Adaptive adjustment method for joint motor parameters of quadruped robot based on fuzzy control

Abstract

The present application relates to the field of quadruped robot, in particular to a kind of quadruped robot joint motor parameter self-adaptive adjustment method based on fuzzy control.The present application includes the following steps:S1: current error and error rate of change are calculated according to current reference instruction and feedback current;S2: current error and error rate of change are divided into minimum, small, medium and large, fuzzy by triangular membership function and form fuzzy set, and the membership of fuzzy set is calculated;S3: according to fuzzy rule, obtain proportional coefficient and integral coefficient fuzzy subset and membership;S4: by inverse triangular membership function, clear value of proportional coefficient and integral coefficient is obtained, and clear value is transmitted to current loop proportional integral parameter.The present application can improve control accuracy, enhance adaptability, reduce electromagnetic noise influence and simplify parameter adjustment process.

Description

Technical Field

[0001] This invention relates to the field of quadruped robots, and more particularly to an adaptive adjustment method for joint motor parameters of quadruped robots based on fuzzy control. Background Technology

[0002] In the control of quadruped robots, the precise control of joint motors is crucial. They are responsible for driving the various joints of the robot's limbs to achieve complex gait and motion control. Because quadruped robots encounter various complex terrains and external disturbances during walking, the motors need to have strong responsiveness to adapt to these changes and ensure stable walking. Especially when dealing with high dynamic response requirements, the performance of the current loop controller directly affects the stability and motion performance of the entire system. In existing technologies, on the one hand, traditional fixed-parameter PI controllers cannot automatically adjust parameters according to load changes, leading to decreased control performance or even instability in some situations; on the other hand, traditional PI control methods require a long adjustment time to restore system stability when dealing with these disturbances, affecting the robot's motion performance and response speed. Summary of the Invention

[0003] To address the aforementioned problems, this invention provides an adaptive adjustment method for joint motor parameters of a quadruped robot based on fuzzy control, which can improve control accuracy, enhance adaptability, reduce the influence of electromagnetic noise, and simplify the parameter adjustment process.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0005] An adaptive adjustment method for joint motor parameters of a quadruped robot based on fuzzy control includes the following steps:

[0006] S1: Calculate the current error and error change rate based on the current reference command and feedback current;

[0007] S2: Divide the current error and error change rate into minimum, small, medium and large, fuzzify them using the triangular membership function and form fuzzy sets, and calculate the membership degree of the fuzzy sets;

[0008] S3: Based on the fuzzy rules, obtain the fuzzy subsets and membership degrees of the proportional coefficient and integral coefficient;

[0009] S4: Obtain the clear values ​​of the proportional coefficient and integral coefficient through the inverse triangular membership function, and transfer the clear values ​​to the proportional-integral parameters of the current loop.

[0010] Furthermore, the current error and the rate of change of error include: the rate of change of error is filtered by a first-order low-pass filter to remove high-frequency sampling errors, and the error and the rate of change of error are normalized to the range of 0 to 1.

[0011] Furthermore, the calculation of the membership degree of the fuzzy set includes the following steps:

[0012] S21: Divide the current error and the rate of change of error into four fuzzy sets: minimal, small, medium and large, denoted as {Z0, PS, PM, PB}. Determine the universe of discourse in the range of 0 to 1 based on the normalization of the error and the rate of change of error.

[0013] S22: For each fuzzy set, the change in membership degree of the fuzzy set in different intervals is determined by the coordinates (a, b, c) of the three vertices of the triangle in the triangular membership function;

[0014] S23: Given a value x for current error or error change rate, determine the interval to which the value x belongs in {Z0, PS, PM, PB}, and calculate the membership degree according to the formula for the triangular membership function.

[0015] In step S23, the formula for calculating the membership function of the triangle is:

[0016] When a≤x≤b When b≤x≤c Where U represents the membership degree.

[0017] Furthermore, the fuzzy rule includes: the transfer function of the open-loop current loop system, with the formula: Where k p It is the proportionality coefficient, k i Here, s is the integral coefficient, s is the Laplace operator, R is the motor resistance, and L is the linear coefficient. q It is the q-axis inductance of the motor;

[0018] k p and k i The setting formula is: Where ω c It is system bandwidth;

[0019] The formula for tuning the closed-loop transfer function of the current loop to that of a first-order inertial system is as follows:

[0020] Furthermore, the system bandwidth ω c ,include:

[0021] When ω c When the volume is large, the system response is fast, and the motor generates large electromagnetic noise after being enabled.

[0022] When ω c When the system is slow, the motor generates a small amount of electromagnetic noise after being enabled.

[0023] According to ω cThe size of the joint motor and its response performance are in four states: minimum, small, medium, and large. The bandwidth values ​​corresponding to the four states are 0.5e3, 1e3, 1.5e3, and 2e3, respectively. The fuzzy set of the four states is {Z0, PS, PM, PB}.

[0024] K p The elements with complete membership are:

[0025] {kp.ZO=0.5e3*R, kp.PS=1e3*R, kp.PM=1.5e3*R, kp.PB=2e3*R};

[0026] k i The elements with complete membership are:

[0027] {ki.ZO=0.5e3*R, ki.PS=1e3*R, ki.PM=1.5e3*R, ki.PB=2e3*R}.

[0028] Furthermore, the fuzzy rules include:

[0029] When the quadruped robot is in motion, the joint motors switch to high system bandwidth.

[0030] When the quadruped robot is standing, the joint motors switch to low system bandwidth.

[0031] When the quadruped robot switches from a standing state to a moving state, the joint motors immediately switch from a small system bandwidth to a large system bandwidth.

[0032] Furthermore, the fuzzy rules include:

[0033] When a large current error occurs with a small error rate of change, the integral coefficient of the joint motor should be increased separately.

[0034] When a small current error occurs with a large error rate of change, the proportional coefficient and integral coefficient of the joint motor should be reduced.

[0035] Further, obtaining the fuzzy subsets and membership degrees of the proportional coefficients and integral coefficients includes:

[0036] Traverse step S2 to obtain the membership degrees of current error and error change rate respectively. Select the maximum membership degree and determine the fuzzy subset of current error and error change rate in terms of minimum, small, medium, and large based on the maximum membership degree. By querying the fuzzy subset of proportional coefficient and integral coefficient to which the fuzzy subset of current error and error change rate belongs, determine the fuzzy subset of control parameters. The maximum value of the membership degree of error and error change rate is used as the membership degree of proportional coefficient and integral coefficient.

[0037] Furthermore, obtaining the clear values ​​of the proportional coefficient and integral coefficient through the inverse triangular membership function includes:

[0038] Based on the fuzzy subsets and membership degrees of the obtained proportional coefficients and integral coefficients, the clear values ​​of the proportional coefficients and integral coefficients are obtained through the inverse triangular membership function.

[0039] The inverse trigonometric membership function, in a trigonometric function, when a membership degree corresponds to two proportional coefficients or integral coefficients, selects the smaller coefficient of the two coefficients as the clear value of the proportional coefficient, and selects the larger coefficient of the two coefficients as the clear value of the integral coefficient.

[0040] Furthermore, the transmission of the clear value to the current loop proportional-integral parameter includes:

[0041] Once the clear value is determined to be within a reasonable range, update the proportional and integral parameters of the PI controller. After updating the PI controller parameters, start the quadruped robot's joint motors and observe the operating status, which includes current response, speed change, and position accuracy.

[0042] The beneficial effects of this invention are as follows:

[0043] 1. By applying fuzzy processing and fuzzy rules, various system states are considered in greater detail, avoiding the inadequacy of traditional fixed-parameter control methods in complex situations and making control more precise. It can automatically adjust the proportional and integral coefficients based on real-time current errors and error change rates, enabling the joint motors to more accurately track the desired current value under different operating conditions, thereby improving the control accuracy of the quadruped robot. The fuzzy control method eliminates the need for manual parameter adjustment; adaptive parameter adjustment is achieved through fuzzy rules and automatic inference processes, greatly simplifying the parameter adjustment process. This not only saves debugging time and labor costs but also improves the accuracy and efficiency of parameter adjustment, allowing the quadruped robot to be deployed in practical applications more quickly.

[0044] 2. The characteristics of the joint motors of a quadruped robot change under different terrains, loads, and motion states. A fuzzy control-based method automatically adapts to these changes without manual parameter adjustment, greatly enhancing the quadruped robot's adaptability to different working environments. Whether on flat ground or rugged terrain, whether under light or heavy loads, this method can adjust parameters to achieve optimal control, improving the robot's reliability and practicality.

[0045] 3. By appropriately adjusting the system bandwidth, a balance is found between system response speed and electromagnetic noise. When the system requires a fast response, the bandwidth is appropriately increased, but this also generates greater electromagnetic noise; conversely, when noise requirements are higher, the bandwidth can be reduced, although the system response speed will be slower, the electromagnetic noise will also be reduced. This adaptive adjustment capability is optimized according to actual needs, reducing the interference of electromagnetic noise on other electronic components of the quadruped robot and improving the stability and reliability of the system. Attached Figure Description

[0046] Figure 1 This is a control block diagram of an adaptive adjustment method for the joint motor parameters of a quadruped robot.

[0047] Figure 2 This is a schematic diagram of the fuzzification of error and error rate of change.

[0048] Figure 3 This is the current loop control block diagram of the articulated motor.

[0049] Figure 4 It is a fuzzy rule table.

[0050] Figure 5 This is a schematic diagram of the defuzzification of error and error rate of change. Detailed Implementation

[0051] Please see Figure 1-5 As shown, this invention relates to an adaptive adjustment method for joint motor parameters of a quadruped robot based on fuzzy control, comprising the following steps:

[0052] S1: Calculate the current error and error change rate based on the current reference command and feedback current;

[0053] S2: Divide the current error and error change rate into minimum, small, medium and large, fuzzify them using the triangular membership function and form fuzzy sets, and calculate the membership degree of the fuzzy sets;

[0054] S3: Based on the fuzzy rules, obtain the fuzzy subsets and membership degrees of the proportional coefficient and integral coefficient;

[0055] S4: Obtain the clear values ​​of the proportional coefficient and integral coefficient through the inverse triangular membership function, and transfer the clear values ​​to the proportional-integral parameters of the current loop.

[0056] Specifically, this method is as follows: Figure 1 As shown, i q This represents the q-axis current of the motor. k represents the rate of change of error. p k represents the proportionality coefficient. iThis represents the integral coefficient. First, current error is calculated based on the current reference command and feedback current. The current error and error rate of change are then calculated, followed by fuzzification to obtain the membership degrees of the corresponding fuzzy sets. For fuzzy rule application, the fuzzy subset with the highest membership degree is selected, and the proportional and integral coefficients and their corresponding membership degrees are determined according to the designed fuzzy rules. Finally, defuzzification is performed. Through the defuzzification process, the specific values ​​of the proportional and integral coefficients are derived from the fuzzy sets and their membership degrees. The current loop PI parameters are then modified, and the proportional and integral coefficients calculated by the fuzzy controller are applied to the current loop PI controller for current loop control.

[0057] In the control of the joint motors of a quadruped robot, the PI controller automatically adjusts the values ​​of the proportional and integral coefficients based on the real-time motor operating status and the desired target to achieve precise motor control. Through fuzzy control, the PI parameters are dynamically adjusted according to different working conditions and load changes, improving the robot's adaptability and stability. For example, when the quadruped robot walks on different terrains, the load changes. The PI controller adjusts the parameters to ensure the motor outputs appropriate torque, guaranteeing smooth movement. When climbing slopes, which require greater torque, the PI controller increases the values ​​of the proportional and integral coefficients to increase the motor's output power; when walking on flat ground, the parameter values ​​are appropriately decreased to reduce motor energy consumption.

[0058] Furthermore, the current error and the rate of change of error include: the rate of change of error is filtered by a first-order low-pass filter to remove high-frequency sampling errors, and the error and the rate of change of error are normalized to the range of 0 to 1.

[0059] Specifically, for acquiring the current reference command and feedback current, the current reference command is typically the desired current value determined based on the motion control requirements of the quadruped robot, while the feedback current is the actual current value of the joint motors measured in real time by devices such as current sensors. When calculating the current error, the current error is defined as the difference between the current reference command and the feedback current, i.e.: Current error (e) = Current reference command - Feedback current. To calculate the error change rate, the current error value needs to be acquired at different time points. The error change rate is defined as the difference in current error between two adjacent time points divided by the time interval, i.e.: Error change rate (ec) = (Current time point current error - Previous time point current error) / Time interval.

[0060] In actual sampling processes, various factors may introduce high-frequency noise and interference, which can affect the accuracy of the error rate of change. A first-order low-pass filter effectively filters out these high-frequency errors, making the error rate of change data more stable. High-frequency sampling errors can lead to instability in the control system, especially in fuzzy control-based adaptive parameter adjustment. Inaccurate error rates of change can cause errors in the fuzzy rule judgments, thus affecting the control effect. Filtering out high-frequency errors can enhance the robustness of the system and improve its resistance to various disturbances. Accurate error rates of change are crucial for adjusting the proportional and integral coefficients. The error rate of change after low-pass filtering provides more reliable information, enabling the fuzzy control algorithm to more accurately determine the appropriate parameter adjustment direction and magnitude, thereby improving the system's control performance.

[0061] After normalization, errors and error rates of change of different magnitudes are unified to the same scale range, making subsequent fuzzification processing more convenient and accurate, and simplifying the design of fuzzy rules. Regardless of the original magnitude of the error and error rate of change, they can be fuzzily partitioned within the range of 0 to 1, determining their degree of belonging to each fuzzy set. Without normalization, the error and error rate of change may exhibit extremely large or small values. In fuzzy control, these extreme values ​​may lead to unstable or unreasonable results in the fuzzy inference process. Normalization limits the values ​​to a reasonable range, reducing such instability and facilitating comparison and analysis.

[0062] Furthermore, the calculation of the membership degree of the fuzzy set includes the following steps:

[0063] S21: Divide the current error and the rate of change of error into four fuzzy sets: minimal, small, medium and large, denoted as {Z0, PS, PM, PB}. Determine the universe of discourse in the range of 0 to 1 based on the normalization of the error and the rate of change of error.

[0064] S22: For each fuzzy set, the change in membership degree of the fuzzy set in different intervals is determined by the coordinates (a, b, c) of the three vertices of the triangle in the triangular membership function;

[0065] S23: Given a value x for current error or error change rate, determine the interval to which the value x belongs in {Z0, PS, PM, PB}, and calculate the membership degree according to the formula for the triangular membership function.

[0066] In step S23, the formula for calculating the membership function of the triangle is:

[0067] When a≤x≤b When b≤x≤c Where U represents the membership degree.

[0068] Specifically, the current error and error rate of change are divided into four fuzzy sets: minimal (Z0), small (PS), medium (PM), and large (PB). These fuzzy sets are defined relatively and describe the magnitude of the error and error rate of change. The error and error rate of change are normalized to the range of 0 to 1, meaning that all error and error rate of change values ​​are mapped to this range for fuzzification processing. Figure 2 The diagram shows the fuzzy representation of the error and the rate of change of error.

[0069] For each fuzzy set, the triangular membership function is used. The shape of the triangular membership function is determined by three vertices, each corresponding to a different degree of membership in the fuzzy set. Taking error as an example, suppose the coordinates of the three vertices of the triangle are (a1, b1, c1), (a2, b2, c2), (a3, b3, c3), etc. The values ​​of these vertices are determined based on the specific fuzzy set and the universe of discourse. For example, for the Z0 (minimal) fuzzy set, the vertex coordinates of the triangle could be (0, 0.1, 0.2), meaning that when the error is between 0 and 0.1, the membership gradually increases from 0 to 1; when it is between 0.1 and 0.2, the membership gradually decreases from 1 to 0; and outside this range, the membership is 0. For the PS (small) fuzzy set, the vertex coordinates could be (0.1, 0.3, 0.5), and when the error is between 0.1 and 0.3, the membership gradually increases from 0 to 1; when it is between 0.3 and 0.5, the membership gradually decreases from 1 to 0. For the PM (medium) fuzzy set, the vertex coordinates can be (0.4, 0.6, 0.8). When the error is between 0.4 and 0.6, the membership degree gradually increases from 0 to 1; when it is between 0.6 and 0.8, the membership degree gradually decreases from 1 to 0. For the PB (large) fuzzy set, the vertex coordinates can be (0.7, 0.9, 1). When the error is between 0.7 and 0.9, the membership degree gradually increases from 0 to 1; when it is between 0.9 and 1, the membership degree gradually decreases from 1 to 0.

[0070] In calculating membership, let's take error as an example. Assume a given error value e = 0.15, and determine the interval where the error value e falls. 0.15 lies within the overlapping interval of the PS (small) fuzzy set and the PM (medium) fuzzy set. Calculate the membership degree in the PS fuzzy set: The coordinates of the vertex of the triangle in the PS fuzzy set are (0.1, 0.3, 0.5). Since 0.1 ≤ 0.15 ≤ 0.3, according to the formula... Given a = 0.1, b = 0.3, and x = 0.15, the membership degree of the error value in the PS (small) fuzzy set is 0.25. Similarly, the membership degree in the PM fuzzy set is calculated. The vertex coordinates of the triangle in the PM fuzzy set are (0.4, 0.6, 0.8). 0.15 is not within the main interval of the PM fuzzy set, but its membership degree can be determined by judging its relationship with adjacent intervals. Since 0.15 is less than the minimum vertex value of the PM fuzzy set (0.4), its membership degree in the PM fuzzy set is 0. The same method is used to calculate the error change rate ec. Assume a given error change rate value ec = 0.45. Determine the interval where ec lies; 0.45 is within the PM (medium) fuzzy set. Calculate its membership degree in the PM fuzzy set: the vertex coordinates of the triangle in the PM fuzzy set are (0.4, 0.6, 0.8). Since 0.4 ≤ 0.45 ≤ 0.6, according to the formula... Substituting this value, we get 0.25.

[0071] Furthermore, the fuzzy rule includes: the transfer function of the open-loop current loop system, with the formula: Where k p It is the proportionality coefficient, k i Here, s is the integral coefficient, s is the Laplace operator, R is the motor resistance, and L is the linear coefficient. q It is the q-axis inductance of the motor;

[0072] k p and k i The setting formula is: Where ω c It is system bandwidth;

[0073] The formula for tuning the closed-loop transfer function of the current loop to that of a first-order inertial system is as follows:

[0074] Specifically, such as Figure 3 The block diagram of the current loop control for the articulated motor shown reveals the open-loop transfer function, which describes the response characteristics of the current control system at different frequencies. By analyzing the frequency response of the transfer function, important characteristics such as system stability, response speed, and bandwidth can be understood. In practical applications, the open-loop transfer function is used to predict the performance of the articulated motor under different operating conditions. Tuning the closed-loop transfer function of the current loop into a first-order inertial system provides a relatively simple mathematical form, facilitating system analysis and design. By tuning the closed-loop transfer function into a first-order inertial system, mature control theory methods are used to analyze the system's stability, response speed, and performance indicators. In fuzzy control-based methods, tuning the closed-loop transfer function into a first-order inertial system makes it easier to design fuzzy rules and determine the parameters of the fuzzy controller to achieve adaptive control of the articulated motor, exhibiting good stability and reliability.

[0075] Furthermore, the system bandwidth ω c ,include:

[0076] When ω c When the volume is large, the system response is fast, and the motor generates large electromagnetic noise after being enabled.

[0077] When ω c When the system is slow, the motor generates a small amount of electromagnetic noise after being enabled.

[0078] According to ω c The size of the joint motor and its response performance are in four states: minimum, small, medium and large. The bandwidth values ​​corresponding to the four states are 0.5e3, 1e3, 1.5e3 and 2e3, respectively. The fuzzy set of the four states is {ZO, PS, PM, PB}.

[0079] k p The elements with complete membership are:

[0080] {kp.ZO=0.5e3*R, kp.PS=1e3*R, kp.PM=1.5e3*R, kp.PB=2e3*R};

[0081] k i The elements with complete membership are:

[0082] {ki.ZO=0.5e3*R, ki.PS=1e3*R, ki.PM=1.5e3*R, ki.PB=2e3*R}.

[0083] Specifically, in the current loop control of an articulated motor, the PI controller function and the motor function are multiplied to obtain the open-loop system transfer function of the current loop. The proportional and integral coefficients are set in specific ways. System bandwidth has a significant impact on the performance of the articulated motor. When the system bandwidth is large, the system response speed is faster, but the motor generates greater electromagnetic noise after being enabled; conversely, when the system bandwidth is small, the system response speed is slower, but the electromagnetic noise generated after the motor is enabled is smaller. More specifically, based on the system bandwidth, the response performance of the articulated motor is divided into four states: minimal, small, medium, and large. The bandwidth values ​​corresponding to these four states are 0.5e3, 1e3, 1.5e3, and 2e3, respectively, and their fuzzy set representation is {ZO (minimal), PS (small), PM (medium), PB (large)}. By adjusting the system bandwidth, a trade-off is struck between response speed and electromagnetic noise to meet the needs of different application scenarios. The proportional and integral coefficients play a crucial role in this process. By fuzzifying the current error and error rate of change, fuzzy subsets and their membership degrees of the proportional and integral coefficients are determined using fuzzy rules. The full membership element plays a crucial role in determining the proportional and integral coefficients: it represents the degree to which the proportional and integral coefficients completely belong to a certain fuzzy subset under specific conditions. When the error and error rate of change are in a specific state, the fuzzy subset to which the proportional and integral coefficients belong is determined by querying the fuzzy rule table. In this case, the full membership element helps to more accurately determine the values ​​of the proportional and integral coefficients, thereby achieving more precise adjustment of the joint motor parameters. When the fuzzy subset of the error and error rate of change is determined to be in a specific state, if a full membership element exists in the corresponding fuzzy subset of the proportional and integral coefficients, then the proportional and integral coefficient values ​​corresponding to that element can be applied with greater confidence to adjust the current loop PI parameters, thereby improving the control performance and stability of the joint motor.

[0084] Furthermore, the fuzzy rules include:

[0085] When a quadruped robot is in motion, the electronic control system needs to respond quickly. During the motion process, the mechanical noise is much greater than the electromagnetic noise, so the joint motors switch to a large system bandwidth.

[0086] When the quadruped robot is standing, the electronic control system is in a steady state, and the only noise of the quadruped robot is electromagnetic noise, then the joint motors switch to a small system bandwidth.

[0087] When a quadruped robot switches from a standing state to a moving state, the electronic control system needs to respond quickly. During the movement process, the mechanical noise is much greater than the electromagnetic noise, so the joint motors immediately switch from the small system bandwidth to the large system bandwidth.

[0088] Specifically, regarding the impact of noise on quadruped robots, these robots are typically equipped with various sensors to monitor parameters such as joint position, velocity, and current. Noise can interfere with the signals of these sensors, leading to inaccurate measurements, and it can also affect the stability of control algorithms. The electromagnetic noise of quadruped robots can interfere with the control signals of the motors, affecting their normal operation. This directly impacts the performance of the joint motors, and consequently, the motion control of the quadruped robot. Inaccurate motor parameter measurements can also affect the accuracy of adaptive parameter adjustment algorithms.

[0089] When the quadruped robot is in motion, the joint motors are switched to high system bandwidth. With high system bandwidth, the system response speed is faster, meeting the requirements for rapid response of the electronic control system during motion. Although this generates significant electromagnetic noise, the increase in electromagnetic noise from the high system bandwidth is relatively negligible because the mechanical noise during motion is far greater than the electromagnetic noise.

[0090] When the quadruped robot is standing, the joint motors are switched to low system bandwidth. With low system bandwidth, the system response is relatively slow, but the generated electromagnetic noise is lower. Since the electronic control system is in a steady state when standing, the response speed requirement is not high, and electromagnetic noise becomes the primary source of concern. Low system bandwidth can effectively reduce electromagnetic noise.

[0091] When a quadruped robot switches from a standing to a moving state, the electronic control system needs to respond rapidly. To meet this requirement, the joint motors should immediately switch from a small system bandwidth to a large system bandwidth. This ensures that the electronic control system can quickly adjust to the needs of the moving state during state transitions, and since mechanical noise is much greater than electromagnetic noise during movement, the increase in electromagnetic noise from a large system bandwidth will not significantly affect system performance.

[0092] Furthermore, the fuzzy rules include:

[0093] When a large current error occurs with a small error rate of change, the motor is in a state of large steady-state error. In this case, the integral coefficient of the joint motor should be increased separately.

[0094] When a small current error occurs with a large error rate of change, the motor is in an unstable state. In this case, the proportional coefficient and integral coefficient of the joint motor should be reduced.

[0095] Specifically, when a large current error and a small rate of change of error occur, the motor is in a state of large steady-state error. In this case, to reduce the steady-state error, the integral coefficient of the motor is increased separately. The integral action is mainly used to eliminate steady-state error. By increasing the integral coefficient, the system's ability to accumulate error is enhanced, thereby gradually reducing the steady-state error and allowing the motor to approach the desired operating state more quickly.

[0096] When a small current error occurs with a large rate of change of error, the motor is in an unstable state. To restore system stability, the proportional and integral coefficients of the motor are reduced. The proportional coefficient mainly affects the system's response speed and immediate response to errors, while the integral coefficient, as mentioned earlier, is used to eliminate steady-state errors. Under a large rate of change of error, the system may oscillate or become unstable. Reducing the proportional coefficient can decrease the system's overreaction to errors and reduce the oscillation amplitude; simultaneously, reducing the integral coefficient can prevent excessive accumulation of errors by the integral action.

[0097] The joint motor parameter adjustment method described above, tailored to different combinations of current errors and error change rates, effectively adapts to the actual operating state of the motor, improving system stability and reliability and enhancing the performance of the quadruped robot under various working conditions. Furthermore, the method of this invention is adaptive and flexible, capable of dynamic adjustment based on real-time operating conditions without manual intervention, providing strong technical support for the intelligent control of quadruped robots.

[0098] Further, obtaining the fuzzy subsets and membership degrees of the proportional coefficients and integral coefficients includes:

[0099] Traverse step S2 to obtain the membership degrees of current error and error change rate respectively. Select the maximum membership degree and determine the fuzzy subset of current error and error change rate in terms of minimum, small, medium, and large based on the maximum membership degree. By querying the fuzzy subset of proportional coefficient and integral coefficient to which the fuzzy subset of current error and error change rate belongs, determine the fuzzy subset of control parameters. The maximum value of the membership degree of error and error change rate is used as the membership degree of proportional coefficient and integral coefficient.

[0100] Specifically, the current error and error change rate are first fuzzified. Using a specific fuzzification method, different fuzzy sets (minimal, small, medium, large) are assigned membership degrees to the current error and error change rate. Then, the membership degrees of the fuzzified current error and error change rate are traversed, and the maximum membership degree is selected. Based on this maximum membership degree, the specific fuzzy subset (minimal, small, medium, large) to which the current error and error change rate belong is determined. Then, by querying a pre-established fuzzy rule table, the fuzzy subsets of the proportional and integral coefficients to which the fuzzy subset of the current error and error change rate belongs are determined. This determines the fuzzy subset of the control parameters, providing a basis for further determining specific control parameter values. Finally, the maximum value of the membership degrees of the error and error change rate is used as the membership degree of the proportional and integral coefficients. This approach ensures that the membership degrees of the proportional and integral coefficients fully reflect the main characteristics of the current error and error change rate, improving the accuracy and reliability of the control parameter determination. The fuzzy rule table is as follows: Figure 4 As shown.

[0101] This method adaptively determines the fuzzy subsets and membership degrees of the proportional and integral coefficients based on the real-time status of the current error and its rate of change, thereby enabling precise adjustment of the joint motor parameters of the quadruped robot. This approach improves the system's adaptability and robustness, maintaining good control performance under varying operating conditions. Furthermore, the application of fuzzification and fuzzy rules reduces reliance on precise mathematical models, enhancing the system's flexibility and versatility.

[0102] Furthermore, obtaining the clear values ​​of the proportional coefficient and integral coefficient through the inverse triangular membership function includes:

[0103] Based on the fuzzy subsets and membership degrees of the obtained proportional coefficients and integral coefficients, the clear values ​​of the proportional coefficients and integral coefficients are obtained through the inverse triangular membership function.

[0104] The inverse trigonometric membership function, in a trigonometric function, when a membership degree corresponds to two proportional coefficients or integral coefficients, selects the smaller coefficient of the two coefficients as the clear value of the proportional coefficient, and selects the larger coefficient of the two coefficients as the clear value of the integral coefficient.

[0105] Specifically, in the application of the inverse triangular membership function, for the fuzzy subset corresponding to the proportional coefficient assumption, the vertex coordinates of the triangular membership function are (a1, b1, c1). According to the properties of the inverse triangular membership function, when the membership degree is... When: When a1≤x≤b1, according to the formula It can be obtained When b1≤x≤c1, according to the formula It can be obtained This yields two possible solutions. For the integral coefficients, assuming similar assumptions to the above, the vertex coordinates of the triangular membership function of its fuzzy subset are (a2, b2, c2). When the membership degree is U... ki At that time, two possible solutions can be obtained through similar derivation, such as Figure 5 As shown.

[0106] For the proportionality coefficient, if the two solutions obtained from the previous derivation are k p 1 and k p 2, and k p 1 < k p 2, then choose k p 1 as k p The clear value. For the integral coefficients, if the two solutions obtained are k i 1 and k i 2, and k i 1 < k i 2, then choose k i2 as k i The clarity value.

[0107] By following the steps above, we can determine the value of k. p k i The fuzzy subsets and their membership degrees are obtained by using the inverse triangular membership function to obtain clear values, and reasonable selection is made when there are two solutions, so as to provide specific control parameter values ​​for the adaptive adjustment of the joint motor parameters of the quadruped robot.

[0108] Furthermore, the transmission of the clear value to the current loop proportional-integral parameter includes:

[0109] Once the clear value is determined to be within a reasonable range, update the proportional and integral parameters of the PI controller. After updating the PI controller parameters, start the quadruped robot's joint motors and observe the operating status, which includes current response, speed change, and position accuracy.

[0110] Specifically, after obtaining clear proportional and integral coefficient values, the first step is to check whether these values ​​are within a reasonable range. If the values ​​are too large or too small, it may lead to system instability or poor control performance. This is achieved through the PI controller section of the current loop in the quadruped robot's joint motor control system, which clearly defines the k... p The value is assigned to the proportional coefficient, thus updating the proportional parameters in the PI controller. Similarly, the clear k... i The value is assigned to the integral coefficient, updating the integral parameters in the PI controller.

[0111] After updating the PI parameters, conduct system testing by starting the quadruped robot's joint motors and observing its operating status, including current response, speed changes, and positional accuracy. If system instability, oscillation, or unsatisfactory response speed is found, readjust the fuzzy control rules or the selection strategy of the inverse triangular membership function to obtain a more suitable k. p and k i value.

[0112] The above embodiments are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for adaptive adjustment of joint motor parameters of a quadruped robot based on fuzzy control, characterized in that, Includes the following steps: S1: Calculate the current error and error change rate based on the current reference command and feedback current; S2: Divide the current error and error change rate into minimum, small, medium and large, fuzzify them using the triangular membership function and form fuzzy sets, and calculate the membership degree of the fuzzy sets; S3: Based on the fuzzy rules, obtain the fuzzy subsets and membership degrees of the proportional coefficient and integral coefficient; S4: Obtain the clear values ​​of the proportional coefficient and integral coefficient through the inverse triangular membership function, and transfer the clear values ​​to the current loop proportional-integral parameters; The fuzzy rules include: the transfer function of the open-loop current loop system, with the formula: ,in It is a proportionality coefficient. Here, s is the integral coefficient, s is the Laplace operator, and R is the motor resistance. It is the q-axis inductance of the motor; and The setting formula is: ,in It is system bandwidth; The formula for tuning the closed-loop transfer function of the current loop to that of a first-order inertial system is as follows: ; The fuzzy rules also include: When the quadruped robot is in motion, the joint motors switch to high system bandwidth. When the quadruped robot is standing, the joint motors switch to low system bandwidth. When the quadruped robot switches from a standing state to a moving state, the joint motors immediately switch from a small system bandwidth to a large system bandwidth; When a large current error occurs with a small error rate of change, the integral coefficient of the joint motor should be increased separately. When a small current error occurs with a large error rate of change, the proportional coefficient and integral coefficient of the joint motor should be reduced.

2. The method for adaptive adjustment of joint motor parameters of a quadruped robot based on fuzzy control according to claim 1, characterized in that, The current error and error rate of change include: the error rate of change is filtered by a first-order low-pass filter to remove high-frequency sampling errors, and the error and error rate of change are normalized to the range of 0 to 1.

3. The method for adaptive adjustment of joint motor parameters of a quadruped robot based on fuzzy control according to claim 2, characterized in that, The calculation of the membership degree of the fuzzy set includes the following steps: S21: Divide the current error and the rate of change of error into four fuzzy sets: minimal, small, medium and large, denoted as {ZO,PS,PM,PB}. Determine the universe of discourse in the range of 0 to 1 based on the normalization of the error and the rate of change of error. S22: For each fuzzy set, the change in membership degree of the fuzzy set in different intervals is determined by the coordinates (a, b, c) of the three vertices of the triangle in the triangular membership function; S23: Given a value x for current error or error change rate, determine the interval to which the value x belongs in {ZO,PS,PM,PB}, and calculate the membership degree according to the formula for the triangular membership function. In step S23, the formula for calculating the membership function of the triangle is: when hour, ;when hour, , where U represents the membership degree.

4. The method for adaptive adjustment of joint motor parameters of a quadruped robot based on fuzzy control according to claim 1, characterized in that, The system bandwidth ,include: when When the volume is large, the system response is fast, and the motor generates large electromagnetic noise after being enabled. when When the system is slow, the motor generates a small amount of electromagnetic noise after being enabled. according to The size of the joint motor and its response performance are in four states: minimum, small, medium and large. The bandwidth values ​​corresponding to the four states are 0.5e3, 1e3, 1.5e3 and 2e3, respectively. The fuzzy set of the four states is {ZO, PS, PM, PB}. The elements with complete membership are: ； The elements with complete membership are: 。 5. The method for adaptive adjustment of joint motor parameters of a quadruped robot based on fuzzy control according to claim 1, characterized in that, The acquisition of the fuzzy subsets and membership degrees of the proportional coefficient and integral coefficient includes: Traverse step S2 to obtain the membership degrees of current error and error change rate respectively. Select the maximum membership degree and determine the fuzzy subset of current error and error change rate in terms of minimum, small, medium, and large based on the maximum membership degree. By querying the fuzzy subset of proportional coefficient and integral coefficient to which the fuzzy subset of current error and error change rate belongs, determine the fuzzy subset of control parameters. The maximum value of the membership degree of error and error change rate is used as the membership degree of proportional coefficient and integral coefficient.

6. The method for adaptive adjustment of joint motor parameters of a quadruped robot based on fuzzy control according to claim 5, characterized in that, The method of obtaining clear values ​​for the proportional coefficient and integral coefficient through the inverse triangular membership function includes: Based on the fuzzy subsets and membership degrees of the obtained proportional coefficients and integral coefficients, the clear values ​​of the proportional coefficients and integral coefficients are obtained through the inverse triangular membership function. The inverse trigonometric membership function, in a trigonometric function, when a membership degree corresponds to two proportional coefficients or integral coefficients, selects the smaller coefficient of the two coefficients as the clear value of the proportional coefficient, and selects the larger coefficient of the two coefficients as the clear value of the integral coefficient.

7. The method for adaptive adjustment of joint motor parameters of a quadruped robot based on fuzzy control according to claim 1, characterized in that, The process of transmitting the clear value to the current loop proportional-integral parameter includes: Once the clear value is determined to be within a reasonable range, update the proportional and integral parameters of the PI controller. After updating the PI controller parameters, start the quadruped robot's joint motors and observe the operating status, which includes current response, speed change, and position accuracy.

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