Variable universe fuzzy adaptive impedance control method and system for fracture reduction robot

Abstract

The present invention provides a variable universe fuzzy adaptive impedance control method and system for a fracture reduction robot. The method includes: designing the input and output variables of the fuzzy adaptive impedance controller of the fracture reduction robot; adjusting the universe size of the input variables according to the first quantization factor and the first scaling factor of the end force tracking error, and the second quantization factor and the second scaling factor of the change rate of the end force tracking error; adjusting the universe size of the output variables according to the scale factor of the output variables and the third scaling factor; symmetrically partitioning the input and output variables, determining the membership functions and fuzzy rules of the impedance controller, and using the adjusted universe of the input variables and the universe of the output variables to make the fuzzy adaptive impedance controller output the correction amount of the damping coefficient; and adjusting the initial damping coefficient of the impedance controller by using the correction amount to obtain the adjusted damping coefficient. Based on this method, an adaptive impedance control system is also provided. The present invention improves the fracture reduction accuracy by using variable universe fuzzy adaptive impedance control.

Description

Technical Field

[0001] The present invention belongs to the technical field of automatic control, and particularly relates to a variable universe fuzzy adaptive impedance control method and system for a fracture reduction robot. Background Art

[0002] With the continuous progress of medical technology, the application of robots in the field of orthopedics, especially in fracture reduction surgery, has gradually become a research hotspot. Traditional fracture reduction surgeries usually rely on doctors' experience and manual operations. This method not only has problems such as low surgical precision and long operation time, but also easily leads to the occurrence of postoperative complications. Especially when facing complex or severe fractures, the limitations of traditional surgical methods become more prominent. To improve the precision and efficiency of surgeries, fracture reduction robots have emerged. This robot integrates advanced sensing technologies, precise robotic arm control systems, and intelligent algorithms, and can provide more precise and stable operations during surgeries, thereby significantly improving the surgical quality and accelerating the postoperative recovery of patients. Although current robotic technologies have made certain progress, they still face challenges such as insufficient control precision and weak flexible adaptability. Especially during surgeries, the stretching of patients' muscles and joint movements will cause changes in dynamic loads, increasing the uncertainty in surgical operations. Patients often unconsciously make responses, such as muscle contractions or small movements of joints. These factors make the robot face changing mechanical loads, which may affect its precision and control capabilities. Therefore, how to further improve the adaptability and stability of the robot system in complex biomechanical environments has become an important research direction.

[0003] In fracture reduction robots, impedance control technology is widely used to ensure precision and safety during surgeries. Traditional impedance control adjusts the robot's movement by setting fixed impedance parameters, thereby providing a stable operating environment, especially suitable for relatively simple and standard surgical operations. However, in complex or unforeseen surgical scenarios, traditional impedance control may not be able to flexibly adapt to changes in the patient's body, resulting in insufficient precision and unable to meet high-precision requirements. To solve this problem, fuzzy impedance control has emerged as a more flexible solution. The fuzzy control system adaptively adjusts control parameters, enabling the robot to dynamically respond according to the patient's body characteristics and surgical conditions, thereby significantly improving the precision and safety of surgeries. However, during surgeries, the stretching of patients' muscles and joint movements may cause changes in dynamic loads, resulting in unstable mechanical loads on the robot and increasing the uncertainty of surgeries. Although traditional fuzzy control systems can achieve good control effects when the force error is large, when the force error is small, further improving control precision usually requires adding more fuzzy control rules, which will greatly increase the computational amount and thus reduce the efficiency of the system. Summary of the Invention

[0004] To solve the technical problems of the deficiencies of traditional impedance control and fuzzy impedance control, the present invention proposes a variable universe fuzzy adaptive impedance control method and system for a fracture reduction robot, effectively improving the operation accuracy and stability of the robot in fracture reduction surgery and ensuring the efficiency of the surgical process and the safety of the patient.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A variable universe fuzzy adaptive impedance control method for a fracture reduction robot, comprising the following steps:

[0007] Design the input variables and output variables of the fuzzy adaptive impedance controller of the fracture reduction robot; the input variables include the end force tracking error and the change rate of the end force tracking error of the robot during fracture reduction; the output variable is the correction amount of the damping coefficient of the impedance controller.

[0008] Design a first scaling factor for adjusting the universe size of the end force tracking error, a second scaling factor for adjusting the universe size of the change rate of the end force tracking error, and a third scaling factor for adjusting the universe size of the correction amount of the damping coefficient; adjust the universe size of the input variables according to the first quantization factor of the end force tracking error and the first scaling factor, and the second quantization factor and the second scaling factor of the change rate of the end force tracking error; adjust the universe size of the output variables according to the proportionality factor of the correction amount of the damping coefficient and the third scaling factor.

[0009] Symmetrically divide the input variables and output variables, determine the membership functions and fuzzy rules of the fuzzy adaptive impedance controller, and use the adjusted universe of the input variables and the universe of the output variables to make the fuzzy adaptive impedance controller output the correction amount of the damping coefficient of the impedance controller; use the correction amount of the damping coefficient to adjust the initial damping coefficient of the impedance controller to obtain the adjusted damping coefficient.

[0010] Further, the method further includes: applying the adjusted damping coefficient to the robot impedance controller to dynamically adjust the impedance characteristics of the end effector; and combining the real-time force feedback and the current state of the robot to calculate the desired fracture reduction end position and attitude correction amount, and superimposing it on the current target position; and then based on the inverse kinematics algorithm, calculate the desired angles of each joint and drive the precise movement of each joint of the robot.

[0011] Further, when designing the input variables and output variables of the fuzzy adaptive impedance controller of the fracture reduction robot, it further includes:

[0012] Determine the change range of the end force tracking error of the robot during fracture reduction.

[0013] Determine the range of change rate of the end-effector force tracking error during the fracture reduction process of the robot;

[0014] Determine the universe of discourse range of the damping coefficient correction amount.

[0015] Further, after designing the first scaling factor for adjusting the universe of discourse size of the end-effector force tracking error, the second scaling factor for adjusting the universe of discourse size of the change rate of the end-effector force tracking error, and the third scaling factor for adjusting the universe of discourse size of the damping coefficient correction amount, it further includes: formulating corresponding fuzzy rules for the first scaling factor, the second scaling factor, and the third scaling factor according to the change situation of the end-effector force tracking error of the robot during the fracture reduction process and the requirements for the damping coefficient correction amount of the impedance controller.

[0016] Further, the process of adjusting the universe of discourse size of the input variable according to the first quantization factor and the first scaling factor of the end-effector force tracking error, the second quantization factor and the second scaling factor of the change rate of the end-effector force tracking error; and adjusting the universe of discourse size of the output variable according to the scale factor of the damping coefficient correction amount and the third scaling factor includes:

[0017] Divide the first quantization factor of the end-effector force tracking error of the robot during the fracture reduction process by the first scaling factor;

[0018] Divide the second quantization factor of the change rate of the end-effector force tracking error of the robot during the fracture reduction process by the second scaling factor;

[0019] Multiply the scale factor of the output variable of the fuzzy adaptive impedance controller by the third scaling factor.

[0020] Further, determining the membership function and fuzzy rules of the fuzzy adaptive impedance controller, and using the adjusted universe of discourse of the input variable and the output variable, so that the fuzzy adaptive impedance controller outputs the damping coefficient correction amount of the impedance controller specifically includes:

[0021] Determine the membership function and fuzzy rules of the fuzzy adaptive impedance controller, and the membership function adopts a Gaussian membership function;

[0022] Use Mamdani fuzzy inference and the centroid method to complete fuzzy inference and defuzzification, and obtain the correction amount of the damping coefficient of the robot impedance controller.

[0023] Further, the method also includes:

[0024] Define the same number of fuzzy subsets for both the input variable and the output variable;

[0025] The fuzzy universe of discourse range of the input variable is the first range; the fuzzy universe of discourse range of the output variable is the second range;

[0026] Determine the linguistic variable values and the corresponding fuzzy sets.

[0027] Further, the process of adjusting the initial damping coefficient of the impedance controller by using the correction amount of the damping coefficient to obtain the adjusted damping coefficient is: b = b0 + Δb;

[0028] where b is the adjusted damping coefficient; b0 is the initial damping coefficient; and Δb is the correction amount of the damping coefficient.

[0029] The present invention also proposes a variable universe fuzzy adaptive impedance control system for a fracture reduction robot, including a data acquisition module and an impedance control module;

[0030] The data acquisition module is used to obtain the end force tracking error and the change rate of the end force tracking error during the fracture reduction process of the robot; and the initial damping coefficient of the impedance controller;

[0031] The impedance control module is used to design the input variables and output variables of the fuzzy adaptive impedance controller of the fracture reduction robot; the input variables include the end force tracking error and the change rate of the end force tracking error during the fracture reduction process of the robot; the output variable is the correction amount of the damping coefficient of the impedance controller; design a first scaling factor for adjusting the size of the universe of discourse of the end force tracking error, a second scaling factor for adjusting the size of the universe of discourse of the change rate of the end force tracking error, and a third scaling factor for adjusting the size of the universe of discourse of the correction amount of the damping coefficient; adjust the size of the universe of discourse of the input variables according to the first quantization factor and the first scaling factor of the end force tracking error, and the second quantization factor and the second scaling factor of the change rate of the end force tracking error; adjust the size of the universe of discourse of the output variable according to the proportionality factor of the correction amount of the damping coefficient and the third scaling factor; symmetrically divide the input variables and output variables, determine the membership functions and fuzzy rules of the fuzzy adaptive impedance controller, use the adjusted universe of discourse of the input variables and the universe of discourse of the output variables, so that the fuzzy adaptive impedance controller outputs the correction amount of the damping coefficient of the impedance controller; use the correction amount of the damping coefficient to adjust the initial damping coefficient of the impedance controller to obtain the adjusted damping coefficient.

[0032] Further, the system further includes an execution module;

[0033] The execution module is used to receive the adjusted damping coefficient, apply the adjusted damping coefficient to the robot impedance controller to dynamically adjust the impedance characteristics of the end effector; and combine the real-time force feedback with the current state of the robot, calculate the desired fracture reduction end position and attitude correction amount, and superimpose it on the current target position; then based on the inverse kinematics algorithm, calculate the desired angles of each joint and drive the precise movement of each joint of the robot.

[0034] The effects provided in the summary of the invention are only the effects of the embodiments, rather than all the effects of the invention. One of the above technical solutions has the following advantages or beneficial effects:

[0035] The present invention proposes a variable universe fuzzy adaptive impedance control method and system for a fracture reduction robot. The method comprises the following steps: designing input variables and output variables of a fuzzy adaptive impedance controller of the fracture reduction robot; the input variables include the end-effector force tracking error and the change rate of the end-effector force tracking error during the fracture reduction process by the robot; the output variable is the correction amount of the damping coefficient of the impedance controller.

[0036] Designing a first scaling factor for adjusting the size of the universe of discourse of the end-effector force tracking error, a second scaling factor for adjusting the size of the universe of discourse of the change rate of the end-effector force tracking error, and a third scaling factor for adjusting the size of the universe of discourse of the correction amount of the damping coefficient; adjusting the size of the universe of discourse of the input variables according to the first quantization factor and the first scaling factor of the end-effector force tracking error, and the second quantization factor and the second scaling factor of the change rate of the end-effector force tracking error; adjusting the size of the universe of discourse of the output variable according to the proportionality factor of the correction amount of the damping coefficient and the third scaling factor; symmetrically partitioning the input variables and the output variable, determining the membership functions and fuzzy rules of the fuzzy adaptive impedance controller, and using the adjusted universe of discourse of the input variables and the output variable to enable the fuzzy adaptive impedance controller to output the correction amount of the damping coefficient of the impedance controller; using the correction amount of the damping coefficient to adjust the initial damping coefficient of the impedance controller to obtain the adjusted damping coefficient. Based on the variable universe fuzzy adaptive impedance control method for a fracture reduction robot, a variable universe fuzzy adaptive impedance control method system for a fracture reduction robot is also proposed. The variable universe fuzzy adaptive impedance control used in the present invention can effectively improve the response speed and control accuracy of the system, while reducing the overshoot and adjustment time.

[0037] The present invention enables the system to adaptively adjust according to the actual situation of the patient and the changes in the surgical environment by dynamically adjusting the universe of discourse range in the fuzzy control system, thereby optimizing the control effect. By shrinking or expanding the fuzzy universe of discourse, without adding control rules, this method uses the idle fuzzy subsets for more precise control, maintains a high control accuracy in the case of small errors, and significantly improves the computational efficiency.

[0038] Compared with the prior art, by introducing variable universe fuzzy control, the present invention can adjust the scaling factors in real time according to the working state or parameter changes of the robot, thereby dynamically optimizing the control range. This adaptive adjustment mechanism enables the robot to more flexibly respond to different operating conditions during the fracture reduction process, providing a more precise reduction effect than traditional control methods. This improvement significantly enhances the adaptive ability of the system, ensuring that the robot can quickly adjust the reduction strategy at critical moments, thereby improving the safety and stability during the surgical process. Description of the Drawings

[0039] Figure 1Flow chart of the variable universe fuzzy adaptive impedance control method for the fracture reduction robot proposed in Embodiment 1 of the present invention;

[0040] Figure 2 Schematic structural diagram of the robot fracture reduction surgery simulation proposed in Embodiment 1 of the present invention;

[0041] Figure 3 Principle diagram of the adaptive impedance control using variable universe fuzzy control proposed in Embodiment 1 of the present invention;

[0042] Figure 4 Model block diagram of the adaptive impedance control using variable universe fuzzy control proposed in Embodiment 1 of the present invention;

[0043] Figure 5 Internal simulation model diagram of the adaptive impedance control algorithm using variable universe fuzzy control proposed in Embodiment 1 of the present invention;

[0044] Figure 6 Trajectory tracking curve of the end of the robotic arm in the X direction in the simulation experiment proposed in Embodiment 1 of the present invention;

[0045] Figure 7 Trajectory tracking curve of the end of the robotic arm in the Y direction in the simulation experiment proposed in Embodiment 1 of the present invention;

[0046] Figure 8 Trajectory tracking curve of the end of the robotic arm in the Z direction in the simulation experiment proposed in Embodiment 1 of the present invention;

[0047] Figure 9 Schematic diagram of the variable universe fuzzy adaptive impedance control system for the fracture reduction robot proposed in Embodiment 2 of the present invention. Detailed implementation manners

[0048] To clearly illustrate the technical features of this solution, the present invention will be elaborated in detail below through specific implementation manners and in conjunction with its accompanying drawings. The following disclosure provides many different embodiments or examples for implementing different structures of the present invention. To simplify the disclosure of the present invention, the components and settings of specific examples are described below. In addition, the present invention may repeat reference numerals and / or letters in different examples. This repetition is for the purpose of simplification and clarity, and does not itself indicate the relationship between the various embodiments and / or settings discussed. It should be noted that the components illustrated in the drawings are not necessarily drawn to scale. The present invention omits the description of well-known components and processing technologies and processes to avoid unnecessarily limiting the present invention.

[0049] Embodiment 1

[0050] Embodiment 1 of the present invention proposes a variable universe fuzzy adaptive impedance control method for a fracture reduction robot, which is used to solve the problems encountered by surgical robots in actual operations in the prior art. The present invention is particularly applicable to the treatment of complex fractures with extremely high requirements for surgical precision, and provides a more accurate and stable control means for fracture reduction surgery.

[0051] Figure 1 It is a flowchart of the variable universe fuzzy adaptive impedance control method for the fracture reduction robot proposed in Embodiment 1 of the present invention;

[0052] In step S100, the input variables and output variables of the fuzzy adaptive impedance controller of the fracture reduction robot are designed; the input variables include the end-effector force tracking error and the change rate of the end-effector force tracking error during the fracture reduction process of the robot; the output variable is the correction amount of the damping coefficient of the impedance controller.

[0053] In the present invention, the end-effector force tracking error and the change rate of the end-effector force tracking error during the fracture reduction process are obtained by a force sensor installed at the end of the robotic arm. Figure 2 It is a schematic structural diagram of the robotic fracture reduction surgery simulation proposed in Embodiment 1 of the present invention;

[0054] Figure 3 It is a schematic diagram of the principle of adaptive impedance control using variable universe fuzzy control in Embodiment 1 of the present invention; the input variables e, ec and the output variable Δb of the fuzzy adaptive impedance controller; where the input variable e represents the end-effector force tracking error during the fracture reduction process of the robot; the input variable ec represents the change rate of the end-effector force tracking error during the fracture reduction process of the robot; the output variable Δb represents the correction amount of the damping coefficient of the impedance controller. The error range of the end-effector force tracking error e of the robot is set to [-6, 6], and the change rate range of its error ec is [-6, 6]; the universe range of the output variable Δb is set to [-5, 30].

[0055] In step S200, a first scaling factor for adjusting the universe size of the end-effector force tracking error, a second scaling factor for adjusting the universe size of the change rate of the end-effector force tracking error, and a third scaling factor for adjusting the universe size of the correction amount of the damping coefficient are designed; the universe size of the input variables is adjusted according to the first quantization factor and the first scaling factor of the end-effector force tracking error, and the second quantization factor and the second scaling factor of the change rate of the end-effector force tracking error; the universe size of the output variable is adjusted according to the proportionality factor of the correction amount of the damping coefficient and the third scaling factor.

[0056] Design the first scaling factor a of the input variable e of the fuzzy adaptive impedance controller e , design the second scaling factor a of the input variable ec ec , design the third scaling factor β of the output variable Δb b . ae The first scaling factor a for adjusting the size of the fuzzy universe of discourse of the input variable e ec The second scaling factor β for adjusting the size of the fuzzy universe of discourse of the input variable ec b The third scaling factor for adjusting the size of the fuzzy universe of discourse of the output variable Δb

[0057] The first scaling factor a e and the second scaling factor a ec and the third scaling factor β b are obtained by fuzzy inference: According to the requirements for the correction amount Δb of the damping coefficient of the impedance controller based on the changes in the end-effector force tracking error and the change rate of the end-effector force tracking error during the fracture reduction operation process, a e and a ec and β b are determined accordingly. When the end-effector force tracking error e of the robot during the fracture reduction process and the change rate ec of the end-effector force tracking error of the robot during the fracture reduction process are large, the system faces a large control error, which may lead to excessive overshoot. To effectively reduce this overshoot phenomenon and enhance the anti-interference ability of the system, the damping coefficient b should be set to a large value. Increasing the damping coefficient can improve the stability of the system, making it show stronger resistance when facing external disturbances, thus avoiding violent fluctuations. On the contrary, when the end-effector force tracking error e of the robot during the fracture reduction process and the change rate ec of the end-effector force tracking error of the robot during the fracture reduction process are small, the control error of the system is relatively small. At this time, it is necessary to improve the response speed of the control system to adjust to the desired state more quickly. In this case, the damping coefficient b should be set to a small value. Reducing the damping coefficient can make the response of the system more rapid, reduce the delay, so that the control system can adapt to input changes more quickly.

[0058] Perform universe of discourse adjustment, i.e., variable universe of discourse; divide the first quantization factor K of the input variable e e by the first scaling factor a e , divide the second quantization factor K of the input variable ec ec by the second scaling factor a ec , and multiply the scale factor K of the output variable Δb u by the third scaling factor β b . Figure 4 is the model block diagram of the adaptive impedance control using variable universe of discourse fuzzy control proposed in Embodiment 1 of the present invention;

[0059] The first quantization factor K e converts the basic universe of discourse range of the actual input variable e to the fuzzy universe of discourse range of the input variable e; The second quantization factor K ec converts the basic universe of discourse range of the actual input variable ec to the fuzzy universe of discourse range of the input variable ec; The scale factor Ku Convert the fuzzy universe range of the output variable Δb to the basic universe range of the output variable Δb.

[0060] Using variable universe fuzzy adaptive impedance control can effectively improve the response speed and control accuracy of the system, while reducing the overshoot and adjustment time. This control method enables the robot to better adapt to the changes in the patient's muscles and soft tissues, thus effectively preventing secondary injuries to the patient during the operation.

[0061] In step S300, symmetrically divide the input variable and the output variable, determine the membership function and fuzzy rules of the fuzzy adaptive impedance controller, use the adjusted input variable universe and output variable universe, so that the fuzzy adaptive impedance controller outputs the damping coefficient correction amount of the impedance controller; use the correction amount of the damping coefficient to adjust the initial damping coefficient of the impedance controller to obtain the adjusted damping coefficient.

[0062] For a e 、a ec and β b When using fuzzy inference, the input variables are still the end force tracking error e and its error change rate ec of the robot during fracture reduction, and the output variables are the first scaling factor a e 、the second scaling factor a ec and the third scaling factor β b . Both the input and output state variables are symmetrically divided, and the membership function is selected as a Gaussian function; the fuzzy universes of the input variables, the end force tracking error e of the robot and its error change rate ec, are both [□6, 6], and the linguistic values are represented by {NB, NM, NS, ZO, PS, PM, PB}; the linguistic values of a e 、a ec are represented by {large, medium, small, very small}, that is, {B, M, S, VS}. The linguistic values of the output state variable β b are represented by {very large, large, medium, small, very small}, that is, {VB, B, M, S, VS}, and the ranges of the output variables a e 、a ec and β b are [0, 1]; according to the above change rules and universe division, summarize the fuzzy rules for obtaining the first scaling factor a e and the second scaling factor a ec as:

[0063] e / ec NB NM NS ZO PS PM PB NB B B M M M B B NM B M M S M M B NS M M S VS S M M ZO M S VS VS VS S M PS M M S VS S M M PM B M M S M M B PB B B M M M B B

[0064] The fuzzy rules for the third scaling factor β b are:

[0065] e / ec NB NM NS ZO PS PM PB NB VB VB B M B VB VB NM VB B M S M B VB NS B M S VS S M B ZO M S VS VS VS S M PS B M S VS S M B PM VB B M S M B VB PB VB VB B M B VB VB

[0066] The input variables e, ec and the output variable Δb are all defined as 7 fuzzy subsets, and the fuzzy universe ranges are all [□6, 6]. The linguistic variable values are represented by {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, and the corresponding fuzzy sets are {NB, NM, NS, ZO, PS, PM, PB}; the fuzzy rules of Δb are obtained according to the impedance parameter adjustment law and combined with simulation experience as follows:

[0067] e / ec NB NM NS ZO PS PM PB NB PB PB PM NB NM NS ZO NM PB PM PS NM NS ZO ZO NS PM PS PS NS ZO PS PS ZO PB PM PS ZO PS PM PB PS PS PS ZO NS PS PS PM PM ZO ZO NS NM PS PM PB PB ZO NS NM NB PM PB PB

[0068] Using Mamdani fuzzy inference and defuzzifying with the centroid method to obtain the clear value Δb of the impedance parameter correction amount.

[0069] The process of adjusting the initial damping coefficient of the impedance controller by using the correction amount of the damping coefficient to obtain the adjusted damping coefficient is: b = b0 + Δb;

[0070] Among them, b is the adjusted damping coefficient; b0 is the initial damping coefficient; Δb is the correction amount of the damping coefficient.

[0071] In step S400, apply the adjusted damping coefficient to the robot impedance controller to dynamically adjust the impedance characteristics of the end effector; and combine the real-time force feedback with the current state of the robot to calculate the expected fracture reduction end position and attitude correction amount, and superimpose it on the current target position; then based on the inverse kinematics algorithm, calculate the expected angles of each joint and drive the joints of the robot to move precisely. Ensure that the robot has adaptability, stability and precision when interacting with the environment, and finally complete the fracture reduction surgery task.

[0072] To fully illustrate the variable universe fuzzy adaptive impedance control method for the fracture reduction robot proposed in Embodiment 1 of the present invention, the present invention uses a simulation model to illustrate the implementation process of the present invention.

[0073] Figure 5 It is the internal simulation model diagram of the adaptive impedance control algorithm using variable universe fuzzy control proposed in Embodiment 1 of the present invention; during the simulation process, it is necessary to first set the external force simulation application module and the desired trajectory planning module. The external force simulation application module is mainly used to simulate the external force generated by muscles and soft tissues during the fracture reduction process. Because it is impossible to directly obtain the force and force error rate of the real manipulator in the simulation, a force signal can be simulated and input through this external force simulation application module. The desired trajectory planning module can simulate the robot to plan a specific reduction path through the vision system and guide the robot to move along the predetermined path.

[0074] The simulation system also includes an impedance algorithm control module and a robotic arm subsystem; the impedance algorithm control module is used to execute the processes in steps S100 to S300. The robotic arm subsystem includes the joints and linkages of the robot, first outputs the motion information in the joint space, and then converts it into the pose information in the Cartesian space through the forward kinematics model. In the impedance algorithm control module, a fuzzy controller is constructed using the Fuzzy Control toolbox in Matlab, and the scaling factor is selected through the fuzzy inference method and added to the input and output of the fuzzy controller. Combining the variable universe fuzzy controller and the impedance controller, an adaptive impedance controller based on variable universe fuzzy control is formed. This controller converts the external force acting on the robot into a pose correction amount through an algorithm, superimposes the correction amount on the current position, and finally obtains the joint angles through inverse kinematics solution to control the motion of the robot.

[0075] To verify that the adjustment performance of the robotic fracture reduction using the adaptive impedance control method based on variable universe fuzzy of the present invention is superior to that of the traditional impedance control and the adjustment performance under the combination of general fuzzy control and impedance control, while ensuring that all parameters of the system are the same, during the operation of the system, a 20N positive external force is applied at 2s to simulate the diastolic state of the muscle, and a 30N negative external force is applied at 7s to simulate the contraction state of the muscle. Observe the trajectory tracking curves of the three methods, and use MATLAB simulation to compare the adjustment performance of the three control methods: Figure 6 This is the trajectory tracking curve of the end of the robotic arm in the X direction in the simulation experiment proposed in Embodiment 1 of the present invention. Figure 6 The trajectory tracking curves of traditional impedance control, fuzzy control, and variable universe fuzzy control are given when the end of the robotic arm in the X direction is subjected to a 20N positive external force at 2s and a 30N negative external force at 7s in the simulation experiment. Figure 7 This is the trajectory tracking curve of the end of the robotic arm in the Y direction in the simulation experiment proposed in Embodiment 1 of the present invention. Figure 7 The trajectory tracking curves of traditional impedance control, fuzzy control, and variable universe fuzzy control are given when the end of the robotic arm in the Y direction is subjected to a 20N positive external force at 2s and a 30N negative external force at 7s in the simulation experiment. Figure 8 This is the trajectory tracking curve of the end of the robotic arm in the Z direction in the simulation experiment proposed in Embodiment 1 of the present invention. Figure 8 The trajectory tracking curves of traditional impedance control, fuzzy control, and variable universe fuzzy control are given when the end of the robotic arm in the Z direction is subjected to a 20N positive external force at 2s and a 30N negative external force at 7s in the simulation experiment. From Figure 6 , Figure 7 and Figure 8From the comparison chart of the simulation waveforms, it can be seen that compared with the traditional impedance control and the combination of general fuzzy control and impedance control, the variable universe fuzzy adaptive impedance control can effectively improve the response speed and control accuracy of the system, while reducing the overshoot and adjustment time.

[0076] The variable universe fuzzy adaptive impedance control method for the fracture reduction robot proposed in Embodiment 1 of the present invention optimizes the control effect by dynamically adjusting the universe range in the fuzzy control system, enabling the system to adaptively adjust according to the actual situation of the patient and the changes in the surgical environment. By shrinking or expanding the fuzzy universe, this method can utilize the idle fuzzy subsets for more precise control without increasing the control rules, maintain a high control accuracy under small errors, and significantly improve the calculation efficiency.

[0077] The variable universe fuzzy adaptive impedance control method for the fracture reduction robot proposed in Embodiment 1 of the present invention can dynamically optimize the control range by introducing variable universe fuzzy control and adjusting the scaling factor in real time according to the working state or parameter changes of the robot. This adaptive adjustment mechanism enables the robot to more flexibly respond to different operating conditions during the fracture reduction process, providing a more accurate reduction effect than traditional control methods. This improvement significantly enhances the adaptive ability of the system, ensuring that the robot can quickly adjust the reduction strategy at critical moments, thereby improving the safety and stability during the surgical process.

[0078] Embodiment 2

[0079] Based on the variable universe fuzzy adaptive impedance control method for the fracture reduction robot proposed in Embodiment 1 of the present invention, Embodiment 2 of the present invention also proposes a variable universe fuzzy adaptive impedance control system for the fracture reduction robot Figure 9 FIG. is a schematic diagram of the variable universe fuzzy adaptive impedance control system for the fracture reduction robot proposed in Embodiment 2 of the present invention. The system includes a data acquisition module and an impedance control module;

[0080] The data acquisition module is used to acquire the end-effector force tracking error and the change rate of the end-effector force tracking error during the fracture reduction process of the robot; and the initial damping coefficient of the impedance controller;

[0081] In the present invention, the end-effector force tracking error and the change rate of the end-effector force tracking error during the fracture reduction process are acquired by a force sensor installed at the end of the robotic arm.

[0082] The impedance control module is used to design the input variables and output variables of the fuzzy adaptive impedance controller for the fracture reduction robot; the input variables include the end - force tracking error and the change rate of the end - force tracking error during the fracture reduction process of the robot; the output variable is the correction amount of the damping coefficient of the impedance controller; design a first scaling factor for adjusting the domain size of the end - force tracking error, a second scaling factor for adjusting the domain size of the change rate of the end - force tracking error, and a third scaling factor for adjusting the domain size of the correction amount of the damping coefficient; adjust the domain sizes of the input variables according to the first quantization factor and the first scaling factor of the end - force tracking error, the second quantization factor and the second scaling factor of the change rate of the end - force tracking error; adjust the domain size of the output variable according to the proportionality factor of the correction amount of the damping coefficient and the third scaling factor; symmetrically divide the input variables and output variables, determine the membership functions and fuzzy rules of the fuzzy adaptive impedance controller, and use the adjusted domain sizes of the input variables and output variables to make the fuzzy adaptive impedance controller output the correction amount of the damping coefficient of the impedance controller; use the correction amount of the damping coefficient to adjust the initial damping coefficient of the impedance controller to obtain the adjusted damping coefficient.

[0083] In the impedance control module, when designing the input variables and output variables of the fuzzy adaptive impedance controller for the fracture reduction robot, it also includes: determining the variation range of the end - force tracking error of the robot during the fracture reduction process; determining the variation range of the change rate of the end - force tracking error of the robot during the fracture reduction process; determining the domain range of the correction amount of the damping coefficient.

[0084] After designing the first scaling factor for adjusting the domain size of the end - force tracking error, the second scaling factor for adjusting the domain size of the change rate of the end - force tracking error, and the third scaling factor for adjusting the domain size of the correction amount of the damping coefficient, it also includes: formulating the corresponding fuzzy rules for the first scaling factor, the second scaling factor, and the third scaling factor according to the variation of the end - force tracking error of the robot at the end during the fracture reduction process and the requirements for the correction amount of the damping coefficient of the impedance controller.

[0085] Divide the first quantization factor of the end - force tracking error of the robot during the fracture reduction process by the first scaling factor; divide the second quantization factor of the change rate of the end - force tracking error of the robot during the fracture reduction process by the second scaling factor; multiply the proportionality factor of the output variable of the fuzzy adaptive impedance controller by the third scaling factor.

[0086] Determine the membership functions and fuzzy rules of the fuzzy adaptive impedance controller. Using the adjusted universes of discourse of the input variables and the output variables, make the fuzzy adaptive impedance controller output the correction amount of the damping coefficient of the impedance controller. Specifically, it includes: determining the membership functions and fuzzy rules of the fuzzy adaptive impedance controller, and the membership functions adopt Gaussian membership functions; using Mamdani fuzzy inference and the centroid method to complete fuzzy inference and defuzzification, and obtaining the correction amount of the damping coefficient of the robot impedance controller.

[0087] The same number of fuzzy subsets are defined for both the input variables and the output variables; the range of the fuzzy universe of discourse of the input variables is the first range; the range of the fuzzy universe of discourse of the output variables is the second range; determine the values of the linguistic variables and the corresponding fuzzy sets.

[0088] The process of using the correction amount of the damping coefficient to adjust the initial damping coefficient of the impedance controller to obtain the adjusted damping coefficient is: b = b0 + Δb;

[0089] where, b is the adjusted damping coefficient; b0 is the initial damping coefficient; Δb is the correction amount of the damping coefficient.

[0090] This system further includes an execution module; the execution module is used to receive the adjusted damping coefficient, apply the adjusted damping coefficient to the robot impedance controller to achieve dynamic adjustment of the impedance characteristics of the end effector; and combine real-time force feedback with the current state of the robot to calculate the desired fracture reduction end position and attitude correction amount, and superimpose it on the current target position; then based on the inverse kinematics algorithm, calculate the desired angles of each joint, and drive each joint of the robot to move precisely to ensure that the robot has adaptability, stability and accuracy when interacting with the environment, and finally complete the fracture reduction surgery task.

[0091] The variable universe of discourse fuzzy adaptive impedance control system of the fracture reduction robot proposed in Embodiment 2 of the present invention can effectively improve the response speed and control accuracy of the system by using variable universe of discourse fuzzy adaptive impedance control, while reducing the overshoot and adjustment time.

[0092] The variable universe of discourse fuzzy adaptive impedance control system of the fracture reduction robot proposed in Embodiment 2 of the present invention can adaptively adjust according to the actual situation of the patient and the changes in the surgical environment by dynamically adjusting the universe of discourse range in the fuzzy control system, so as to optimize the control effect. By shrinking or expanding the fuzzy universe of discourse, this method can use the idle fuzzy subsets for more precise control without increasing the control rules, maintain a high control accuracy under a small error, and significantly improve the calculation efficiency.

[0093] The variable universe fuzzy adaptive impedance control system of the fracture reduction robot proposed in Embodiment 2 of the present invention can, by introducing variable universe fuzzy control, adjust the scaling factor in real time according to the working state or parameter changes of the robot, thereby dynamically optimizing the control range. This adaptive adjustment mechanism enables the robot to more flexibly respond to different operating conditions during the fracture reduction process, providing a more accurate reduction effect than traditional control methods. This improvement significantly enhances the adaptive ability of the system, ensuring that the robot can quickly adjust the reduction strategy at critical moments, thereby improving the safety and stability during the operation.

[0094] For the description of the relevant part of the impedance control module in the variable universe fuzzy adaptive impedance control system of the fracture reduction robot provided in Embodiment 2 of the present invention, reference can be made to the detailed description of the corresponding part in the method of the variable universe fuzzy adaptive impedance control method of the fracture reduction robot provided in Embodiment 1 of the present invention, which will not be elaborated here.

[0095] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that the elements inherent in a process, method, article or device comprising a series of elements are included. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the said element. In addition, the parts of the above technical solutions provided in the embodiments of the present invention that are consistent with the corresponding technical solutions in the prior art in terms of implementation principles are not described in detail to avoid excessive elaboration.

[0096] Although the specific implementation manners of the present invention have been described above in conjunction with the drawings, they are not limitations on the protection scope of the present invention. For those skilled in the art, other different forms of modifications or deformations can be made on the basis of the above description. It is not necessary and impossible to enumerate all the implementation manners here. All modifications or deformations that can be made by those skilled in the art without creative efforts on the basis of the technical solutions of the present invention are still within the protection scope of the present invention.

Claims

1. Variable universe fuzzy adaptive impedance control method for fracture reduction robot, characterized in that, Including the following steps: Design the input variables and output variables of the fuzzy adaptive impedance controller of the fracture reduction robot; the input variables include the end force tracking error and the change rate of the end force tracking error of the robot during the fracture reduction process; The output variable is the damping coefficient correction amount of the impedance controller; Design a first scaling factor for adjusting the size of the end force tracking error domain, a second scaling factor for adjusting the size of the change rate domain of the end force tracking error, and a third scaling factor for adjusting the size of the damping coefficient correction amount domain; Adjust the size of the input variable domain according to the first quantization factor and the first scaling factor of the end force tracking error, and the second quantization factor and the second scaling factor of the change rate of the end force tracking error; Adjust the size of the output variable domain according to the proportionality factor of the damping coefficient correction amount and the third scaling factor; The first scaling factor, the second scaling factor, and the third scaling factor are obtained by fuzzy inference. Specifically: according to the requirements for the damping coefficient correction amount of the impedance controller based on the changes in the end force tracking error and the change rate of the force tracking error of the robot during the fracture reduction operation process, formulate the corresponding fuzzy rules for the first scaling factor, the second scaling factor, and the third scaling factor; The first quantization factor is used to convert the basic domain range of the end force tracking error of the robot during the fracture reduction process into the fuzzy domain range; The second quantization factor is used to convert the basic domain range of the change rate of the end force tracking error of the robot during the fracture reduction process into the fuzzy domain range; The proportionality factor is used to convert the fuzzy domain range of the damping coefficient correction amount of the impedance controller into the basic domain range; Symmetrically divide the input variables and output variables, determine the membership functions and fuzzy rules of the fuzzy adaptive impedance controller, and use the adjusted input variable domain and output variable domain to make the fuzzy adaptive impedance controller output the damping coefficient correction amount of the impedance controller; Adjust the initial damping coefficient of the impedance controller by using the damping coefficient correction amount to obtain the adjusted damping coefficient; The method further includes: applying the adjusted damping coefficient to the robot impedance controller to realize dynamic adjustment of the impedance characteristics of the end effector; And combining the real-time force feedback with the current state of the robot, calculating the desired fracture reduction end position and attitude correction amount, and superimposing it on the current target position; then based on the inverse kinematics algorithm, calculating the desired angles of each joint and driving the precise movement of each joint of the robot.

2. The variable universe fuzzy adaptive impedance control method for a fracture reduction robot according to claim 1, characterized in that When designing the input variables and output variables of the fuzzy adaptive impedance controller of the fracture reduction robot, it further includes: Determine the change range of the end force tracking error of the robot during the fracture reduction process; Determine the change range of the change rate of the end force tracking error of the robot during the fracture reduction process; Determine the domain range of the damping coefficient correction amount.

3. The variable universe fuzzy adaptive impedance control method for a fracture reduction robot according to claim 1, characterized in that After designing the first scaling factor for adjusting the domain size of the end-effector force tracking error, the second scaling factor for adjusting the domain size of the change rate of the end-effector force tracking error, and the third scaling factor for adjusting the domain size of the damping coefficient correction amount, it further includes: formulating corresponding fuzzy rules for the first scaling factor, the second scaling factor, and the third scaling factor according to the change of the end-effector force tracking error during the fracture reduction process and the requirement for the damping coefficient correction amount of the impedance controller.

4. The variable universe fuzzy adaptive impedance control method for a fracture reduction robot according to claim 3, wherein, Adjust the domain size of the input variables according to the first quantization factor of the end-effector force tracking error and the first scaling factor, and the second quantization factor of the change rate of the end-effector force tracking error and the second scaling factor; The process of adjusting the domain size of the output variable according to the proportionality factor of the damping coefficient correction amount and the third scaling factor includes: Dividing the first quantization factor of the end-effector force tracking error of the robot during the fracture reduction process by the first scaling factor; Dividing the second quantization factor of the change rate of the end-effector force tracking error of the robot during the fracture reduction process by the second scaling factor; Multiplying the proportionality factor of the output variable of the fuzzy adaptive impedance controller by the third scaling factor.

5. The variable universe fuzzy adaptive impedance control method for a fracture reduction robot according to claim 1, characterized in that Determine the membership function and fuzzy rules of the fuzzy adaptive impedance controller, and use the adjusted input variable domain and output variable domain to make the fuzzy adaptive impedance controller output the damping coefficient correction amount of the impedance controller, specifically including: Determine the membership function and fuzzy rules of the fuzzy adaptive impedance controller, and the membership function adopts a Gaussian membership function; Use Mamdani fuzzy inference and the centroid method to complete fuzzy inference and defuzzification, and obtain the correction amount of the damping coefficient of the robot impedance controller.

6. The variable universe fuzzy adaptive impedance control method for a fracture reduction robot according to claim 5, wherein The method also includes: Define the same number of fuzzy subsets for both the input variable and the output variable; The fuzzy domain range of the input variable is the first range; the fuzzy domain range of the output variable is the second range; Determine the language variable values and the corresponding fuzzy sets.

7. The variable universe fuzzy adaptive impedance control method for a fracture reduction robot according to claim 1, characterized in that The process of adjusting the initial damping coefficient of the impedance controller by using the damping coefficient correction amount to obtain the adjusted damping coefficient is as follows: ; Among them, is the adjusted damping coefficient; is the initial damping coefficient; is the correction amount of the damping coefficient.

8. The variable universe fuzzy adaptive impedance control system of the fracture reduction robot is used to execute the variable universe fuzzy adaptive impedance control method of the fracture reduction robot described in any one of claims 1 to 7, and is characterized in that It includes a data acquisition module and an impedance control module; The data acquisition module is used to acquire the end-effector force tracking error and the change rate of the end-effector force tracking error of the robot during the fracture reduction process; And the initial damping coefficient of the impedance controller; The impedance control module is used to design the input variables and output variables of the fuzzy adaptive impedance controller of the fracture reduction robot; the input variables include the end-effector force tracking error and the change rate of the end-effector force tracking error of the robot during the fracture reduction process; The output variable is the damping coefficient correction amount of the impedance controller; design the first scaling factor for adjusting the domain size of the end-effector force tracking error, the second scaling factor for adjusting the domain size of the change rate of the end-effector force tracking error, and the third scaling factor for adjusting the domain size of the damping coefficient correction amount; Adjust the domain size of the input variables according to the first quantization factor of the end-effector force tracking error and the first scaling factor, and the second quantization factor of the change rate of the end-effector force tracking error and the second scaling factor; Adjust the domain size of the output variable according to the proportional factor of the damping coefficient correction amount and the third scaling factor; symmetrically divide the input variable and the output variable, determine the membership function and fuzzy rules of the fuzzy adaptive impedance controller, and use the adjusted input variable domain and output variable domain to make the fuzzy adaptive impedance controller output the damping coefficient correction amount of the impedance controller; use the damping coefficient correction amount to adjust the initial damping coefficient of the impedance controller to obtain the adjusted damping coefficient. The first scaling factor, the second scaling factor, and the third scaling factor are obtained by fuzzy inference. Specifically, the corresponding fuzzy rules of the first scaling factor, the second scaling factor, and the third scaling factor are formulated according to the requirements of the damping coefficient correction amount of the impedance controller based on the changes in the end-effector force tracking error and the change rate of the force tracking error during the fracture reduction operation. The first quantization factor is used to convert the basic domain range of the end-effector force tracking error during the fracture reduction process of the robot into the fuzzy domain range. The second quantization factor is used to convert the basic domain range of the change rate of the end-effector force tracking error during the fracture reduction process of the robot into the fuzzy domain range. The proportional factor is used to convert the fuzzy domain range of the damping coefficient correction amount of the impedance controller into the basic domain range. The system further includes an execution module. The execution module is used to receive the adjusted damping coefficient, apply the adjusted damping coefficient to the robot impedance controller to dynamically adjust the impedance characteristics of the end effector; and combine the real-time force feedback with the current state of the robot, calculate the desired fracture reduction end position and attitude correction amount, and superimpose it on the current target position; then based on the inverse kinematics algorithm, calculate the desired angles of each joint and drive the precise movement of each joint of the robot.

Images