5 It is a curve graph of the position tracking error obtained by using the control method of the present invention.

[0030] Figure 6 It is a curve graph of the tracking trajectories u and u of the image projection plane obtained by using the control method of the present invention d of.

[0031] Figure 7 It is a curve graph of the tracking trajectories v and v of the image projection plane obtained by using the control method of the present invention d of.

[0032] Figure 8 It is a curve graph of the tracking errors Δu and Δv of the image projection plane obtained by using the control method of the present invention.

[0033] Figure 9The curve graph of the control input τ obtained by using the control method of the present invention.

[0034] Figure 10 The virtual control functions α1 and the filter output x obtained by using the control method of the present invention 1,c curve graph. Detailed implementation manners

[0035] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners:

[0036] Embodiment 1

[0037] For a rigid multi-joint manipulator system, this Embodiment 1 proposes a fuzzy adaptive visual servo control method for a multi-joint manipulator system for placing yarn bobbins in a textile workshop. This method constructs an image error model according to the position of the feature points at the end of the manipulator in the image plane. At the same time, the desired trajectory tracking control of the rigid multi-joint manipulator system is realized by using command filtering and fuzzy adaptive technology, and the fuzzy logic system is used to process the uncertain terms in the system. By using the command filtering technology and introducing an error compensation mechanism, not only the problem of computational complexity is solved, but also the adverse effects brought by the filtering error are eliminated. The method of the present invention also proves the convergence of all variables in the rigid multi-joint manipulator control system through the Lyapunov control principle. The command filtering fuzzy control strategy proposed by the present invention can realize the tracking control of the desired trajectory of the rigid multi-joint manipulator system, solve the problem of computational complexity in the controller design process, has a good control effect on the rigid multi-joint manipulator system, and has a smaller trajectory tracking error and stronger system robustness for the rigid multi-joint manipulator system controlled by the method of the present invention.

[0038] As Figure 1 shown, the fuzzy adaptive visual servo control method for a multi-joint manipulator system for placing yarn bobbins in a textile workshop specifically includes the following steps:

[0039] Step 1. Establish a camera model to analyze and process the feature points at the end of the manipulator, and use it for subsequent controller design.

[0040] As Figure 2 shown, establish three coordinate systems to represent the relationship between the movement of the end of the manipulator and the world coordinate system and the camera coordinate system. The three coordinate systems are respectively the robot basic coordinate system established based on the end effector frame, that is, the manipulator coordinate system, the world coordinate system established based on the manipulator base, and the camera coordinate system established based on the camera frame, that is, the camera coordinate system. Among them, X b represents the x-axis of the world coordinate system, Y b represents the y-axis of the world coordinate system, Z b represents the z-axis of the world coordinate system; X c represents the x-axis of the camera coordinate system, Yc represents the y-axis of the camera coordinate system, Z c represents the z-axis of the camera coordinate system; X e represents the x-axis of the robotic arm coordinate system, Y e represents the y-axis of the robotic arm coordinate system, Z e represents the z-axis of the robotic arm coordinate system.

[0041]

[0042] Among them, the vector x, i.e., x(t), represents the homogeneous coordinates of the feature point at the end of the robotic arm relative to the base of the robotic arm, and the vector q, i.e., q(t), represents the joint angles of the robotic arm. represents the joint angular velocity of the robotic arm, and J(q(t)) represents the Jacobian matrix of the robotic arm.

[0043] The coordinates of the feature point at the end of the robotic arm relative to the camera coordinate system c x(t) is:

[0044] c x(t) = Tx(t) (2)

[0045] Among them, T is the 4×4 homogeneous transformation matrix from the basic coordinate system of the robot to the camera coordinate system, and T represents the external parameters of the camera.

[0046]

[0047] Among them, R represents the rotation matrix and P represents the translation vector.

[0048] Ω is a matrix determined by the internal parameters of the camera:

[0049]

[0050] Among them, α and γ respectively represent the scale factors of the u-axis and v-axis of the image plane. represents the angle between the u-axis and v-axis, and (u0, v0) represents the position of the camera principal point.

[0051] The perspective projection matrix M is:

[0052] M = ΩT (5)

[0053] The depth of the feature point at the end of the robotic arm is given by the following formula:

[0054]

[0055] Among them, c z(q(t)) represents the depth of the feature point at the end of the robotic arm. represents the third row vector of the perspective projection matrix M.

[0056] The homogeneous coordinates y(t) of the feature points at the end of the robotic arm projected onto the image plane are as follows:

[0057]

[0058] Among them, y(t) is the projection position of the end of the robotic arm on the image plane, u(t) represents the u-axis of the image projection plane, and v(t) represents the v-axis of the image projection plane.

[0059] Taking the derivative of formula (7), the following velocity relationship is obtained:

[0060]

[0061] Among them, represents the first row vector of the perspective projection matrix M, represents the second row vector of the perspective projection matrix M.

[0062]

[0063] Among them, J m is the image Jacobian matrix.

[0064]

[0065] J m The pseudo-inverse is:

[0066]

[0067] The establishment process of the camera model corresponds to formulas (1) to (11).

[0068] Step 2. Establish a mathematical model of a rigid multi-joint robotic arm system with uncertain dynamics.

[0069] The dynamic model of a rigid multi-joint robotic arm with n degrees of freedom is expressed as:

[0070]

[0071] Among them, M(q) represents the inertia matrix of the robotic arm, represents the Coriolis force and centripetal force matrix of the robotic arm, G(q) represents the gravity vector of the robotic arm, and τ represents the joint torque of the robotic arm.

[0072] After the following coordinate transformation:

[0073]

[0074] Substitute Equation (13) and Equation (14) into Equation (12) to obtain the mathematical model of the rigid multi-joint robotic arm system with uncertain dynamics:

[0075]

[0076] Where:

[0077]

[0078] Define \(x_1 = y\) and y and respectively represent the projection position and velocity of the end of the robotic arm on the image plane. Equation (15) is rewritten as:

[0079]

[0080] Where \(D\) is \(D(x_1)\), \(K\) is \(K(t)\), \(B\) is \(B(x_1,x_2)\), and \(E\) is \(E(x_1)\).

[0081] Step 3. Based on the mathematical model of the rigid multi-joint robotic arm system with uncertain dynamics constructed in Step 2, design a fuzzy adaptive visual servo controller for the rigid multi-joint robotic arm system with uncertain dynamics according to the command filter and backstepping method to solve the computational complexity problem of the virtual control law in the backstepping design.

[0082] The command filter adopted in the present invention is defined as follows:

[0083]

[0084] Where \(i = 1,2,3\), \(x\) i,c \(= z\) i,1 and are the two output signals of the filter, \(\alpha\) i is the input of the filter, the initial state of the filter \(\alpha\) i (0) \(= z\) i,1 (0), \(z\) i,2 (0) \(= 0\), where \(\alpha\) i (0) is the initial value of \(\alpha\) i , \(z\) i,1 (0) is the initial value of \(z\) i,1 , \(z\) i,2 (0) is the initial value of \(z\) i,2 . When \(t\geq0\), if the input signal satisfies where \(\rho_1\), \(\rho_2\) are constants, then for any \(\mu>0\), there exist \(\xi\in(0,1]\) and \(\omega\) n \(\geq0\) such that \(|x\) i,c - \(\alpha\) i | \(\leq\mu\), are all bounded.

[0085] Construct an image error model and measure the difference between the current position y(t) and the desired position y d to obtain the image error Δy:

[0086] Δy = y(t) - y d (17)

[0087] Define the error variables:

[0088]

[0089] where z1 and z2 represent the error variables, and x d represents the preset desired signal; x 1,c is the output signal of the filter.

[0090] The compensated tracking error variable is defined as:

[0091]

[0092] where v1 and v2 represent the compensated tracking error variables, and v1 and ξ2 are the filter error compensation signals.

[0093] The specific forms of the virtual control law and the filter error compensation signal will be given in the subsequent design process.

[0094] Select the Lyapunov function V1 as: Taking the derivative gives:

[0095]

[0096] Design the virtual control law Δ1 and the error compensation signal ξ1 as:

[0097]

[0098] where k1 is the system control gain, and k1 > 0.

[0099] Substitute Equation (21) into Equation (20) to get:

[0100]

[0101] In Step 3, define the following fuzzy logic system:

[0102] Assume that f(Z) is a continuous function on the compact set Ω z For any constant ε > 0, there always exists a fuzzy logic system W T S(Z) that satisfies: The input vector q is the fuzzy input dimension, and R q is the set of real vectors; W ∈ R nis the fuzzy weight vector, the number of fuzzy nodes n is a positive integer, and n > 1.

[0103] S(Z) = [s1(Z),..., s n (Z)] T ∈R n is the basis function vector, s m (Z) is a Gaussian function, s m (Z)'s expression is:

[0104]

[0105] where, μ m is the center position of the Gaussian function distribution curve, η m is the width of the Gaussian function.

[0106] Define W j as the fuzzy weight vector, j = 1,..., n, n is a constant, n ∈ N + , N + represents the set of positive integers, represents the estimate of θ, the estimation error is

[0107] Select the Lyapunov function V2 as: where r represents a constant, and the derivative is obtained:

[0108]

[0109] According to the fuzzy logic system, for any small constant ε j > 0, there exists a fuzzy logic function S(Z) is the basis function vector, such that where f j (Z) represents the j-th nonlinear fuzzy term in the system, δ j is the approximation error, and δ j ≤ ε j . According to the Young's inequality and v2 = [v 21 ,..., v 2n T where v 21 to v 2n respectively represent the 1st to the n-th components of v2, and we get:

[0110]

[0111] where, f(Z) represents the nonlinear fuzzy term in the rigid multi-joint robotic arm system, h is a constant, and h > 0.

[0112] Design the actual control law τ as:​

[0113]

[0114] Among them, k2 is the system control gain, and k2 > 0.

[0115] Design the compensation signal ξ2 as:

[0116]

[0117] Design the adaptation law as:

[0118]

[0119] Among them, m1 is a constant.

[0120] Substitute formulas (25) to (27) into formula (23) to obtain:

[0121]

[0122] Design the fuzzy adaptive visual servo controller for the rigid multi-joint robotic arm system with uncertain dynamics as:

[0123]

[0124] In step 3 of this embodiment, after completing the design of the fuzzy adaptive visual servo controller for the rigid multi-joint robotic arm system with uncertain dynamics, perform a stability analysis on the rigid multi-joint robotic arm system controlled by the fuzzy adaptive visual servo controller for the rigid multi-joint robotic arm system with uncertain dynamics. The specific process is as follows:

[0125] Select a Lyapunov function for derivation to prove that the system Lyapunov is stable for the system controlled by the fuzzy adaptive visual servo controller for the rigid multi-joint robotic arm system designed in step 3.

[0126] Select the Lyapunov function V:

[0127]

[0128] Take the derivative of formula (29), and substitute formulas (21), (26), and (28) into the derivative of formula (29) to obtain:

[0129]

[0130] Among them:

[0131]

[0132] Obtained from formula (30):

[0133]

[0134] Among them, V(t) represents the Lyapunov function value of V at time t, and V(t0) represents the Lyapunov function value of V at time t0.

[0135] Equation (32) shows that and both belong to the compact set Therefore, all signals of the closed-loop system are bounded. The error compensation signal of the filter satisfies where ρ represents a constant, i1 = 1, 2. Since and is bounded, the tracking error is also bounded.

[0136] Step 4. Use the fuzzy adaptive visual servo controller of the rigid multi-joint robotic arm system with uncertain dynamics to achieve position tracking control of the rigid multi-joint robotic arm system.

[0137] As Figure 3 shows the control flow chart of the control method of the present invention.

[0138] For the rigid multi-joint robotic arm system, the present invention proposes a fuzzy adaptive visual servo control strategy based on command filtering. First, according to the position of the feature point at the end of the robotic arm in the image plane, an image error model is constructed. Aiming at the uncertainty of the robotic arm parameters, the fuzzy logic system is used to approximate the dynamic model of the system, and the control problem of the rigid multi-joint robotic arm system under uncertain dynamics is solved. Secondly, by using the command filtering technology and introducing the error compensation mechanism, not only the computational complexity problem is solved, but also the adverse effects brought by the filtering error are eliminated, and the control effect of the system is improved.

[0139] The method of the present invention can be used in the process of placing the yarn bobbin on the yarn rack by the multi-degree-of-freedom robotic arm for yarn bobbin replacement. Since the gripper at the end of the robotic arm needs to accurately grasp the yarn bobbin for loading and unloading, there is a need for precise and rapid control of the robotic arm. Therefore, it is of great significance to study the visual servo control of the robotic arm. The fuzzy adaptive visual servo control method of the multi-joint robotic arm system for yarn bobbin placement in the textile workshop described in Embodiment 1 of the present invention is particularly suitable for the position tracking control of the multi-joint robotic arm visual servo system with uncertain dynamics.

[0140] To verify the effectiveness of the control method of the present invention, the following parameters are selected for simulation in the rigid multi-joint robotic arm control system:

[0141] M is measured by the robot system toolbox, and the lengths of the three bars of the robotic arm are all 0.15 m.

[0142] Select the reference signal: x d = [2440; 1460].

[0143] Select the controller parameters as: k1 = 340, k2 = 180, h = 10, m1 = 3, r = 0.02.

[0144] Select the filter parameters: ω n = 1100, ξ = 0.28.

[0145] Select the external parameter matrix:

[0146] Figure 4 is the three-dimensional trajectory of the end effector of the robotic arm, and the position where the square is located is the final position of the feature point at the end of the robotic arm. Figure 5 describes the error curve between the position of the feature point at the end of the robotic arm and the desired position, where Δx represents the error on the x-axis, Δy represents the error on the y-axis, and Δz represents the error on the z-axis.

[0147] Figure 6 and Figure 7 are the tracking trajectories of the image projection plane obtained by using the control method of the present invention, where u represents the value of the u-axis on the trajectory of the feature point at the end of the robotic arm in the image projection plane, u d represents the value of the u-axis of the desired trajectory, v represents the value of the v-axis on the trajectory of the feature point at the end of the robotic arm in the image projection plane, and v d represents the value of the v-axis of the desired trajectory. Figure 8 is the tracking error obtained by using the control method of the present invention, where Δu represents the error on the u-axis and Δv represents the error on the v-axis. Figures 6 to 8 The unit of the ordinate in is pixels. It can be seen from Figures 6 to 8 that the control method proposed by the present invention can effectively control the feature point at the end of the robotic arm to reach the desired position, and the rigid multi-joint robotic arm system controlled by the controller designed by using the control method of the present invention has a good tracking effect, with a faster convergence speed and a smaller tracking error.

[0148] Figure 9 represents the control input τ, where τ(1) represents the first element in the control input vector, τ(2) represents the second element in the control input vector, and τ(3) represents the third element in the control input vector. Figure 10 shows that the command filtering has a good filtering effect, where α1(1) represents the first element in the virtual control law α1, and x 1,c (1) represents x 1,cThe first element in, α1(2) represents the second element in the virtual control law α1, x 1,c (2) represents x 1,c The second element in, α1(3) represents the third element in the virtual control law α1, x 1,c (3) represents x 1,c The third element in.

[0149] For the visual servo system of a rigid multi-joint robotic arm, the present invention can well achieve the tracking control of the target trajectory. Not only is the tracking error small, but it can also well handle different degrees of uncertainties in the system model, and the system has strong robustness. In the present invention, the controller adopts the instruction filtering technology and introduces an error compensation mechanism, solves the problem of computational complexity, and eliminates the adverse effects brought by the filtering error, making the errors of the state variables, the desired signals, and the filtered signals converge, so as to achieve a better control effect. The method of the present invention also uses a fuzzy logic system to approximate the uncertain terms in the model, so as to effectively control the robotic arm to reach the desired position. Whether from the theoretical stability proof or from the simulation results, the method of the present invention has achieved the expected goals of improving the control effect and reducing the computational complexity under the condition of uncertain model parameters.

[0150] Embodiment 2

[0151] This Embodiment 2 describes a fuzzy adaptive visual servo control system for a multi-joint robotic arm system for placing yarn bobbins in a textile workshop. This system and the fuzzy adaptive visual servo control method for the multi-joint robotic arm system for placing yarn bobbins in the textile workshop described in the above Embodiment 1 are based on the same inventive concept.

[0152] The fuzzy adaptive visual servo control system for the multi-joint robotic arm system for placing yarn bobbins in the textile workshop specifically includes the following modules:

[0153] A model construction module, used to establish a camera model and a mathematical model of a rigid multi-joint robotic arm system with uncertain dynamics.

[0154] A signal analysis and processing module, used to analyze and process the feature points at the end of the robotic arm.

[0155] A controller design module, used to design a fuzzy adaptive visual servo controller for a rigid multi-joint robotic arm system with uncertain dynamics based on the mathematical model of the rigid multi-joint robotic arm system with uncertain dynamics according to instruction filtering and backstepping.

[0156] And a position tracking control module, used to use the fuzzy adaptive visual servo controller of the rigid multi-joint robotic arm system with uncertain dynamics to achieve the position tracking control of the rigid multi-joint robotic arm system.

[0157] It should be noted that in the fuzzy adaptive visual servo control system of the multi-joint robotic arm for yarn bobbin placement in the textile workshop, the implementation processes of the functions and roles of each functional module are specifically described in the corresponding steps of the fuzzy adaptive visual servo control method of the multi-joint robotic arm for yarn bobbin placement in the textile workshop in the above-mentioned Embodiment 1, and will not be elaborated here.

[0158] Embodiment 3

[0159] This Embodiment 3 describes a computer device, which is used to implement the fuzzy adaptive visual servo control method of the multi-joint robotic arm for yarn bobbin placement in the textile workshop described in the above-mentioned Embodiment 1.

[0160] Specifically, the computer device includes a memory and one or more processors.

[0161] An executable code is stored in the memory. When the processor executes the executable code, it is used to implement the steps of the above-mentioned fuzzy adaptive visual servo control method of the multi-joint robotic arm for yarn bobbin placement in the textile workshop.

[0162] In this embodiment, the computer device is any device or apparatus with data processing capabilities, which will not be elaborated here.

[0163] Embodiment 4

[0164] This Embodiment 4 describes a computer-readable storage medium, which is used to implement the fuzzy adaptive visual servo control method of the multi-joint robotic arm for yarn bobbin placement in the textile workshop described in the above-mentioned Embodiment 1.

[0165] Specifically, in the computer-readable storage medium of this Embodiment 4, a program is stored thereon. When the program is executed by the processor, it is used to implement the steps of the above-mentioned fuzzy adaptive visual servo control method of the multi-joint robotic arm for yarn bobbin placement in the textile workshop.

[0166] 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, a Smart Media Card (SMC), an SD card, a Flash Card, etc. equipped on the device.

[0167] Of course, the above description is only the preferred embodiments of the present invention. The present invention is not limited to listing the above embodiments. It should be noted that all equivalent substitutions and obvious deformation forms made by any person skilled in the art under the guidance of this specification fall within the substantial scope of this specification and should be protected by the present invention.

Claims

1. A fuzzy adaptive visual servo control method for a multi-joint robotic arm system for placing yarn bobbins in a textile workshop, characterized in that, It includes the following steps: Step 1. Establish a camera model to analyze and process the feature points at the end of the robotic arm, and use it for subsequent controller design; Step 2. Establish a mathematical model of a rigid multi-joint robotic arm system with uncertain dynamics; Step 3. Based on the mathematical model of the rigid multi-joint robotic arm system with uncertain dynamics constructed in Step 2, design a fuzzy adaptive visual servo controller for the rigid multi-joint robotic arm system according to command filtering and backstepping method; Step 4. Use the fuzzy adaptive visual servo controller of the rigid multi-joint robotic arm system to achieve position tracking control of the rigid multi-joint robotic arm system.

2. The fuzzy adaptive visual servo control method for the multi-joint robotic arm system for placing yarn bobbins in a textile workshop according to claim 1, characterized in that, The specific content of Step 1 is as follows: Establish a basic coordinate system, a world coordinate system and a camera coordinate system of the robot, which are used to represent the movement of the end of the robotic arm and the relationship between the world coordinate system and the camera coordinate system; Among them, the vector x, that is, x(t), represents the homogeneous coordinates of the characteristic point at the end of the robotic arm relative to the base frame of the robotic arm, and the vector q, that is, q(t), represents the joint angles of the robotic arm. represents the joint angular velocity of the robotic arm, and J(q(t)) represents the Jacobian matrix of the robotic arm; The coordinates of the feature points at the end of the robotic arm relative to the camera coordinate system c x(t) is as follows: c x(t) = Tx(t) (2) Among them, T is the external parameter of the camera, that is, the homogeneous transformation matrix from the basic coordinate system of the robot to the camera coordinate system; Among them, R represents the rotation matrix, and P represents the translation vector; Ω represents the matrix determined by the internal parameters of the camera: where α and γ respectively represent the scale factors of the u-axis and v-axis of the image plane, represents the angle between the u-axis and v-axis of the image plane, and (u0, v0) represents the position of the camera principal point; The perspective projection matrix M is: M = ΩT (5) Depth of the feature point at the end of the robotic arm c z(q(t)) is given by Equation (6): Among them, represents the third row vector of the perspective projection matrix M; The homogeneous coordinates y(t) of the projection of the feature points at the end of the robotic arm on the image plane are: Among them, u(t) represents the value of the u-axis of the feature points at the end of the robotic arm on the image projection plane, and v(t) represents the value of the v-axis of the feature points at the end of the robotic arm on the image projection plane; Derive the formula (7) to obtain the velocity relationship shown in formula (8): Among them, represents the first row vector of the perspective projection matrix M, represents the second row vector of the perspective projection matrix M; Among them, J m is the image Jacobian matrix; J m pseudo-inverse is as follows:

3. The fuzzy adaptive visual servo control method for the multi-joint robotic arm system for placing yarn bobbins in a textile workshop according to claim 2, characterized in that, The specific content of Step 2 is as follows: The dynamic model of a rigid multi-joint robotic arm with n degrees of freedom is: where M1(q) represents the inertial matrix of the robotic arm, represents the Coriolis force and centripetal force matrix of the robotic arm, G(q) represents the gravitational vector of the robotic arm, and τ represents the joint torque of the robotic arm; After coordinate transformation: Substitute formula (13) and formula (14) into formula (12) to obtain the mathematical model of the rigid multi-joint robotic arm system with uncertain dynamics as: Among them: Define x1 = y and Equation (15) is rewritten as: Among them, D is D(x1), K is K(t), B is B(x1,x2), and E is E(x1).

4. The fuzzy adaptive visual servo control method for the multi-joint robotic arm system for placing yarn bobbins in a textile workshop according to claim 3, wherein, The specific content of Step 3 is as follows: Define the command filter as: where i = 1, 2, 3, x i,c = z i,1 and is the output signal of the command filter, ξ and ω n are constants; α i is the input signal of the command filter; Initial state α of the instruction filter i (0) = z i,1 (0), z i,2 (0) = 0, where α i (0) is the initial value of α i and z i,1 (0) is the initial value of z i,1 and z i,2 (0) is the initial value of z i,2 ; When \(t\geq0\), if the input signal satisfies where \(\rho_1\) and \(\rho_2\) are constants, then for any \(\mu>0\), there exist \(\xi\in(0,1]\) and \(\omega\) n \(\geq0\) such that \(|x\) i,c -\alpha i |\leq\mu\), and are all bounded; Construct an image error model and measure the difference between the current position y(t) and the desired position y d to obtain the image error Δy: Δy = y(t) - y d (17) Define the error variable: where z1 and z2 represent error variables, and x d represents a preset desired signal; Define the compensated tracking error variable as: Among them, v1 and v2 represent the compensated tracking error variables, and ξ1 and ξ2 are the filtered error compensation signals; Select the Lyapunov function V1 as follows: Derive V1 to obtain: Design the virtual control law α1 and the error compensation signal ξ1 as: Among them, k1 is the system control gain, and k1 > 0; Substitute formula (21) into formula (20) to get: Define θ = max{||W1|| 2 , ||W2|| 2 ,..., ||W n || 2}, where W j is the fuzzy weight vector, j = 1,..., n, n is a constant, n ∈ N + , N + represents the set of positive integers, denotes the estimate of θ, and the estimation error is Select the Lyapunov function V2 as follows: Among them, r is a constant, and derive V2 to obtain: Obtained according to the fuzzy logic system, for any small constant ε j > 0, there exists a fuzzy logic function such that , where f j (Z) represents the j-th non-linear fuzzy term in the rigid multi-joint robotic arm system, S(Z) is the basis function vector, and δ j is the approximation error, and δ j ≤ ε j ; According to Young's inequality and v2 = [v 21 ,..., v 2n T , where v 21 to v 2n respectively represent the 1st to the n-th components of v2, we get:​ Among them, f(Z) represents the non-linear fuzzy term in the rigid multi-joint robotic arm system, h is a constant, and h > 0; Design the actual control law τ as: Among them, k2 is the system control gain, and k2 > 0; Design the compensation signal ξ2 as: Design adaptation law is as follows: Among them, m1 is a constant; Substitute formula (25) to formula (27) into formula (23) to obtain: The fuzzy adaptive visual servo controller of the rigid multi-joint robotic arm system with uncertain dynamics is designed as:

5. According to the fuzzy adaptive visual servo control method of the multi-joint robotic arm system for placing yarn bobbins in a textile workshop described in claim 1, characterized in that, In step 3, after designing the fuzzy adaptive visual servo controller for the rigid multi-joint manipulator system with uncertain dynamics, a stability analysis is carried out on the rigid multi-joint manipulator system controlled by the fuzzy adaptive visual servo controller for the rigid multi-joint manipulator system with uncertain dynamics.

6. The fuzzy adaptive visual servo control method for the multi-joint manipulator system for placing yarn bobbins in a textile workshop according to claim 4, characterized in that In step 3, the process of performing a stability analysis on the rigid multi-joint manipulator system controlled by the fuzzy adaptive visual servo controller for the rigid multi-joint manipulator system with uncertain dynamics is specifically as follows: Select the Lyapunov function V: Take the derivative of formula (29) and substitute formulas (21), (26), and (28) to obtain: Where: Obtained from formula (30): Among them, \(V(t)\) represents the value of the Lyapunov function of \(V\) at time \(t\), and \(V(t_0)\) represents the value of the Lyapunov function of \(V\) at time \(t_0\). Equation (32) shows that and both belong to the compact set Therefore, all signals of the closed-loop system are bounded; the error compensation signal of the command filter satisfies where ρ represents a constant, i1 = 1, 2; Since and is bounded, the tracking error is also bounded.

7. The fuzzy adaptive visual servo control method for the multi-joint manipulator system for placing yarn bobbins in a textile workshop according to claim 1, characterized in that Step 4 is specifically as follows: Construct an image error model based on the position of the characteristic point at the end of the manipulator in the image plane; for the uncertainty of the manipulator parameters, use a fuzzy logic system to approximate the mathematical model of the rigid multi-joint manipulator system with uncertain dynamics; apply command filtering technology and introduce an error compensation mechanism to solve the problem of computational complexity and eliminate filtering errors, so as to realize the position tracking control of the rigid multi-joint manipulator system.

8. Fuzzy Adaptive Visual Servo Control System for a Multi-Joint Robot Arm System for Placing Yarn Bobbins in a Textile Workshop, characterized in that, It includes the following modules: A model construction module for establishing a camera model and a mathematical model of the rigid multi-joint manipulator system with uncertain dynamics; A signal analysis and processing module for analyzing and processing the characteristic points at the end of the manipulator; A controller design module for designing a fuzzy adaptive visual servo controller for the rigid multi-joint manipulator system with uncertain dynamics based on the mathematical model of the rigid multi-joint manipulator system with uncertain dynamics according to command filtering and backstepping; And a position tracking control module for using the fuzzy adaptive visual servo controller for the rigid multi-joint manipulator system with uncertain dynamics to realize the position tracking control of the rigid multi-joint manipulator system.

9. A computer device, comprising a memory and one or more processors, wherein executable code is stored in the memory, characterized in that, When the processor executes the executable code, the steps of the fuzzy adaptive visual servo control method for the multi-joint manipulator system for placing yarn bobbins in a textile workshop according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a program stored thereon, characterized in that, When this program is executed by the processor, the steps of the fuzzy adaptive visual servo control method for the multi-joint manipulator system for placing yarn bobbins in a textile workshop according to any one of claims 1 to 7 are implemented.

Images