An adaptive sliding mode variable structure control method and system for robots

Abstract

The application relates to the technical field of robot control, in particular to a self-adaptive sliding mode variable structure control method and system for a robot, which comprises the following steps: obtaining sensor data of a motion motor of the robot and a trajectory tracking controller of a corresponding control system, time series decomposing the sensor data and combining the decomposition results to carry out feature extraction, obtaining a feature group of the sensor data, combining the feature group of the sensor data and chattering of a state point on a sliding mode surface to determine a chattering influence coefficient of the sensor data, thereby obtaining an adjusting coefficient of an approaching factor in the trajectory tracking controller to adjust the approaching factor in the trajectory tracking controller, and realizing fast and accurate tracking of a desired trajectory by the robot motion motor. The application realizes the collaborative promotion of control precision and dynamic performance, and enables the robot to still maintain good motion control effect in a complex and changeable working environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of robot control, and in particular relates to an adaptive sliding mode variable structure control method and system for a robot. BACKGROUND

[0002] The rapid development of embodied intelligence technology has driven the rapid innovation of robot intelligent control technology. In the process of sensing and interacting with the environment, the motion motor of the robot needs to be accurately controlled. Since the DC torque motor has the characteristics of large torque coefficient, fast reaction speed, small torque and speed fluctuation, it is usually selected to be applied to the position servo system and low-speed servo system. However, the DC torque motor is easily affected by problems such as friction, vibration and load mutation caused by the transmission mechanism during operation. Therefore, a method of using sliding mode variable structure control for robot motion control is proposed.

[0003] In the existing sliding mode variable structure control method, although the chattering can be suppressed to a certain extent and the convergence speed can be improved by improving the reaching law, the controller parameters cannot be effectively adjusted according to the actual running state of the robot and the external disturbance. Therefore, when the working environment or load condition of the robot changes, the controller cannot effectively suppress the chattering problem, which further leads to insufficient motion control precision of the robot. SUMMARY

[0004] The present application provides an adaptive sliding mode variable structure control method and system for a robot to solve the existing problems.

[0005] The adaptive sliding mode variable structure control method and system for a robot provided by the present application adopt the following technical solutions:

[0006] An embodiment of the present application provides an adaptive sliding mode variable structure control method for a robot, which comprises the following steps:

[0007] Obtain sensor data of the motion motor of the robot and a trajectory tracking controller of the corresponding control system, wherein the trajectory tracking controller contains a reaching factor;

[0008] Perform time series decomposition on the sensor data of the motion motor, and perform feature extraction on the data variation characteristics in the decomposition result to obtain a feature group of the sensor data;

[0009] Determine the chattering influence coefficient of each sensor data in combination with the chattering situation of the feature group of the sensor data and the state point of the control system on the sliding mode surface, and obtain the adjustment coefficient of the reaching factor in the trajectory tracking controller based on the chattering influence coefficient of all sensor data.

[0010] The approach factor in the trajectory tracking controller is adjusted using an adjustment coefficient, and the motion motor of the robot is controlled by the trajectory tracking controller.

[0011] Optionally, the specific method for performing time-series decomposition on the sensor data of the motion motor includes:

[0012] The EEMD signal decomposition algorithm is used to perform mode decomposition on arbitrary sensor data, and the quantity control function is determined during the mode decomposition process.

[0013] Based on the numerical change trend of the quantity control function, several IMF component data corresponding to the sensor data are obtained.

[0014] Optionally, the method for obtaining the quantity control function is as follows:

[0015] For any sensor data, the number of noise additions K of the EEMD signal decomposition algorithm is preset. All noise addition processes and the EMD decomposition process of the sensor data after noise addition are processed in parallel. Based on the fluctuation characteristics of the IMF component data decomposed in each parallel process, the quantity control function under the mode decomposition process of sensor data is obtained.

[0016] Optionally, the specific method for obtaining the quantity control function includes:

[0017] Any sensor data is denoted as target sensor data. K white noise sequences with a mean of 0 and different standard deviations are obtained and added to the target sensor data respectively to obtain K noisy target sensor data. For any noisy target sensor data, iterative EMD decomposition is performed on the noisy target sensor data. Each decomposition yields IMF component data of the corresponding order. During the iterative EMD decomposition of all noisy target sensor data, the data points in the corresponding IMF component data at each order are analyzed for fluctuation and similarity to construct the quantity control function at the corresponding order.

[0018] Optionally, the specific method for obtaining K white noise sequences with a mean of 0 and different standard deviations includes:

[0019] Obtain the standard deviation of all extreme points in the target sensor data, and then... As the range of standard deviation values ​​for the K white noise sequences, within the range of standard deviation values, As the initial value, every We take values ​​to represent the standard deviation of all elements in each white noise sequence, thus obtaining K white noise sequences with a mean of 0 and different standard deviations; where a standard deviation of all extreme points in the target sensor data, a preset range parameter.

[0020] Optionally, the method for obtaining the plurality of IMF component data corresponding to the target sensor data based on the numerical change trend of the quantity control function comprises the following steps:

[0021] A two-dimensional rectangular coordinate system is constructed, the numerical value of the quantity control function is taken as the vertical axis of the two-dimensional rectangular coordinate system, the order of the IMF component data is taken as the horizontal axis of the two-dimensional rectangular coordinate system, the quantity control function value of the IMF component data at each order of the target sensor data is mapped into the two-dimensional rectangular coordinate system to form a corresponding scatter plot, which is recorded as the quantity control function scatter plot of the target sensor data; the quantity control function scatter plot of the target sensor data is curve-fitted by the least square method, the elbow method is used to obtain the inflection point in the curve-fitting result of the quantity control function scatter plot, the order closest to the inflection point is taken as the order of the IMF component data of the target sensor data, which is recorded as the target order of the target sensor data, so that the IMF component data of the target sensor data is obtained by the EEMD signal decomposition algorithm, and the order of the IMF component data of the target sensor data is the corresponding target order.

[0022] Optionally, the specific method for obtaining the feature group of the sensor data comprises the following steps:

[0023] For each IMF component data of the target sensor data, the instantaneous frequency sequence and the amplitude envelope sequence of the IMF component data are calculated by using the Hilbert-Huang transform, the average frequency, the frequency bandwidth and the energy proportion of the IMF component data are calculated based on the instantaneous frequency sequence and the amplitude envelope sequence, and the array formed by the average frequency, the frequency bandwidth and the energy proportion of the IMF component data is taken as the feature group of the IMF component data, so as to obtain the feature groups of all the IMF component data of the target sensor data.

[0024] Optionally, the specific method for determining the chattering influence coefficient of each sensor data by combining the feature group of the sensor data and the chattering condition of the state point of the control system on the sliding surface comprises the following steps:

[0025] Real-time acquisition of the sliding mode surface deviation, constructing a window at the current time and taking the current time as the rightmost time of the window, accumulating the absolute value of the sliding mode surface deviation of all times in the window as the chattering index of the current time, taking the IMF component data with the largest energy proportion in the corresponding feature group of the target sensor data as the main IMF component data of the target sensor data, obtaining the feature group of the main IMF component data, and calculating the absolute value mean of the cross-correlation coefficient of all elements in the feature group and the chattering index as the chattering influence coefficient of the target sensor data.

[0026] Optionally, the chattering influence coefficient based on all sensor data obtains an adjustment coefficient of the approaching factor in the trajectory tracking controller, and the specific method comprises the following steps:

[0027] A preset reference chattering level is obtained, the ratio of the reference chattering level and the chattering index at the current time is obtained, and the normalized result is taken as the chattering deviation value at the current time by inputting the ratio into a sigmoid function for normalization processing.

[0028] The adjustment coefficient is calculated in combination with the chattering influence coefficient and the chattering deviation value at the current time, and the adjustment coefficient is positively correlated with the chattering influence data and the chattering deviation value.

[0029] An adaptive sliding mode variable structure control system for a robot comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of any one of the adaptive sliding mode variable structure control methods for the robot.

[0030] The technical scheme of the present application has the following beneficial effects: after obtaining sensor data, the key feature group reflecting the change of the system state is effectively identified through time series decomposition and feature extraction of the data, providing a reliable basis for the adaptive adjustment of the subsequent control parameters, and on this basis, the influence degree of different sensor data on the system chattering is further evaluated in combination with the chattering of the sliding mode surface state, which enables the controller to dynamically perceive external disturbances and internal state changes according to the actual working conditions, and then generates a reasonable adjustment coefficient for optimizing the approaching factor in the trajectory tracking controller, and the adaptive adjustment of the approaching factor significantly enhances the robustness of the system to uncertain disturbances and load fluctuations, which not only suppresses the chattering phenomenon commonly seen in traditional sliding mode control, but also ensures the rapidity and stability of the system response, so that the embodiment of the present application realizes the cooperative improvement of control precision and dynamic performance, and the robot can still maintain good motion control effect in a complex and variable working environment. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0032] Figure 1 A step flow chart of an adaptive sliding mode variable structure control method for a robot according to an embodiment of the present application;

[0033] Figure 2 A deviation curve diagram of a state point and a sliding surface according to an embodiment of the present application;

[0034] Figure 3 A structural block diagram of an adaptive sliding mode variable structure control system for a robot according to an embodiment of the present application. DETAILED DESCRIPTION

[0035] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined purposes, the following will combine the drawings and the preferred embodiments to specifically describe the adaptive sliding mode variable structure control method and system for a robot according to the present application, the specific implementation, structure, features and effects thereof, in detail as follows. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0037] The following will specifically describe the specific scheme of the adaptive sliding mode variable structure control method and system for a robot according to the present application in combination with the drawings.

[0038] Please refer to Figure 1 which shows a step flow chart of an adaptive sliding mode variable structure control method for a robot according to an embodiment of the present application. The method comprises the following steps:

[0039] Step S001: obtaining sensor data of a motion motor of a robot and a trajectory tracking controller of a corresponding control system.

[0040] It should be noted that in the process of controlling the motion motor of the robot by the sliding mode variable structure control method, the sliding surface is usually designed according to the motor system of the robot first, so that the system has the desired dynamic performance on the sliding surface, and further based on the sliding surface, the corresponding control rate is constructed to ensure that the state point of the control system can reach and remain on the sliding surface in a limited time, so as to apply the designed control rate to the robot motor control system, and adjust the control signal in real time to cope with the system parameter changes and external disturbances. However, the robot is affected by the change of the external environment during the movement, so that the load of the motor will also change, and therefore this change will aggravate the degree of the system in the sliding mode variable to cross back and forth, thereby causing the motion control accuracy of the robot to be reduced, and the action to be dithered. In the control system, the state point of the system makes a back-and-forth crossing motion on the sliding surface to produce chattering, as shown in Figure 2 The deviation curve diagram of the state point and the sliding surface is shown in the figure, wherein the horizontal axis is time and the vertical axis is deviation value. It can be seen from the curve that the deviation value of the early state fluctuates up and down, and the chattering phenomenon occurs. Therefore, in order to further weaken the chattering phenomenon of the motion motor of the robot in the sliding mode variable structure control, the working environment and working parameters of the motion motor of the robot are utilized to realize the self-adaptive adjustment of the controller parameters with external interference, so as to improve the motion control effect of the robot.

[0041] Specifically, in order to realize the self-adaptive sliding mode variable structure control method for the robot proposed in this embodiment, the working environment data and working parameter data of the motion motor of the robot need to be collected first, and the specific process is as follows:

[0042] Firstly, the sensor data of the motion motor of the robot is acquired in real time by using a plurality of sensors, and the sensor data is divided into working environment data and working parameter data.

[0043] The plurality of sensors include a vibration sensor, an electromagnetic field intensity sensor, a current sensor and a voltage sensor, and a torque sensor.

[0044] The working environment data includes vibration data and electromagnetic field intensity data.

[0045] The working parameter data includes current data, voltage data and torque data.

[0046] It should be noted that the data acquisition frequency of the sensor in the embodiment of the application is set to 200Hz, which can be adjusted according to the actual situation in other embodiments, and the embodiment of the application is not limited specifically.

[0047] Then, the acquired working environment data and working parameter data are standardized and filtered by a Gaussian filter.

[0048] Finally, the trajectory tracking controller of the motion motor of the robot is obtained by using the method provided in the paper “Terminal sliding mode trajectory tracking control of torque motor based on reaching law, Journal of Wuhan University of Science and Technology (Natural Science Edition), Wang Yan, Lu Yiyun, Ge Yunwang”, and the initial values of the parameters and in the trajectory tracking controller are preset, the parameters and are recorded as the first reaching factor and the second reaching factor, the first reaching factor and the second reaching factor are collectively referred to as the reaching factor, the reaching law term corresponding to the first reaching factor in the trajectory tracking controller is recorded as the first reaching term, and the reaching law term corresponding to the second reaching factor in the trajectory tracking controller is recorded as the second reaching term.

[0049] It should be noted that in the embodiment of the present application, according to the values of the first reaching factor and the second reaching factor in the paper “Terminal sliding mode trajectory tracking control of torque motor based on reaching law, Journal of Wuhan University of Science and Technology (Natural Science Edition), Wang Yan, Lu Yiyun, Ge Yunwang”, the initial values of the first reaching factor and the second reaching factor are preset as 5 and 20, respectively, that is, , and can be adjusted according to actual conditions, and the embodiment of the present application is not specifically limited.

[0050] In addition, it should be noted that the motion motor described in the embodiment of the present application is a direct current torque motor.

[0051] At this point, the working environment data and the working parameter data of the motion motor of the robot are obtained by the above method.

[0052] Step S002: Time series decomposition is performed on the sensor data of the motion motor, and feature extraction is performed in combination with the data change characteristics in the decomposition result to obtain a feature group of the sensor data.

[0053] It should be noted that in the process of movement of the robot driven by the movement motor, the movement trajectory of the motor is analyzed by the trajectory tracking controller, so as to control the movement motor. In order to weaken the chattering degree of the state point of the control system when approaching the sliding mode surface in the control process, the exponential approaching law and the power approaching law are combined in the torque motor terminal sliding mode trajectory tracking control based on approaching law in Journal of Wuhan University of Science and Technology (Natural Science Edition), Wang Yan, Lu Yiyun and Ge Yunwang, so as to make the state point of the control system quickly and smoothly reach the sliding mode surface. However, due to the change of the working environment of the motor in the actual movement process of the robot, and the working load acting on the motor, the actual control effect of the motor cannot be stable and accurate enough. Therefore, in the process of movement control of the robot, the actual working environment of the motor should also be analyzed, so that the controller parameters can be adaptively adjusted with external interference in the process of controlling the motor by the trajectory tracking controller. In addition, any mechanical system has a certain natural frequency and vibration mode in the running process, which constitutes the physical mode of the system. Therefore, in the process of sensing and interacting with the external environment, the working environment of the motor has a certain modal characteristic, and it is this modal characteristic that affects the working condition of the motor. Therefore, the working environment data and working parameter data of the movement motor are analyzed by the embodiment of the application to determine the corresponding modal characteristics.

[0054] Specifically, in step S201, the sensor data of the movement motor is modal decomposed to obtain a plurality of IMF component data corresponding to the sensor data respectively.

[0055] It should be noted that the robot has a specific interaction mode when interacting with the environment. For example, when the robot moves heavy objects, it shows a low-frequency, large-amplitude load change mode. When crossing small obstacles, it shows a medium-frequency, short-time pulse impact mode. When moving on soft ground, it shows a high-frequency, decaying oscillation adaptation mode. These interaction modes are transmitted to the motor through the mechanical transmission system, forming specific current and voltage waveform characteristics, and affecting the torque of the motor, thereby affecting the chattering degree of the sliding mode control. It can be seen that the robot system involves the coupling of multiple physical fields of mechanics and electricity in the process of driving its movement by the movement motor, forming a multi-physical field mode. Because mechanical vibration leads to changes in electrical load, and further changes in mechanical performance, the vibration characteristics change. It is this coupling that forms a complex feedback loop, which produces specific composite modal characteristics. Furthermore, it is these modal characteristics that determine the behavior pattern of the robot in a specific environment, which is the fundamental reason why the embodiment of the application selects the modal analysis of the working environment data and working parameter data of the movement motor to facilitate the subsequent further implementation of adaptive sliding mode variable structure control.

[0056] As a preferred embodiment, the method for performing modal decomposition on the sensor data of the motion motor to obtain several modal component data corresponding to the sensor data includes:

[0057] First, the EEMD signal decomposition algorithm is used to perform mode decomposition on arbitrary sensor data, and the quantity control function is determined during the mode decomposition process.

[0058] It should be noted that the EEMD (Ensemble Empirical Mode Decomposition) signal decomposition algorithm is an existing empirical mode decomposition algorithm, and therefore will not be described in detail in this embodiment of the invention.

[0059] As a preferred embodiment, the method for obtaining the quantity control function is as follows:

[0060] For any sensor data, the preset number of noise additions in the EEMD signal decomposition algorithm is... All noise-adding processes and the EMD decomposition process of the sensor data after noise addition are processed in parallel. Based on the fluctuation characteristics of the IMF component data decomposed in each parallel processing, the quantitative control function under the mode decomposition process of sensor data is obtained.

[0061] It should be noted that, in the embodiments of the present invention, the number of noise additions K in the EEMD signal decomposition algorithm is preset to 20 based on experience, which can be adjusted according to the actual situation. The embodiments of the present invention do not impose specific limitations.

[0062] As an optional embodiment, the specific method for obtaining the quantity control function includes: denoting any sensor data as target sensor data; obtaining K white noise sequences with a mean of 0 and different standard deviations; adding the white noise sequences to the target sensor data respectively to obtain K noisy target sensor data; for any noisy target sensor data, performing iterative EMD decomposition on the noisy target sensor data; obtaining IMF component data of the corresponding order in each decomposition; and constructing a quantity control function of the corresponding order based on the fluctuation degree and similarity of data points in the IMF component data of each order during the iterative EMD decomposition of all noisy target sensor data of the target sensor data.

[0063] As an optional embodiment, the acquisition The method for generating white noise sequences with a mean of 0 and different standard deviations includes: obtaining the standard deviation of all extreme points in the target sensor data, and then... The standard deviation of the K white noise sequences is taken as a value range, and the value is taken as an initial value in the value range of the standard deviation , every , and the standard deviation of all elements in each white noise sequence is taken as a value, thereby obtaining white noise sequences with a mean of 0 and different standard deviations; wherein is the standard deviation of all extreme points in the target sensor data, is a preset range parameter.

[0064] It should be noted that, since the EEMD signal decomposition algorithm decomposes data, the purpose of adding white noise in the sensor data is to eliminate the randomness by adding noise multiple times to avoid the phenomenon of modal aliasing in the IMF component data obtained in the subsequent decomposition. However, in the process of adding white noise, if the noise amplitude of the added white noise is too large, the sensor data will be overwhelmed, and if it is too small, it cannot suppress modal aliasing. Therefore, in the embodiment of the present application, the standard deviation of the white noise sequence is determined based on the standard deviation of the extreme points in the target sensor data when adding white noise to the target sensor data, thereby ensuring that the IMF component data obtained in the subsequent decomposition can effectively contain the modal information of the motor of the robot in the corresponding working environment or working parameter, so as to facilitate subsequent feature analysis using this modal information to further adjust the related parameters in the trajectory tracking controller of the motor, to weaken the chattering phenomenon of the state point on the sliding surface of the control system, and to improve the control accuracy of the motor. In addition, in the embodiment of the present application, the range parameter is empirically set to 1 / 2, which can be adjusted according to actual conditions, and the embodiment of the present application is not limited in detail.

[0065] As an optional embodiment, the specific calculation method of the quantity control function is: ; wherein, represents the quantity control function value of the IMF component data of the target sensor data of the th order; represents the standard deviation of the IMF component data of the th order of the sensor data after adding noise to the target sensor data of the th order; represents the normalized cross-correlation coefficient mean between the IMF component data of the th order of the sensor data after adding noise to the target sensor data of the th order and other K-1 corresponding IMFs; represents the number of the sensor data after adding noise to the target sensor data.

[0066] It should be noted that the quantity regulation function value is used to reflect the degree of modal characteristic information contained in the IMF component data of the corresponding order, the smaller the numerical value of the quantity regulation function value, the smaller the degree of modal characteristic information contained in the IMF component data of the corresponding order, and the higher the possibility of stopping further EMD decomposition at the order; wherein, is used to measure the fluctuation degree of the first order IMF under different noise disturbances. is used to reflect the similarity of the corresponding modal component under different noise environments; in addition, since the noise sequence is used to add white noise to the sensor data, the number of noise sequences is equal to the number of sensor data obtained after adding noise to any sensor data, that is and are equal in value.

[0067] It should be noted that the sensor data of the motion motor is composed of different aspects, i.e. mechanical level and electrical level, corresponding to working environment data and working parameter data respectively, since the system dynamic characteristics reflected by different physical quantities have essential differences and different modal components have different correlations with control targets, therefore, when the modal characteristics of different sensor data are used to adjust the approaching factor subsequently, the order of the IMF component data of the sensor data to be obtained is different; in addition, in the process of modal decomposition of the EEMD signal decomposition algorithm, the number of IMF component data finally obtained is preset by human, therefore, modal information may not be effectively reflected in part of the modal components, and therefore there is certain information redundancy, therefore, in the embodiment of the present application, the decomposition process of the EEMD signal decomposition algorithm is constructed by constructing the quantity regulation function, the quantity regulation function value of the IMF component data of the sensor data at different orders is determined, so as to balance the calculation amount of the obtained IMF component data and the accuracy of the subsequent adjustment of the trend factor, so that the system can more accurately identify the modal characteristics of the current working environment, and then adaptively adjust the sliding mode control parameters (i.e. the first trend factor and the second trend factor ), while maintaining the stability of the system, effectively suppressing chattering, and improving the precision and adaptability of the robot motion control.

[0068] Then, based on the numerical change trend of the quantity regulation function, a plurality of IMF component data corresponding to the sensor data are obtained.

[0069] As an optional embodiment, the method for obtaining a plurality of modal component data corresponding to the working environment data and the working parameter data respectively based on the quantity regulation function comprises the following specific steps: a two-dimensional rectangular coordinate system is constructed, a value of the quantity regulation function is taken as a vertical axis of the two-dimensional rectangular coordinate system, and an order of the IMF component data is taken as a horizontal axis of the two-dimensional rectangular coordinate system; the quantity regulation function values of the IMF component data at each order of the target sensor data are mapped into the two-dimensional rectangular coordinate system to form a corresponding scatter plot, which is recorded as a quantity regulation function scatter plot of the target sensor data; a curve fitting is performed on the quantity regulation function scatter plot of the target sensor data by using the least square method, an elbow method is used to obtain an inflection point in the curve fitting result of the quantity regulation function scatter plot, an order closest to the inflection point is taken as the order of the IMF component data of the target sensor data, and the order is recorded as a target order of the target sensor data, so that the IMF component data of the target sensor data is obtained by using the EEMD signal decomposition algorithm, and the order of the IMF component data of the target sensor data is the corresponding target order.

[0070] In step S202, a feature group of the sensor data is extracted based on modal characteristics corresponding to data changes in all IMF component data corresponding to the sensor data.

[0071] As an optional embodiment, the specific method for obtaining the feature group comprises the following steps: for each IMF component data of the target sensor data, a Hilbert-Huang transform is used to calculate an instantaneous frequency sequence and an amplitude envelope sequence of the IMF component data, an average frequency, a frequency bandwidth and an energy proportion of the IMF component data are calculated based on the instantaneous frequency sequence and the amplitude envelope sequence, and an array formed by the average frequency, the frequency bandwidth and the energy proportion of the IMF component data is taken as a feature group of the IMF component data, and the feature groups of all IMF component data of the target sensor data are obtained.

[0072] Up to now, the feature group of the sensor data is obtained by using the above method.

[0073] In step S003, a chattering influence coefficient of each sensor data is determined by combining the feature group of the sensor data and a chattering condition of the state point of the control system on the sliding surface, and an adjustment coefficient of the reaching factor in the trajectory tracking controller is obtained based on the chattering influence coefficients of all sensor data.

[0074] It should be noted that, by analyzing the modal characteristics embodied in the working environment data and the working parameter data due to the influence of the working environment during the process of driving the robot by the motor, the influence of different modal characteristics on the system chattering is further determined, so as to construct the adjustment degree of the first approaching factor and the second approaching factor in the trajectory tracking controller, so as to adjust the specific dominant degree when the first approaching term and the second approaching term in the trajectory tracking controller respectively play a leading role when the system approaches or moves away from the sliding mode surface, thereby further weakening the chattering phenomenon occurring in the actual motion control process, and improving the motion driving effect of the motor on the robot.

[0075] Specifically, in step S301, the chattering influence coefficient of each sensor data is determined in combination with the feature group of the sensor data and the chattering situation of the state point of the control system on the sliding mode surface.

[0076] As an optional embodiment, the specific acquisition method of the chattering influence coefficient is as follows: the sliding mode surface deviation is acquired in real time, a window at the current time is constructed, the current time is taken as the rightmost time of the window, and the absolute value cumulative value of the sliding mode surface deviation at all times in the window is taken as the chattering index of the current time; the IMF component data with the largest energy proportion in the corresponding feature group of the target sensor data is taken as the main IMF component data of the target sensor data, the feature group of the continuously acquired main IMF component data is acquired, and the absolute value mean of the cross-correlation coefficient of all elements in the feature group and the chattering index is taken as the chattering influence coefficient of the target sensor data.

[0077] It should be noted that in the embodiment of the application, the window length is empirically preset to 100, which can be adjusted in other embodiments, and the embodiment of the application is not specifically limited.

[0078] It should be further noted that the chattering index directly reflects the chattering intensity, and the greater the value of the chattering index, the more frequent the system passes through the vicinity of the sliding mode surface, and the more serious the control signal chattering; in addition, in the embodiment of the application, the energy proportion is most indicative of the modal influence, so the feature group of the IMF component data with the highest energy proportion in the sensor data is selected as the input feature, and the cross-correlation coefficient quantifies the synchronism of the modal energy change and the chattering intensity, and the greater the absolute value of the cross-correlation coefficient, the more significant the influence of the modal characteristics of the sensor data on the chattering.

[0079] It should be noted that the chattering influence coefficient reflects the coupling strength of the modal characteristics and the system dynamics, that is, when a specific modal is excited by the working environment (such as the interaction between the robot and the ground during the robot movement, causing high-frequency vibration of the motor), the chattering influence coefficient automatically increases, triggering the adaptive adjustment of the subsequent approaching factor, thereby specifically suppressing the chattering caused by the modal.

[0080] Step S302: Based on the jitter impact coefficient of all sensor data, obtain the adjustment coefficient of the approach factor in the trajectory tracking controller.

[0081] First, a reference jitter level is preset, the ratio of the reference jitter level to the jitter index at the current moment is obtained, and the ratio is input into the sigmoid function for normalization. The normalization result is used as the jitter deviation value at the current moment.

[0082] It should be noted that, in this embodiment of the invention, a reference chatter level is calibrated and preset through an interference-free experiment. In other embodiments, adjustments can be made based on actual circumstances; if the jitter index at the current moment... If the value is 0, it indicates that the chattering is slight, and the approach factor can be increased to improve the response speed; while when When the threshold is reached, it indicates severe chattering, requiring a reduction in the convergence factor to suppress chattering. This represents the jitter index at the current moment.

[0083] Then, by combining the jitter impact coefficient and the jitter deviation value at the current moment, an adjustment coefficient is calculated. The adjustment coefficient is positively correlated with both the jitter impact data and the jitter deviation value.

[0084] As an optional embodiment, the specific method for calculating the adjustment coefficient is as follows:

[0085]

[0086] in , Indicates the current time period of the first... One adjustment coefficient; This represents the chattering deviation value at the current moment. hour, and ;when hour, and ;when hour, ; Indicates the first The jitter impact coefficient of individual sensor data; This indicates the amount of sensor data.

[0087] It should be noted that, Provides adjustment direction and range; The adjustment coefficient represents the sensitivity to chattering, reflecting the influence of environmental modes. A larger adjustment coefficient increases the approach factor, resulting in a faster system response, which is suitable for stable environments. Conversely, a smaller adjustment coefficient decreases the approach factor, weakening abrupt changes in the control signal, which is suitable for environments with severe chattering.

[0088] At this point, the adjustment coefficient is obtained by the above method.

[0089] Step S004: adjusting the approaching factor in the trajectory tracking controller using the adjustment coefficient, and controlling the motion motor of the robot through the trajectory tracking controller.

[0090] It should be noted that the adjustment coefficient is applied to the trajectory tracking controller in this step, and the approaching factor is updated in real time to form an environment-adaptive sliding mode control law. The core is to suppress the sliding mode surface crossing chattering by dynamically adjusting the dominant degree of the first approaching term and the second approaching term, while maintaining system stability. Compared with the traditional fixed parameter sliding mode control, the embodiment of the application can automatically switch the control strategy when the environment changes suddenly. When a high-frequency adaptive mode is detected, the first approaching term is weakened to smooth the control signal; when the system is stable, the second approaching term is strengthened to improve the response speed.

[0091] Specifically, first, the adjustment coefficient at the current time is obtained and and the approaching factor in the trajectory tracking controller is updated. The updated approaching factor is substituted into the trajectory tracking controller to obtain the control signal.

[0092] As an optional embodiment, the specific calculation method of updating the approaching factor in the trajectory tracking controller includes: , wherein and are the preset initial values of the first approaching factor and the second approaching factor.

[0093] It should be noted that the update of the approaching factor is directly related to the environment mode. For example, when , , thereby reducing the strength of the first approaching term and reducing high-frequency chattering.

[0094] Then, the control signal is input into the driver of the motion motor. The driver adjusts the voltage of the direct current torque motor according to the control signal to drive the robot to execute the desired motion trajectory.

[0095] Finally, steps S001 to S004 are repeated at a preset control period to form a closed-loop adaptive control.

[0096] It should be noted that in the embodiment of the application, the driver of the motion motor is a PWM speed regulation module, and the closed-loop mechanism ensures that the controller continuously tracks the environment changes. For example, when the robot enters the sand from the hard ground, the high-frequency vibration mode is identified, The automatic reduction, the control signal smoothing, avoid action jitter; in addition, in the embodiment of the application, the control period is 5ms according to experience, can be adjusted according to actual situation, the embodiment of the application is not specifically limited.

[0097] Through the above steps, the control process of the motor of the robot is completed by the adaptive sliding mode variable structure control method.

[0098] Please refer to Figure 3 It shows an adaptive sliding mode variable structure control system for robot provided by an embodiment of the application, including memory 302, processor 301 and computer program 3021 stored in the memory 302 and can run on the processor, the processor 301 executes the computer program 3021 when realizing the steps S001 to S004 of the adaptive sliding mode variable structure control method for robot.

[0099] Further, in an alternative embodiment, the above-mentioned memory 302 can include read-only memory and random access memory, and provide instructions and data to the processor. The memory 302 can also include non-volatile random access memory. For example, the memory can also store device type information.

[0100] The above only describes the preferred embodiment of the application, and does not limit the application, any modification, equivalent replacement, improvement, etc. within the principles of the application, should be included in the protection scope of the application.

Claims

1. An adaptive sliding mode variable structure control method for a robot, characterized by, The method comprises the following steps: Obtain sensor data of a motion motor of a robot and a trajectory tracking controller of a corresponding control system, wherein the trajectory tracking controller contains a reaching factor; Perform time series decomposition on the sensor data of the motion motor, and perform feature extraction on the data variation characteristics in the decomposition results to obtain a feature group of the sensor data; Determine a chattering influence coefficient of each sensor data by combining the feature group of the sensor data and the chattering of the state point of the control system on the sliding surface, and obtain an adjustment coefficient of the reaching factor in the trajectory tracking controller based on the chattering influence coefficients of all the sensor data; Adjust the reaching factor in the trajectory tracking controller by using the adjustment coefficient, and control the motion motor of the robot through the trajectory tracking controller; The method for determining the chattering influence coefficient of each sensor data by combining the feature group of the sensor data and the chattering of the state point of the control system on the sliding surface comprises the following specific method: Obtain the sliding surface deviation in real time, construct a window at the current time and take the current time as the rightmost time of the window, and take the absolute value cumulative value of the sliding surface deviation at all times in the window as the chattering index at the current time; take the IMF component data with the largest energy proportion in the corresponding feature group of the target sensor data as the main IMF component data of the target sensor data, obtain the feature group of the main IMF component data, and calculate the absolute value mean of the cross-correlation coefficient of all elements in the feature group and the chattering index as the chattering influence coefficient of the target sensor data; The method for obtaining the adjustment coefficient of the reaching factor in the trajectory tracking controller based on the chattering influence coefficients of all the sensor data comprises the following specific method: Predefine a reference chattering level, obtain the ratio of the reference chattering level to the chattering index at the current time, and input it into a sigmoid function for normalization processing, and take the normalized result as the chattering deviation value at the current time; Combine the chattering influence coefficient and the chattering deviation value at the current time to calculate the adjustment coefficient, wherein the adjustment coefficient is positively correlated with the chattering influence data and the chattering deviation value.

2. The adaptive sliding mode variable structure control method for robots according to claim 1, characterized in that, The method for performing time series decomposition on the sensor data of the motion motor comprises the following specific method: Perform modal decomposition on any sensor data by using an EEMD signal decomposition algorithm, determine a quantity control function in the modal decomposition process; Based on the numerical variation trend of the quantity control function, obtain a plurality of IMF component data corresponding to the sensor data.

3. The adaptive sliding mode variable structure control method for robots according to claim 2, wherein The method for obtaining the quantity control function comprises the following steps: For any sensor data, preset the number of noise addition times K of the EEMD signal decomposition algorithm, perform EMD decomposition processes on all noise addition processes and the sensor data after noise addition in parallel, and obtain the quantity control function in the modal decomposition process on the sensor data according to the fluctuation variation characteristics of the IMF component data decomposed each time in the parallel processing process.

4. The adaptive sliding mode variable structure control method for robots according to claim 3, wherein, The specific method for obtaining the quantity control function comprises the following steps: The arbitrary sensor data is recorded as target sensor data, K white noise sequences with mean value of 0 and different standard deviations are obtained, and the white noise sequences are added to the target sensor data respectively to obtain K target sensor data after adding noise; for any one target sensor data after adding noise, the EMD decomposition of the target sensor data after adding noise is carried out iteratively, and each time decomposition obtains the IMF component data corresponding to the order, and in the iterative EMD decomposition process of all target sensor data after adding noise of the target sensor data, the data points in the corresponding IMF component data under each order are in the degree of fluctuation change and the similar situation, and the number control function under the corresponding order is constructed.

5. The adaptive sliding mode variable structure control method for robots according to claim 4, wherein, The specific method for obtaining K white noise sequences with mean value of 0 and different standard deviations comprises: obtaining the standard deviation of all extreme points in the target sensor data, and taking the standard deviation value range of the K white noise sequences, and taking the initial value in the standard deviation value range, and taking the value every to obtain K white noise sequences with different standard deviations and with the mean value of 0; wherein is the standard deviation of all extreme points in the target sensor data, is a preset range parameter.

6. The adaptive sliding mode variable structure control method for robots according to claim 4, wherein, The specific method for obtaining the target sensor data corresponding to the IMF component data based on the numerical change trend of the number control function comprises: A two-dimensional rectangular coordinate system is constructed, the value of the number control function is taken as the vertical axis of the two-dimensional rectangular coordinate system, the order of the IMF component data is taken as the horizontal axis of the two-dimensional rectangular coordinate system, the number control function value of the IMF component data of each order of the target sensor data is mapped into the two-dimensional rectangular coordinate system to form a corresponding scatter plot, which is recorded as the number control function scatter plot of the target sensor data; the number control function scatter plot of the target sensor data is curve-fitted by the least square method, the elbow method is used to obtain the inflection point in the curve-fitting result of the number control function scatter plot, the order closest to the inflection point is taken as the order of the IMF component data of the target sensor data, which is recorded as the target order of the target sensor data, so that the IMF component data of the target sensor data is obtained by the EEMD signal decomposition algorithm, and the order of the IMF component data of the target sensor data is the corresponding target order.

7. The adaptive sliding mode variable structure control method for robots according to claim 6, wherein, The specific acquisition method of the feature group of the sensor data comprises: For each IMF component data of the target sensor data, the instantaneous frequency sequence and the amplitude envelope sequence of the IMF component data are calculated by using the Hilbert-Huang transform, the average frequency, the frequency bandwidth and the energy proportion of the IMF component data are calculated based on the instantaneous frequency sequence and the amplitude envelope sequence, and the array formed by the average frequency, the frequency bandwidth and the energy proportion of the IMF component data is taken as the feature group of the IMF component data, and the feature groups of all IMF component data of the target sensor data are obtained.

8. An adaptive sliding mode variable structure control system for a robot, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the steps of the adaptive sliding mode variable structure control method for the robot according to any one of claims 1-7.

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