Fracture reduction robot variable universe fuzzy adaptive impedance control method and system

Abstract

The invention provides a variable universe fuzzy adaptive impedance control method and system for a fracture reduction robot. The method comprises the following steps: designing input and output variables of a fuzzy adaptive impedance controller of the fracture reduction robot; adjusting the size of the input variable discourse domain according to a first quantization factor and a first telescopic factor of the tail end force tracking error and a second quantization factor and a second telescopic factor of the tail end force tracking error change rate; adjusting the universe size of the output variable according to the scale factor and the third telescopic factor of the output variable; symmetrically dividing an input variable and an output variable, determining a membership function and a fuzzy rule of the impedance controller, and enabling the fuzzy adaptive impedance controller to output a damping coefficient correction by using the adjusted input variable discourse domain and output variable discourse domain; and adjusting the initial damping coefficient of the impedance controller by using the correction to obtain an adjusted damping coefficient. Based on the method, the invention further provides a self-adaptive impedance control system. According to the method, the fracture reduction precision is improved by using variable universe fuzzy self-adaptive impedance control.

Description

Technical Field

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

[0002] With the continuous advancement 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 surgery usually relies on the doctor's experience and manual operation. This method not only has the problems of low surgical accuracy and long operation time, but also easily leads to postoperative complications. Especially when facing complex or severe fractures, the limitations of traditional surgical methods are more and more significant. In order to improve the accuracy and efficiency of surgery, fracture reduction surgical robots have emerged. The robot integrates advanced sensing technology, precise robotic arm control system and intelligent algorithm, which can provide more accurate and stable operation during surgery, thereby significantly improving the quality of surgery and accelerating the patient's postoperative recovery. Although current robotic technology has made certain progress, it still faces challenges such as insufficient control accuracy and weak flexibility and adaptability. Especially during surgery, the patient's muscle stretching and joint activity will cause changes in dynamic loads, increasing the uncertainty in surgical operations. Patients often react unconsciously, such as muscle contraction or slight movement of joints. These factors make the robot face constantly changing mechanical loads, which may affect its accuracy and control ability. Therefore, how to further improve the adaptability and stability of the robot system in a complex biomechanical environment has become an important direction of current research.

[0003] Impedance control technology is widely used in fracture reduction surgical robots to ensure accuracy and safety during surgery. Traditional impedance control adjusts robot motion by setting fixed impedance parameters to provide a stable operating environment, which is 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 accuracy and failure to meet high-precision requirements. To solve this problem, fuzzy impedance control has emerged as a more flexible solution. Fuzzy control systems adaptively adjust control parameters to enable robots to dynamically respond according to the patient's physical characteristics and surgical conditions, thereby significantly improving the accuracy and safety of surgery. However, during surgery, the patient's muscle stretching and joint activity may cause dynamic load changes, resulting in unstable mechanical loads on the robot, increasing the uncertainty of surgery. Although traditional fuzzy control systems can achieve good control effects when the force error is large, when the force error is small, further improving the control accuracy usually requires adding more fuzzy control rules, which greatly increases the amount of calculation and reduces the efficiency of the system. Summary of the invention

[0004] In order to solve the technical problems of the shortcomings of traditional impedance control and fuzzy impedance control, the present invention proposes a variable domain fuzzy adaptive impedance control method and system for a fracture reduction robot, which effectively improves the operating accuracy and stability of the robot in fracture reduction surgery, 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] The variable domain fuzzy adaptive impedance control method for a fracture reduction robot comprises 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 end force tracking error change rate of the robot during the fracture reduction process; the output variable is the damping coefficient correction amount of the impedance controller;

[0008] Design a first scaling factor for adjusting the domain size of the terminal force tracking error, a second scaling factor for adjusting the domain size of the terminal force tracking error change rate, and a third scaling factor for adjusting the domain size of the damping coefficient correction amount; adjust the domain size of the input variable according to the first quantization factor and the first scaling factor of the terminal force tracking error, the second quantization factor and the second scaling factor of the terminal force tracking error change rate; adjust the domain size of the output variable according to the proportional factor and the third scaling factor of the damping coefficient correction amount;

[0009] The input variables and output variables are symmetrically divided, the membership function and fuzzy rules of the fuzzy adaptive impedance controller are determined, and the fuzzy adaptive impedance controller outputs a damping coefficient correction amount of the impedance controller using the adjusted input variable domain and output variable domain; the initial damping coefficient of the impedance controller is adjusted using the correction amount of the damping coefficient to obtain the adjusted damping coefficient.

[0010] Furthermore, the method also includes: applying the adjusted damping coefficient to the robot impedance controller to dynamically adjust the impedance characteristics of the end effector; and combining real-time force feedback with the current state of the robot to calculate the desired fracture reduction end position and posture correction, and superimpose them on the current target position; then based on the inverse kinematics algorithm, calculate the desired angles of each joint, and drive the robot's joints to move precisely.

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

[0012] Determine the range of variation of the robot's end force tracking error during fracture reduction;

[0013] Determine the range of the robot's end force tracking error change rate during fracture reduction;

[0014] Determine the domain range of the damping coefficient correction.

[0015] Furthermore, after designing the first expansion factor for adjusting the domain size of the end force tracking error, the second expansion factor for adjusting the domain size of the end force tracking error change rate, and the third expansion factor for adjusting the domain size of the damping coefficient correction amount, it also includes: formulating fuzzy rules corresponding to the first expansion factor, the second expansion factor, and the third expansion factor according to the change of the robot's end force tracking error during fracture reduction and the requirements for the damping coefficient correction amount of the impedance controller.

[0016] Furthermore, the process of adjusting the input variable domain size according to the first quantization factor and the first expansion factor of the terminal force tracking error, the second quantization factor and the second expansion factor of the terminal force tracking error change rate; and adjusting the output variable domain size according to the proportional factor and the third expansion factor of the damping coefficient correction amount includes:

[0017] Dividing a first quantization factor of the end force tracking error of the robot during fracture reduction by a first expansion and contraction factor;

[0018] Dividing the second quantization factor of the rate of change of the end force tracking error of the robot during the fracture reduction process by the second expansion and contraction factor;

[0019] The scaling factor of the fuzzy adaptive impedance controller output variable is multiplied by the third scaling factor.

[0020] Further, the membership function and fuzzy rules of the fuzzy adaptive impedance controller are determined, and the adjusted input variable domain and output variable domain are used to make the damping coefficient correction amount of the fuzzy adaptive impedance controller output impedance controller specifically include:

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

[0022] Mamdani fuzzy reasoning and center of gravity method are used to complete fuzzy reasoning and clarification, and the correction value of the damping coefficient of the robot impedance controller is obtained.

[0023] Furthermore, the method also includes:

[0024] The same number of fuzzy subsets are defined for both input and output variables;

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

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

[0027] Furthermore, 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] Wherein, b is the adjusted damping coefficient; b0 is the initial damping coefficient; Δb is the correction amount of the damping coefficient.

[0029] The present invention also proposes a variable domain 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 acquire the end force tracking error and the end force tracking error change rate of the robot during the fracture reduction process; 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 end force tracking error change rate of the robot during the fracture reduction process; the output variable is the damping coefficient correction of the impedance controller; the first expansion factor for adjusting the end force tracking error domain size, the second expansion factor for adjusting the end force tracking error change rate domain size and the third expansion factor for adjusting the damping coefficient correction domain size are designed; the input variable domain size is adjusted according to the first quantization factor and the first expansion factor of the end force tracking error, the second quantization factor and the second expansion factor of the end force tracking error change rate; the output variable domain size is adjusted according to the proportional factor and the third expansion factor of the damping coefficient correction; the input variables and the output variables are symmetrically divided, the membership function and the fuzzy rules of the fuzzy adaptive impedance controller are determined, and the adjusted input variable domain and output variable domain are used to make the fuzzy adaptive impedance controller output the damping coefficient correction of the impedance controller; the damping coefficient correction is used to adjust the initial damping coefficient of the impedance controller to obtain the adjusted damping coefficient.

[0032] Furthermore, the system also 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, and 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 desired fracture reduction end position and posture correction, and superimpose them to the current target position; then based on the inverse kinematics algorithm, calculate the desired angle of each joint, and drive each joint of the robot to move precisely.

[0034] The effects provided in the content of the invention are only the effects of the embodiments, not 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 domain 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 force tracking error and the end force tracking error change rate of the robot during the fracture reduction process; the output variable is the damping coefficient correction amount of the impedance controller;

[0036] Design a first expansion factor for adjusting the domain size of the end force tracking error, a second expansion factor for adjusting the domain size of the end force tracking error change rate, and a third expansion factor for adjusting the domain size of the damping coefficient correction amount; adjust the domain size of the input variable according to the first quantization factor and the first expansion factor of the end force tracking error, the second quantization factor and the second expansion factor of the end force tracking error change rate; adjust the domain size of the output variable according to the proportional factor and the third expansion factor of the damping coefficient correction amount; symmetrically divide the input variables and the output variables, determine the membership function and fuzzy rules of the fuzzy adaptive impedance controller, use the adjusted input variable domain and output variable domain, so that the fuzzy adaptive impedance controller outputs 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. Based on the variable domain fuzzy adaptive impedance control method of the fracture reduction robot, a variable domain fuzzy adaptive impedance control method system for the fracture reduction robot is also proposed. The adaptive impedance control using variable domain fuzzy in the present invention can effectively improve the response speed and control accuracy of the system, while reducing overshoot and adjustment time.

[0037] The present invention dynamically adjusts the domain range in the fuzzy control system so that the system can be adaptively adjusted according to the actual situation of the patient and the changes in the surgical environment, thereby optimizing the control effect. By shrinking or expanding the fuzzy domain, the method uses idle fuzzy subsets for more precise control without adding control rules, maintains high control accuracy with small errors, and significantly improves calculation efficiency.

[0038] Compared with the prior art, the present invention introduces variable domain fuzzy control, which can adjust the expansion and contraction factor in real time according to the robot's working state or parameter changes, thereby dynamically optimizing the control range. This adaptive adjustment mechanism enables the robot to respond to different operating conditions more flexibly during fracture reduction, providing a more accurate reduction effect than traditional control methods. This improvement significantly enhances the system's adaptive ability, ensuring that the robot can quickly adjust the reduction strategy at critical moments, thereby improving the safety and stability of the surgical process. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1This is a flow chart of the variable domain fuzzy adaptive impedance control method for a fracture reduction robot proposed in Example 1 of the present invention;

[0040] Figure 2 This is a schematic diagram of the structure of the robot fracture reduction surgery simulation proposed in Example 1 of the present invention;

[0041] Figure 3 This is a schematic diagram of the adaptive impedance control using variable universe fuzzy control proposed in Example 1 of the present invention;

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

[0043] Figure 5 This is an internal simulation model diagram of the adaptive impedance control algorithm using variable universe fuzzy control proposed in Example 1 of the present invention;

[0044] Figure 6 It is the trajectory tracking curve of the end of the robot arm in the X direction in the simulation experiment proposed in Example 1 of the present invention;

[0045] Figure 7 It is the trajectory tracking curve of the end of the robot arm in the Y direction in the simulation experiment proposed in Example 1 of the present invention;

[0046] Figure 8 It is the trajectory tracking curve of the end of the robot arm in the Z direction in the simulation experiment proposed in Example 1 of the present invention;

[0047] Fig. 9 This is a schematic diagram of the variable domain fuzzy adaptive impedance control system of the fracture reduction robot proposed in Example 2 of the present invention. DETAILED DESCRIPTION

[0048] In order to clearly illustrate the technical features of the present solution, the present invention is described in detail below through specific implementation methods and in conjunction with the accompanying drawings. The disclosure below provides many different embodiments or examples for realizing different structures of the present invention. In order 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 numbers 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 accompanying drawings are not necessarily drawn to scale. The present invention omits the description of known components and processing techniques and processes to avoid unnecessary limitations on the present invention.

[0049] Example 1

[0050] Embodiment 1 of the present invention proposes a variable domain 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 suitable for the treatment of complex fractures that require extremely high surgical precision, and provides a more accurate and stable control method for fracture reduction surgery.

[0051] Figure 1 This is a flow chart of the variable domain fuzzy adaptive impedance control method for a fracture reduction robot proposed in Example 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 force tracking error and the end force tracking error change rate of the robot during the fracture reduction process; the output variable is the damping coefficient correction amount of the impedance controller;

[0053] In the present invention, the end force tracking error and the end force tracking error change rate during the fracture reduction process are obtained by a force sensor installed at the end of the mechanical arm. Figure 2 This is a schematic diagram of the structure of the robot fracture reduction surgery simulation proposed in Example 1 of the present invention;

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

[0055] In step S200, a first scaling factor for adjusting the domain size of the terminal force tracking error, a second scaling factor for adjusting the domain size of the terminal force tracking error change rate, and a third scaling factor for adjusting the domain size of the damping coefficient correction are designed; the domain size of the input variable is adjusted according to the first quantization factor and the first scaling factor of the terminal force tracking error, the second quantization factor and the second scaling factor of the terminal force tracking error change rate; the domain size of the output variable is adjusted according to the proportional factor and the third scaling factor of the damping coefficient correction;

[0056] Design the first expansion 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 expansion factor β of the output variable Δb b .ae represents the first scaling factor for adjusting the size of the fuzzy domain of the input variable e, a ec The second scaling factor, β, represents the adjustment of the size of the fuzzy domain of the input variable ec. b Represents the third scaling factor for adjusting the size of the fuzzy domain of the output variable Δb.

[0057] The first expansion factor a e , the second scaling factor a ec and the third stretch factor β b Fuzzy reasoning is used to obtain: According to the change of the robot end force tracking error and the force tracking error change rate during the fracture reduction operation, the requirements for the impedance controller damping coefficient correction Δb are formulated. e 、a ec and β b Corresponding fuzzy rules; when the robot's end force tracking error e and its end force tracking error change rate ec during fracture reduction are large, the system faces a large control error, which may lead to excessive overshoot. In order to effectively reduce this overshoot phenomenon and enhance the system's anti-interference ability, the damping coefficient b should be set to a larger value. Increasing the damping coefficient can improve the stability of the system, making it more resistant to external interference, thereby avoiding drastic fluctuations. On the contrary, when the robot's end force tracking error e and its end force tracking error change rate ec during fracture reduction are small, the system's control error is relatively small. At this time, the response speed of the control system needs to be increased in order to adjust to the desired state faster. In this case, the damping coefficient b should be set to a smaller value. Reducing the damping coefficient can make the system respond more quickly and reduce delays, so that the control system can adapt to input changes more quickly.

[0058] Adjust the domain, that is, change the domain; change the first quantization factor K of the input variable e e Divide by the first scaling factor a e , the second quantization factor K of the input variable ec ec Divide by the second scaling factor a ec , the proportional factor K of the output variable Δb u Multiply by the third scaling factor β b . Figure 4 A model block diagram of adaptive impedance control using variable universe fuzzy control proposed in Example 1 of the present invention;

[0059] The first quantization factor K e Convert the basic domain range of the actual input variable e to the fuzzy domain range of the input variable e; the second quantization factor K ec Convert the basic domain range of the actual input variable ec to the fuzzy domain range of the input variable ec; the scaling factor Ku The fuzzy domain range of the output variable Δb is converted to the basic domain range of the output variable Δb.

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

[0061] In step S300, the input variables and output variables are symmetrically divided, the membership function and fuzzy rules of the fuzzy adaptive impedance controller are determined, and the fuzzy adaptive impedance controller outputs a damping coefficient correction amount of the impedance controller using the adjusted input variable domain and output variable domain; the initial damping coefficient of the impedance controller is adjusted using the damping coefficient correction amount to obtain the adjusted damping coefficient.

[0062] to a e 、a ec and β b When fuzzy reasoning is used, the input variable is still the robot's end force tracking error e and its error change rate ec during fracture reduction, and the output variable is the first expansion factor a e , the second scaling factor a ec and the third stretch factor β b The input and output state variables are symmetrically divided, and the membership functions are all Gaussian functions; the fuzzy domains of the input variables robot end force tracking error e and its error change rate ec are [□6, 6], and the language values ​​are represented by {NB, NM, NS, ZO, PS, PM, PB}; a e 、a ec The language value is represented by {large, medium, small, very small}, that is, {B, M, S, VS}. Output state variable β b The language value is represented by {very large, large, medium, small, very small}, that is, {VB, B, M, S, VS}, and the output variable a e 、a ec and β b The range is [0,1]; According to the above change rules and domain division, the first scaling factor a is obtained e and the second scaling factor a ec The fuzzy rules are:

[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 third expansion factor β b The fuzzy rules 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 output variable Δb are defined as 7 fuzzy subsets, and the fuzzy domain range is [□6,6]. The linguistic variable value is represented by {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, and the corresponding fuzzy set is {NB, NM, NS, ZO, PS, PM, PB}. According to the impedance parameter adjustment rule and combined with simulation experience, the fuzzy rules of Δb are 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] The impedance parameter correction value Δb is obtained by using Mamdani fuzzy reasoning and centroid method.

[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] Wherein, 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, the adjusted damping coefficient is applied to the robot impedance controller to dynamically adjust the impedance characteristics of the end effector; and the desired fracture reduction end position and posture correction are calculated by combining real-time force feedback with the current state of the robot, and superimposed on the current target position; then based on the inverse kinematics algorithm, the desired angle of each joint is calculated, and each joint of the robot is driven to move accurately. Ensure that the robot has adaptability, stability and accuracy when interacting with the environment, and finally complete the fracture reduction surgery task.

[0072] In order to fully illustrate the variable domain fuzzy adaptive impedance control method for the fracture reduction robot proposed in Example 1 of the present invention, the present invention adopts a simulation model to illustrate the implementation process of the present invention.

[0073] Figure 5 This is an internal simulation model diagram of the adaptive impedance control algorithm using variable domain fuzzy control proposed in Example 1 of the present invention; during the simulation process, it is necessary to first set up an external force simulation application module and an expected trajectory planning module. The external force simulation application module is mainly used to simulate the external force generated by muscles and soft tissues during fracture reduction. Because there is no way to directly obtain the force and force error rate of the real robotic arm in the simulation, a force signal can be simulated and input through this external force simulation application module. The expected trajectory planning module can simulate the robot to plan a specific reduction path through the visual system and guide the robot to move along the predetermined path.

[0074] The simulation system also includes an impedance algorithm control module and a manipulator subsystem; the impedance algorithm control module is used to execute the process in steps S100 to S300. The manipulator subsystem includes the joints and connecting rods of the robot, first outputs the motion information of the joint space, and then converts it into the posture information in the Cartesian space through the forward kinematics model. In the impedance algorithm control module, the fuzzy controller is constructed using the Fuzzy Control toolbox in Matlab, and the expansion factor is selected by the fuzzy reasoning method and added to the input and output of the fuzzy controller. Combining the variable domain fuzzy controller and the impedance controller, an adaptive impedance controller based on variable domain fuzzy control is formed. The controller converts the external force acting on the robot into a posture correction through an algorithm, and superimposes the correction on the current position. Finally, the angles of each joint are obtained by inverse kinematics, thereby controlling the movement of the robot.

[0075] In order to verify that the regulation performance of the robot fracture reduction when using the adaptive impedance control method based on variable universe fuzzy of the present invention is better than the regulation performance under the traditional impedance control and the general fuzzy control combined with impedance control, under the condition that all system parameters are consistent, during the operation of the system, a 20N positive direction external force is applied at 2s to simulate the relaxation state of the muscle, and a 30N negative direction external force is applied at 7s to simulate the contraction state of the muscle, and the trajectory tracking curves of the three methods are observed, and the regulation performance under the three control modes is compared by using MATLAB simulation: Figure 6 is the trajectory tracking curve of the end of the robot arm in the X direction in the simulation experiment proposed in Example 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 robot arm is subjected to a 20N positive external force in the X direction at 2s and a 30N negative external force at 7s in the simulation experiment. Figure 7 is the trajectory tracking curve of the end of the robot arm in the Y direction in the simulation experiment proposed in Example 1 of the present invention, Figure 7 The trajectory tracking curves of traditional impedance control, fuzzy control and variable domain fuzzy control are given when the end of the robot arm is subjected to a 20N positive external force in the Y direction at 2s and a 30N negative external force at 7s in the simulation experiment. Figure 8 is the trajectory tracking curve of the end of the robot arm in the Z direction in the simulation experiment proposed in Example 1 of the present invention, Figure 8 The trajectory tracking curves of traditional impedance control, fuzzy control and variable domain fuzzy control are given when the end of the robot arm is subjected to a 20N positive force in the Z direction at 2s and a 30N negative force at 7s in the simulation experiment. Figure 6 , Figure 7 and Figure 8The comparison of simulation waveforms shows that compared with traditional impedance control and general fuzzy control combined with impedance control, the adaptive impedance control using variable domain fuzzy can effectively improve the response speed and control accuracy of the system, while reducing overshoot and adjustment time.

[0076] The variable domain fuzzy adaptive impedance control method for fracture reduction robot proposed in Example 1 of the present invention dynamically adjusts the domain range in the fuzzy control system so that the system can be adaptively adjusted according to the actual situation of the patient and the changes in the surgical environment, thereby optimizing the control effect. By shrinking or expanding the fuzzy domain, this method uses idle fuzzy subsets for more precise control without adding control rules, maintains high control accuracy with small errors, and significantly improves computational efficiency.

[0077] The variable domain fuzzy adaptive impedance control method for fracture reduction robot proposed in Example 1 of the present invention can adjust the expansion and contraction factor in real time according to the working state or parameter changes of the robot by introducing variable domain fuzzy control, thereby dynamically optimizing the control range. This adaptive adjustment mechanism enables the robot to respond to different operating conditions more flexibly during fracture reduction, providing a more accurate reduction effect than traditional control methods. This improvement significantly improves 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.

[0078] Example 2

[0079] Based on the variable domain fuzzy adaptive impedance control method for fracture reduction robot proposed in Example 1 of the present invention, Example 2 of the present invention also proposes a variable domain fuzzy adaptive impedance control system for fracture reduction robot. Fig. 9 This is a schematic diagram of a variable-domain fuzzy adaptive impedance control system for a fracture reduction robot proposed in Example 2 of the present invention, the system comprising a data acquisition module and an impedance control module;

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

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

[0082] 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 end force tracking error change rate of the robot during the fracture reduction process; the output variable is the damping coefficient correction of the impedance controller; the first expansion factor for adjusting the end force tracking error domain size, the second expansion factor for adjusting the end force tracking error change rate domain size and the third expansion factor for adjusting the damping coefficient correction domain size are designed; the input variable domain size is adjusted according to the first quantization factor and the first expansion factor of the end force tracking error, the second quantization factor and the second expansion factor of the end force tracking error change rate; the output variable domain size is adjusted according to the proportional factor and the third expansion factor of the damping coefficient correction; the input variables and the output variables are symmetrically divided, the membership function and the fuzzy rules of the fuzzy adaptive impedance controller are determined, and the adjusted input variable domain and output variable domain are used to make the fuzzy adaptive impedance controller output the damping coefficient correction of the impedance controller; the damping coefficient correction is used 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 of the fracture reduction robot, it also includes: determining the range of variation of the end force tracking error of the robot during the fracture reduction process; determining the range of variation of the rate of change of the end force tracking error of the robot during the fracture reduction process; and determining the domain range of the damping coefficient correction amount.

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

[0085] The first quantization factor of the robot's end force tracking error during fracture reduction is divided by the first expansion factor; the second quantization factor of the robot's end force tracking error change rate during fracture reduction is divided by the second expansion factor; and the proportional factor of the fuzzy adaptive impedance controller output variable is multiplied by the third expansion factor.

[0086] Determine the membership function and fuzzy rules of the fuzzy adaptive impedance controller, 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 include: determine the membership function and fuzzy rules of the fuzzy adaptive impedance controller, the membership function adopts a Gaussian membership function; use Mamdani fuzzy reasoning and center of gravity method to complete fuzzy reasoning and clarification, and obtain the correction amount of the damping coefficient of the robot impedance controller.

[0087] The input variables and the output variables are defined with the same number of fuzzy subsets; the fuzzy domain range of the input variables is the first range; the fuzzy domain range of the output variables is the second range; the linguistic variable values ​​and the corresponding fuzzy sets are determined.

[0088] 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;

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

[0090] The system also 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, and dynamically adjust 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 posture correction, and superimpose them on the current target position; then based on the inverse kinematics algorithm, calculate the desired angle of each joint, and drive the robot's joints to move precisely, ensuring that the robot has adaptability, stability and accuracy when interacting with the environment, and ultimately completing the fracture reduction surgery task.

[0091] The variable universe fuzzy adaptive impedance control system for a fracture reduction robot proposed in Example 2 of the present invention uses variable universe fuzzy adaptive impedance control to effectively improve the response speed and control accuracy of the system, while reducing overshoot and adjustment time.

[0092] The variable domain fuzzy adaptive impedance control system for fracture reduction robot proposed in Example 2 of the present invention dynamically adjusts the domain range in the fuzzy control system so that the system can be adaptively adjusted according to the actual situation of the patient and the changes in the surgical environment, thereby optimizing the control effect. By shrinking or expanding the fuzzy domain, this method uses idle fuzzy subsets for more precise control without adding control rules, maintains high control accuracy with small errors, and significantly improves computational efficiency.

[0093] The variable domain fuzzy adaptive impedance control system for the fracture reduction robot proposed in Example 2 of the present invention can adjust the expansion and contraction factor in real time according to the working state or parameter changes of the robot by introducing variable domain fuzzy control, thereby dynamically optimizing the control range. This adaptive adjustment mechanism enables the robot to respond to different operating conditions more flexibly during fracture reduction, providing a more accurate reduction effect than traditional control methods. This improvement significantly improves 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 parts of the impedance control module in the variable domain fuzzy adaptive impedance control system of the fracture reduction robot provided in Example 2 of the present invention, please refer to the detailed description of the corresponding parts in the variable domain fuzzy adaptive impedance control method of the fracture reduction robot provided in Example 1 of the present invention, and will not be repeated here.

[0095] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device that includes a series of elements are inherent to the elements. In the absence of more restrictions, the elements defined by the sentence "comprise one..." do not exclude the existence of other identical elements in the process, method, article or device that includes the elements. In addition, the part of the above-mentioned technical solution provided by the embodiment of the present invention that is consistent with the corresponding technical solution in the prior art in principle is not described in detail, so as to avoid excessive elaboration.

[0096] Although the above describes the specific implementation of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection 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 list all the implementation methods here. On the basis of the technical solution of the present invention, various modifications or deformations that can be made by those skilled in the art without creative work are still within the scope of protection of the present invention.

Claims

1. A variable domain fuzzy adaptive impedance control method for a fracture reduction robot, characterized in that: The following steps are involved: Design 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 value of the impedance controller; Design a first expansion factor for adjusting the domain size of the terminal force tracking error, a second expansion factor for adjusting the domain size of the terminal force tracking error change rate, and a third expansion factor for adjusting the domain size of the damping coefficient correction amount; Adjust the input variable domain size according to the first quantization factor and the first scaling factor of the terminal force tracking error, the second quantization factor and the second scaling factor of the terminal force tracking error change rate; Adjust the output variable domain size according to the proportional factor of the damping coefficient correction amount and the third expansion factor; The input variables and output variables are symmetrically divided, the membership function and fuzzy rules of the fuzzy adaptive impedance controller are determined, and the fuzzy adaptive impedance controller outputs a damping coefficient correction amount of the impedance controller using the adjusted input variable domain and output variable domain; the initial damping coefficient of the impedance controller is adjusted using the correction amount of the damping coefficient to obtain the adjusted damping coefficient.

2. The variable domain fuzzy adaptive impedance control method for fracture reduction robot according to claim 1 is characterized in that: The method also includes: applying the adjusted damping coefficient to the robot impedance controller to dynamically adjust the impedance characteristics of the end effector; and combining real-time force feedback with the current state of the robot to calculate the desired fracture reduction end position and posture correction, and superimpose them on the current target position; then based on the inverse kinematics algorithm, calculate the desired angle of each joint, and drive each joint of the robot to move precisely.

3. The variable domain fuzzy adaptive impedance control method for fracture reduction robot according to claim 1 is characterized in that: When designing the input variables and output variables of the fuzzy adaptive impedance controller of the fracture reduction robot, it also includes: Determine the range of variation of the robot's end force tracking error during fracture reduction; Determine the range of the robot's end force tracking error change rate during fracture reduction; Determine the domain range of the damping coefficient correction.

4. The variable domain fuzzy adaptive impedance control method for fracture reduction robot according to claim 1, characterized in that: After designing the first expansion factor for adjusting the domain size of the end force tracking error, the second expansion factor for adjusting the domain size of the end force tracking error change rate, and the third expansion factor for adjusting the domain size of the damping coefficient correction amount, it also includes: formulating corresponding fuzzy rules for the first expansion factor, the second expansion factor, and the third expansion factor according to the change of the robot's end force tracking error during fracture reduction and the requirements for the damping coefficient correction amount of the impedance controller.

5. The variable domain fuzzy adaptive impedance control method for fracture reduction robot according to claim 4 is characterized in that: The input variable domain size is adjusted according to the first quantization factor and the first scaling factor of the terminal force tracking error, the second quantization factor and the second scaling factor of the terminal force tracking error change rate; The process of adjusting the output variable domain size according to the proportional factor of the damping coefficient correction amount and the third expansion factor includes: Dividing a first quantization factor of the end force tracking error of the robot during fracture reduction by a first expansion and contraction factor; Dividing the second quantization factor of the rate of change of the end force tracking error of the robot during the fracture reduction process by the second expansion and contraction factor; The scaling factor of the fuzzy adaptive impedance controller output variable is multiplied by the third scaling factor.

6. The variable domain fuzzy adaptive impedance control method for fracture reduction robot according to claim 1, characterized in that: The membership function and fuzzy rules of the fuzzy adaptive impedance controller are determined, and the adjusted input variable domain and output variable domain are used to make the damping coefficient correction amount of the fuzzy adaptive impedance controller output impedance controller specifically include: Determine the membership function and fuzzy rules of the fuzzy adaptive impedance controller, wherein the membership function adopts a Gaussian membership function; Mamdani fuzzy reasoning and center of gravity method are used to complete fuzzy reasoning and clarification, and the correction value of the damping coefficient of the robot impedance controller is obtained.

7. The variable domain fuzzy adaptive impedance control method for fracture reduction robot according to claim 6, characterized in that: The method also includes: The same number of fuzzy subsets are defined for both input and output variables; 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 linguistic variable values ​​and the corresponding fuzzy sets.

8. The variable domain fuzzy adaptive impedance control method for fracture reduction robot according to claim 2, characterized in that: 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; Wherein, b is the adjusted damping coefficient; b0 is the initial damping coefficient; Δb is the correction amount of the damping coefficient.

9. A variable universe fuzzy adaptive impedance control system for a fracture reduction robot, characterized in that: It includes a data acquisition module and an impedance control module; The data acquisition module is used to acquire the end force tracking error and the end force tracking error change rate 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 force tracking error and the end force tracking error change rate of the robot during the fracture reduction process; The output variable is the damping coefficient correction of the impedance controller; a first expansion factor for adjusting the domain size of the terminal force tracking error, a second expansion factor for adjusting the domain size of the terminal force tracking error change rate, and a third expansion factor for adjusting the domain size of the damping coefficient correction are designed; Adjust the input variable domain size according to the first quantization factor and the first scaling factor of the terminal force tracking error, the second quantization factor and the second scaling factor of the terminal force tracking error change rate; The output variable domain size is adjusted according to the proportional factor of the damping coefficient correction amount and the third expansion factor; the input variables and output variables are symmetrically divided, the membership function and fuzzy rules of the fuzzy adaptive impedance controller are determined, and the fuzzy adaptive impedance controller outputs the damping coefficient correction amount of the impedance controller using the adjusted input variable domain and output variable domain; the initial damping coefficient of the impedance controller is adjusted using the damping coefficient correction amount to obtain the adjusted damping coefficient.

10. The variable domain fuzzy adaptive impedance control system of the fracture reduction robot according to claim 9, wherein the system further comprises an execution module; The execution module is used to receive the adjusted damping coefficient, apply the adjusted damping coefficient to the robot impedance controller, and 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 desired fracture reduction end position and posture correction, and superimpose them to the current target position; then based on the inverse kinematics algorithm, calculate the desired angle of each joint, and drive each joint of the robot to move precisely.

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