CN119036457A - Method and device for determining angular acceleration of rotating joint of robot and electronic equipment - Google Patents
Method and device for determining angular acceleration of rotating joint of robot and electronic equipment Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1628—Programme controls characterised by the control loop
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Abstract
The application relates to a method and a device for determining angular acceleration of a rotating joint of a robot and electronic equipment. The method comprises the steps of obtaining motion information of a rotating joint of a robot from a sensor, wherein the motion information is generated by the robot under the action of a control moment and a driving moment, obtaining disturbance observation compensation quantity and error feedback of the robot in a motion process, and processing the disturbance observation compensation quantity, the error feedback and the control moment by utilizing a pre-established observer dynamic model to determine target angular acceleration. By adopting the method, the angular acceleration measurement accuracy can be improved.
Description
Technical Field
The present application relates to the field of robot control technologies, and in particular, to a method and an apparatus for determining angular acceleration of a rotating joint of a robot, and an electronic device.
Background
In the technical field of robots, precise control of angular acceleration of a rotating joint of the robot is a key to realizing complex actions. Accurate measurement of angular acceleration is important to links such as dynamic modeling, impedance shaping control, joint compliance control and the like. Direct measurement of angular acceleration is often difficult to achieve.
To solve this problem, conventional techniques typically acquire a differential variable from the measured velocity signal, and then derive an angular acceleration based on the differential variable.
However, since noise, external disturbance, and the like may exist in the velocity signal, there is a problem in that the angular acceleration determined by the conventional method is not accurate.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, an apparatus, and an electronic device for determining angular acceleration of a rotational joint of a robot, which can improve accuracy of angular acceleration measurement.
In a first aspect, the present application provides a method for determining angular acceleration of a rotational joint of a robot, including:
The method comprises the steps of acquiring motion information of a rotary joint of a robot from a sensor, wherein the motion information is generated by the robot under the action of control moment and driving moment;
obtaining disturbance observation compensation quantity and error feedback of a robot in a motion process;
And processing disturbance observation compensation quantity, error feedback and control moment by using a pre-established observer dynamic model, and determining the target angular acceleration.
In one embodiment, the processing the disturbance observer compensation amount, the error feedback and the control moment by using the pre-established observer dynamics model to determine the target angular acceleration includes:
inputting the control moment and disturbance observation compensation quantity into an observer dynamic model, and determining initial angular acceleration;
And adjusting the initial angular acceleration according to the error feedback, and determining the target angular acceleration.
In one embodiment, the method for determining the disturbance observer compensation amount includes:
and carrying out fitting processing on the motion information in a pre-established radial basis function neural network, and determining disturbance observation compensation quantity.
In one embodiment, the method further comprises:
and adjusting the radial basis neural network by using a weight matrix adaptive law algorithm so as to dynamically compensate the comprehensive disturbance by using the disturbance observation compensation quantity output by the radial basis neural network.
In one embodiment, the error feedback includes at least one of a linear compensation error feedback and a nonlinear compensation error feedback, and the determining of the error feedback includes:
acquiring a speed tracking error;
and/or determining linear compensation error feedback according to the speed tracking error and the linear feedback control law.
In one embodiment, the motion information includes velocity information, and the acquiring the velocity tracking error includes:
Inputting the speed information into an observer dynamics model, and determining a speed estimator;
based on the velocity estimate and the velocity information, a velocity tracking error is determined.
In one embodiment, the method further comprises:
Regulating the speed tracking error by using a Lyapunov function to obtain a regulated speed tracking error;
Correspondingly, determining nonlinear compensation error feedback according to the velocity tracking error and the fractional order sliding mode control law, and/or determining linear compensation error feedback according to the velocity tracking error and the linear feedback control law, including:
determining nonlinear compensation error feedback according to the regulated speed tracking error and the fractional order sliding mode control law; and/or determining linear compensation error feedback according to the adjusted speed tracking error and the linear feedback control law.
In a second aspect, the present application also provides a device for determining angular acceleration of a rotating joint of a robot, including:
The operation information acquisition module is used for acquiring the motion information of the rotary joint of the robot from the sensor, wherein the motion information is generated by the robot under the action of control moment and driving moment;
The error feedback acquisition module is used for acquiring disturbance observation compensation quantity and error feedback of the robot in the motion process;
and the angular acceleration determining module is used for processing disturbance observation compensation quantity, error feedback and control moment by utilizing a pre-established observer dynamic model to determine target angular acceleration.
The application further provides a system for determining the angular acceleration of the rotary joint of the robot, which comprises a robot module, an observation algorithm module, a communication connection between the robot module and the sensor module, and a communication connection between the robot power module and the sensor module;
The observation algorithm module is used for determining target angular acceleration at the rotary joint of the robot based on the control moment and the motion information;
the robot power assembly is used for generating motion under the action of control moment and driving moment;
And the sensor assembly is used for acquiring the motion information.
In a fourth aspect, the present application also provides an electronic device, including a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
The method comprises the steps of acquiring motion information of a rotary joint of a robot from a sensor, wherein the motion information is generated by the robot under the action of control moment and driving moment;
obtaining disturbance observation compensation quantity and error feedback of a robot in a motion process;
And processing disturbance observation compensation quantity, error feedback and control moment by using a pre-established observer dynamic model, and determining the target angular acceleration.
In a fifth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
The method comprises the steps of acquiring motion information of a rotary joint of a robot from a sensor, wherein the motion information is generated by the robot under the action of control moment and driving moment;
obtaining disturbance observation compensation quantity and error feedback of a robot in a motion process;
And processing disturbance observation compensation quantity, error feedback and control moment by using a pre-established observer dynamic model, and determining the target angular acceleration.
In a sixth aspect, the application also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of:
The method comprises the steps of acquiring motion information of a rotary joint of a robot from a sensor, wherein the motion information is generated by the robot under the action of control moment and driving moment;
obtaining disturbance observation compensation quantity and error feedback of a robot in a motion process;
And processing disturbance observation compensation quantity, error feedback and control moment by using a pre-established observer dynamic model, and determining the target angular acceleration.
The method, the device and the electronic equipment for determining the angular acceleration of the rotating joint of the robot acquire the motion information of the rotating joint of the robot from a sensor, the motion information is generated by the robot under the action of a control moment and a driving moment, disturbance observation compensation quantity and error feedback of the robot in the motion process are acquired, and finally the disturbance observation compensation quantity, the error feedback and the control moment are processed by utilizing a pre-established observer dynamic model to determine the target angular acceleration. In addition, by utilizing a pre-established observer dynamics model, disturbance observation compensation quantity, error feedback and control moment are processed, various influencing factors can be comprehensively considered, and the comprehensive processing improves the prediction capability of the model on the actual system dynamics, so that the accuracy of angular acceleration measurement is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the drawings that are needed in the description of the embodiments of the present application or the related technologies will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other related drawings may be obtained according to these drawings without inventive effort to those of ordinary skill in the art.
FIG. 1 is a diagram of an application environment of a method for determining angular acceleration of a rotational joint of a robot in one embodiment;
FIG. 2 is a flow chart of a method for determining angular acceleration of a rotational joint of a robot in one embodiment;
FIG. 3 is a flow chart of a method for determining angular acceleration of a rotational joint of a robot in another embodiment;
FIG. 4 is a flow chart of a method for determining angular acceleration of a rotational joint of a robot in another embodiment;
FIG. 5 is a flow chart of a method for determining angular acceleration of a rotational joint of a robot in another embodiment;
FIG. 6 is a block diagram of a robot rotational joint angular acceleration determining apparatus in one embodiment;
Fig. 7 is an internal structural diagram of an electronic device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
In a robot rotary joint system, the differential state quantity of angular acceleration is critical to dynamic modeling, impedance shaping control and joint compliance control, but cannot be directly measured through a sensor, and the differential state is easily affected by disturbance which is difficult to predict, so that difficulty is increased in state estimation. The existing differential reconstruction method can be divided into a model-free estimation method and a model-based observation method, the model-free estimation method does not depend on system dynamics, differential variables are directly obtained from measurement speed signals, the representative methods include a numerical difference method, a Lu Enba-grid Luenberger differential observer, a Levlet Levant arbitrary-order robust differential observer and the like, noise is usually assumed to be continuous and tiny, convergence performance is difficult to be guaranteed theoretically, the model method reconstructs differential signals on the basis of system dynamics, theoretical stability is guaranteed through state feedback control law design, the model method can be divided into a linear observer and a nonlinear observer according to a feedback form, the linear observer can only achieve infinite time convergence of reconstruction errors, and the nonlinear observer represented by a sliding mode observer can ensure that observation errors converge to zero in a limited time, and error convergence speed and disturbance suppression capability are improved.
The method has the main problems that (1) transient errors lack constraint, the existing differential observation research mostly focuses on error convergence speed and disturbance inhibition, but ignores the transient error constraint problem. The high-gain state feedback can accelerate the convergence speed of errors and inhibit the influence of disturbance, but larger overshoot and oscillation are generally introduced in the transient process, the uncertain transient estimation errors can cause the dislocation and even the inversion of exoskeleton power-assisted output, equipment damage and even the condition of hurting a wearer, and an angular acceleration observation method under the error constraint is needed urgently. (2) The finite time control method based on nonlinear feedback can ensure that the error converges to zero in finite time, but the convergence time depends on the initial state of the system, and the initial state is not accurately or can not be measured under normal conditions, so that an observer can not provide the determined transient convergence time, and can only determine the upper limit of the convergence time according to the state quantity amplitude, so that the conservation of an observation system is strong, and a fixed time convergence observation method irrelevant to the initial state is needed.
Based on this, the method for determining the angular acceleration of the rotating joint of the robot, provided by the embodiment of the application, can be applied to an application environment as shown in fig. 1. The system for determining the angular acceleration of the rotating joint of the robot consists of a robot module 101 and an observation algorithm module 102. The robot module 101 and the observation algorithm module 102 exchange data through a communication interface to cooperatively complete the determination of the target angular acceleration at the rotary joint of the robot.
The robot module 101 is composed of a robot power assembly and a sensor assembly. The robot power assembly is responsible for driving the rotary joint of the robot to move and executing tasks under the action of control moment and driving moment. The robot power assembly comprises actuating mechanisms such as a servo motor, a driver and the like, so that the movement of the robot accords with an expected control instruction. Meanwhile, the robot power assembly is also provided with a moment sensor, and moment acting on the joints is monitored in real time. The sensor assembly is responsible for acquiring motion information, including angular velocity and angular position. The sensor assembly is equipped with various sensors, such as encoders, gyroscopes, and accelerometers, to gather motion information with high accuracy. The motion information is transmitted to the observation algorithm module 102 for further processing.
The observation algorithm module 102 has built-in processor and observer dynamics models. The observation algorithm module 102 performs a corresponding calculation based on the motion information obtained from the sensor assembly and the control torque received from the power assembly.
All the components realize real-time data exchange through the communication interfaces, and the reliability and timeliness of information transmission are ensured. The communication interface may take a variety of forms, including serial communication or ethernet, etc.
In actual operation, the robot power assembly drives the joints to move according to the control instructions, and the sensor assembly collects motion information in real time and transmits the motion information to the observation algorithm module 102.
In an exemplary embodiment, as shown in fig. 2, a method for determining angular acceleration of a rotational joint of a robot is provided, and an example of application of the method to a processor in an observation algorithm module in fig. 1 is described, including the following steps 201 to 203. Wherein:
Step 201, motion information of a rotating joint of the robot is obtained from a sensor, wherein the motion information is generated by the robot under the action of control moment and driving moment.
The robot rotary joint refers to a connection part allowing rotary motion in the robot. The rotary joint can rotate around a certain axis, and is a component for realizing flexible movement and complex action of the robot.
The motion information refers to motion state data, including angular velocity, angular position, and the like, of the rotating joint of the robot, which is acquired from a sensor. The motion information is a representation of the state of the joint in actual motion.
The control moment is a moment applied to the rotating joint of the robot for driving the joint to move according to a predetermined trajectory or speed. The driving moment is similar to the control moment and is the driving force applied to the robot joint. It is the torque required to drive the articulation, including control torque and additional torque due to gravity, inertia, etc.
In the embodiment of the application, the sensor firstly collects the original data of the rotary joint of the robot. And then, carrying out data preprocessing on the collected original data. The preprocessing process includes filtering and calibration, for example, a low pass filter may be used to eliminate high frequency noise, or zero offset calibration may be performed to reduce sensor errors. Then, the data of the different sensors are combined by using a fusion algorithm, so that motion information is obtained. The sensor then sends the motion information to the observation algorithm module.
Step 202, obtaining disturbance observation compensation quantity and error feedback of a robot in a motion process.
The disturbance observation compensation amount refers to estimation and compensation of influence on external disturbance (such as friction, collision, environmental disturbance and the like). Error feedback is the difference between the actual motion information and the expected motion information. The desired movement information is determined by a predetermined control strategy or movement plan, while the actual movement information is obtained by sensor measurements.
In the embodiment of the application, the robot is affected by various external disturbances, such as friction, collision, environmental disturbance or uncertain load change, during the movement of the robot. To identify and estimate these disturbances, the observation algorithm module may compare the acquired motion information with the expected motion state in the model using a pre-established comparison model. If there are significant deviations, these deviations may be caused by external disturbances. By analyzing these deviations, the observation algorithm module can estimate the disturbance observer compensation. The observation algorithm module then calculates an error feedback by comparing the desired motion information with the actual motion information.
And 203, processing disturbance observation compensation quantity, error feedback and control moment by using a pre-established observer dynamics model to determine target angular acceleration.
The observer dynamics model is established based on the physical characteristics, the dynamics equation, the external load and other factors of the robot rotary joint. The observer dynamics model can determine the angular acceleration of the rotary joint under the action of different control moments and driving moments.
The target angular acceleration is the expected angular acceleration of the rotating joint of the robot, which is determined after the processing of the observation algorithm. The target angular acceleration is used for guiding the robot to execute corresponding motions so as to realize motion control at the rotary joint of the robot.
In the embodiment of the application, disturbance observation compensation quantity, error feedback and control moment are input into an observer dynamics model for comprehensive processing, so that target angular acceleration can be determined.
The method comprises the steps of firstly obtaining motion information of a rotating joint of a robot from a sensor, wherein the motion information is generated by the robot under the action of a control moment and a driving moment, then obtaining disturbance observation compensation quantity and error feedback of the robot in the motion process, and finally processing the disturbance observation compensation quantity, the error feedback and the control moment by utilizing a pre-established observer dynamic model to determine target angular acceleration. In addition, by utilizing a pre-established observer dynamics model, disturbance observation compensation quantity, error feedback and control moment are processed, various influencing factors can be comprehensively considered, and the comprehensive processing improves the prediction capability of the model on the actual system dynamics, so that the accuracy of angular acceleration measurement is improved.
In an exemplary embodiment, as shown in FIG. 3, the disturbance observer compensation amount, the error feedback and the control moment are processed by using a pre-established observer dynamics model to determine a target angular acceleration, including steps 301 to 302. Wherein:
Step 301, inputting the control moment and disturbance observation compensation quantity into an observer dynamics model, and determining initial angular acceleration.
In the embodiment of the application, the control moment and the disturbance observation compensation quantity are input into the observer dynamic model, the observer dynamic model compensates disturbance possibly occurring in the rotating shutdown process of the robot according to the disturbance observation compensation quantity, and then the initial angular acceleration which is supposed to be generated at the rotating joint under the current condition is determined according to the input control moment.
Step 302, adjusting the initial angular acceleration according to the error feedback, and determining the target angular acceleration.
In the embodiment of the application, after the initial angular acceleration is obtained, the observer dynamic model adjusts the initial angular acceleration by utilizing error feedback, so as to determine the target angular acceleration.
Illustratively, if the error feedback is large, this means that the actual motion state caused by the initial angular acceleration deviates far from the expected one. At this time, the observer dynamic model performs a decrease adjustment on the initial angular acceleration, and determines the initial angular acceleration after the decrease adjustment as the target acceleration. If the error feedback is small, the initial angular acceleration is insufficient to correct the existing motion deviation, the observer dynamic model increases the initial angular acceleration, and the initial angular acceleration after the increase adjustment is determined as the target acceleration.
In the above embodiment, the disturbance observation compensation amount is input into the observer dynamic model, so that the influence of the comprehensive disturbance can be effectively compensated, thereby being beneficial to improving the measurement accuracy of the angular acceleration. By using error feedback for adjusting the initial angular acceleration, errors in the initial estimate can be dynamically corrected.
In one exemplary embodiment, a method for determining a disturbance observer compensation amount includes:
and carrying out fitting processing on the motion information in a pre-established radial basis function neural network, and determining disturbance observation compensation quantity.
In the embodiment of the present application, first, the motion information at the rotary joint acquired from the sensor is input into the radial basis function network. The radial basis function neural network inputs the received motion information into the hidden layer through its input layer. The plurality of radial basis functions in the hidden layer may capture non-linear features in the motion information. Each radial basis function calculates its output value according to the distance between the motion information and the center point, thereby extracting the features in the motion information.
By feature extraction of the radial basis function network, the radial basis function network can fit potential modes in motion information and identify features related to external disturbance. In the fitting process, the radial basis function neural network maps the input motion information to an output layer, thereby generating disturbance observation compensation quantity.
In the above embodiment, by performing fitting processing on the motion information by using the radial basis function neural network, the disturbance influence in the motion process of the robot rotary joint can be effectively identified and estimated, and a corresponding disturbance observation compensation amount is generated.
In an exemplary embodiment, the method further comprises:
and adjusting the radial basis neural network by using a weight matrix adaptive law algorithm so as to dynamically compensate the comprehensive disturbance by using the disturbance observation compensation quantity output by the radial basis neural network.
In the embodiment of the application, first, a weight matrix of a radial basis function neural network is initialized. The weight matrix includes weight values for each connection in the radial basis function network, which are adjusted during the training process. Then, the initial learning rate and the parameters of the adaptive law algorithm are set so that the radial basis function neural network can be adjusted according to the actual situation.
The acquired motion information is input into a radial basis function neural network. The radial basis neural network is based on the weight matrix and the input motion information, and calculates and outputs disturbance observation compensation quantity through forward propagation. And comparing the error between the disturbance observation compensation quantity output by the radial basis function neural network and the actually measured disturbance quantity, wherein the error reflects the deviation between the disturbance estimation of the radial basis function neural network and the actual disturbance.
And the weight matrix self-adaptive law algorithm automatically adjusts the weight matrix in the radial basis neural network according to the error calculation result so as to reduce the error between the predicted disturbance observation compensation quantity and the actual disturbance.
The radial basis function neural network regulated by the weight matrix adaptive law algorithm outputs a new disturbance observation compensation quantity, and the disturbance observation compensation quantity can be dynamically regulated to compensate the disturbance influence of the comprehensive disturbance, so that the motion control of the rotating joint of the robot is optimized.
In the above embodiment, the weight matrix adaptive law algorithm can adjust the weight matrix of the radial basis function neural network in real time, so that the radial basis function neural network adapts to continuously changing comprehensive disturbance and environmental conditions, and the dynamic adjustment capability can provide disturbance observation compensation under different operation conditions, thereby enhancing adaptability and stability.
In an exemplary embodiment, as shown in fig. 4, the error feedback includes at least one of a linear compensation error feedback and a nonlinear compensation error feedback, and the determining process of the error feedback includes steps 401 to 402. Wherein:
step 401, acquiring a velocity tracking error.
The speed tracking error is used for measuring the deviation between the rotating joint of the robot and the target speed in actual motion.
In an embodiment of the application, first, the actual speed at the robot's rotational joint is monitored using sensors. Then, the actual speed is compared with the expected speed, and the difference between the actual speed and the expected speed is calculated, wherein the difference is the speed tracking error. Where the desired speed is the ideal speed that the robot joint should reach.
Step 402, determining nonlinear compensation error feedback according to the speed tracking error and the fractional order sliding mode control law, and/or determining linear compensation error feedback according to the speed tracking error and the linear feedback control law.
The fractional order sliding mode control law is a nonlinear control method, and the fractional order derivative and the sliding mode control theory are combined to design the control law. Slip-form control aims to improve robustness by designing the slip-form face.
Nonlinear compensation error feedback is a feedback signal generated based on a nonlinear control strategy for compensating for velocity tracking errors caused by nonlinear factors. The output is dynamically regulated by a nonlinear control law, so that the error is reduced.
A linear feedback control law is a control strategy based on a linear function of error that adjusts the control input in a linear fashion to compensate for the error.
The linear compensation error feedback is a feedback signal generated based on a linear feedback control law for adjusting the control system to reduce the velocity tracking error. It compensates for tracking errors due to linear errors by linear adjustment.
In the embodiment of the application, the nonlinear compensation error feedback is determined according to the speed tracking error and the fractional order sliding mode control law.
And determining the linear compensation error feedback according to the speed tracking error and a linear feedback control law.
In the above embodiment, the robustness of the observation algorithm module can be enhanced by using the nonlinear and linear compensation error feedback in combination. The fractional order sliding mode control has stronger robustness to uncertainty and external disturbance, the linear feedback control can provide stable performance, the combination can more comprehensively cope with various interferences of the observation algorithm module, and the instability of the observation algorithm module is reduced.
In an exemplary embodiment, as shown in fig. 5, the motion information includes velocity information, on the basis of which a velocity tracking error is obtained, including steps 501 to 502. Wherein:
Step 501, inputting speed information into an observer dynamics model, determining a speed estimator.
In the embodiment of the application, the speed information of the rotating joint of the robot, which is acquired by a sensor, is input into a pre-established observer dynamics model. The observer dynamics model determines a velocity estimate from the input velocity information.
Step 502, determining a velocity tracking error based on the velocity estimate and the velocity information.
In an embodiment of the application, the speed estimate generated by the observer dynamics model is compared with the actual speed information. By calculating the difference between the two, a velocity tracking error is determined.
In the above embodiment, the speed information is processed by using the observer dynamics model to obtain the speed estimation amount, and the speed tracking error is determined based on the speed estimation amount, so that the accuracy of angular acceleration calculation is improved, the influence of noise and external interference on calculation is reduced, the capturing capability of dynamic change is enhanced, and the error compensation effect is improved, so that more stable and accurate angular acceleration calculation is realized.
In an exemplary embodiment, the method further comprises:
And adjusting the speed tracking error by using the Lyapunov function to obtain the adjusted speed tracking error.
Correspondingly, determining nonlinear compensation error feedback according to the velocity tracking error and the fractional order sliding mode control law, and/or determining linear compensation error feedback according to the velocity tracking error and the linear feedback control law, including:
determining nonlinear compensation error feedback according to the regulated speed tracking error and the fractional order sliding mode control law; and/or determining linear compensation error feedback according to the adjusted speed tracking error and the linear feedback control law.
In the embodiment of the application, first, the difference between the actual speed and the expected speed is obtained by measuring the sensor, and the initial speed tracking error is calculated. Next, the initial velocity tracking error is input into the barrier lyapunov function for adjustment. The barrier lyapunov function is used to ensure that the speed tracking error varies within an acceptable range and to prevent the system from entering an unstable state. By this function, the error can be limited and adjusted, resulting in an adjusted velocity tracking error.
An appropriate control strategy is then selected to determine error feedback based on the adjusted velocity tracking error. First, a fractional order sliding mode control law may be utilized to determine nonlinear compensation error feedback. And inputting the regulated speed tracking error into a fractional order sliding mode control law, calculating a sliding mode surface, and generating a nonlinear compensation control signal. Such control signals may dynamically adjust the system to compensate for errors caused by non-linear factors.
In addition, a linear feedback control law may be employed to determine the linear compensation error feedback based on the adjusted velocity tracking error. The adjusted error is input into a linear feedback control law, and a linear compensation control signal is calculated through a linear function. The method directly utilizes a linear feedback gain adjustment system to reduce linear errors.
After determining the error feedback signal, a nonlinear compensation error feedback, a linear compensation error feedback, or a combination thereof may be selected according to the system requirements. Finally, the generated error feedback signal is applied to a control system to adjust the control input, reduce the speed tracking error and improve the control precision and stability of the system.
In the above embodiment, the speed tracking error is adjusted by using the barrier lyapunov function, so that the error overshoot can be effectively suppressed, the stability is enhanced, the error can be quickly converged, and the accuracy of the angular acceleration calculation is further improved.
According to some embodiments of the present application, a method for determining angular acceleration of a rotating joint of a robot is provided, and the method is applied to an observation algorithm module for example, and may include the following steps:
step 1, acquiring motion information of a rotating joint of the robot from a sensor, wherein the motion information is generated by the robot under the action of control moment and driving moment.
And 2, fitting the motion information in a pre-established radial basis function neural network to determine disturbance observation compensation quantity. And adjusting the radial basis neural network by using a weight matrix adaptive law algorithm so as to dynamically compensate the comprehensive disturbance by using the disturbance observation compensation quantity output by the radial basis neural network.
And step 3, inputting the speed information into an observer dynamics model, and determining a speed estimator.
And step 4, determining a speed tracking error based on the speed estimation amount and the speed information.
And step 5, adjusting the speed tracking error by using the Lyapunov function to obtain the adjusted speed tracking error.
And step 6, determining nonlinear compensation error feedback according to the regulated speed tracking error and the fractional order sliding mode control law, and/or determining linear compensation error feedback according to the regulated speed tracking error and the linear feedback control law.
And 7, inputting the control moment and disturbance observation compensation quantity into an observer dynamics model, and determining initial angular acceleration.
And 8, adjusting the initial angular acceleration according to the error feedback, and determining the target angular acceleration.
It should be understood that, although the steps in the flowcharts related to the above embodiments are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a robot rotary joint angular acceleration determining device for realizing the above-mentioned method for determining the angular acceleration of the robot rotary joint. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the device for determining the angular acceleration of the rotational joint of the robot provided below may be referred to the limitation of the method for determining the angular acceleration of the rotational joint of the robot in the above description, and will not be repeated here.
In an exemplary embodiment, as shown in fig. 6, there is provided a robot rotary joint angular acceleration determining apparatus including an operation information acquiring module 601, an error feedback acquiring module 602, and an angular acceleration determining module 603, wherein:
The operation information acquisition module 601 is used for acquiring motion information of a rotary joint of the robot from a sensor, wherein the motion information is generated by the robot under the action of a control moment and a driving moment;
the error feedback acquisition module 602 is configured to acquire disturbance observation compensation amount and error feedback of the robot in a motion process;
the angular acceleration determining module 603 is configured to process the disturbance observer compensation amount, the error feedback and the control moment by using a pre-established observer dynamics model, and determine the target angular acceleration.
In an exemplary embodiment, the angular acceleration determining module 603 is specifically configured to input the control moment and the disturbance observer compensation amount into an observer dynamics model, determine an initial angular acceleration, adjust the initial angular acceleration according to the error feedback, and determine a target angular acceleration.
In an exemplary embodiment, the error feedback obtaining module 602 is specifically configured to determine the disturbance observer compensation amount by performing a fitting process on the motion information in the pre-established radial basis function network.
In an exemplary embodiment, the above apparatus further includes:
And the adjusting module is used for adjusting the radial basis function neural network by utilizing a weight matrix adaptive law algorithm so as to dynamically compensate the comprehensive disturbance by using the disturbance observation compensation quantity output by the radial basis function neural network.
In an exemplary embodiment, the error feedback obtaining module 602 is specifically configured to obtain a velocity tracking error, determine a nonlinear compensation error feedback according to the velocity tracking error and a fractional order sliding mode control law, and/or determine a linear compensation error feedback according to the velocity tracking error and a linear feedback control law.
In an exemplary embodiment, the error feedback acquisition module 602 is specifically configured to input the velocity information into the observer dynamics model, determine a velocity estimator, and determine a velocity tracking error based on the velocity estimator and the velocity information.
In an exemplary embodiment, the error feedback obtaining module 602 is specifically configured to adjust the velocity tracking error by using the lyapunov function, so as to obtain an adjusted velocity tracking error.
The above-described respective modules in the robot rotary joint angular acceleration determination apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one exemplary embodiment, an electronic device is provided, which may be a controller, and an internal structure thereof may be as shown in fig. 7. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing data in the process of determining the angular acceleration of the rotary joint of the robot. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of determining angular acceleration of a robot rotary joint.
It will be appreciated by those skilled in the art that the structure shown in FIG. 7 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one exemplary embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
The method comprises the steps of acquiring motion information of a rotary joint of a robot from a sensor, wherein the motion information is generated by the robot under the action of control moment and driving moment;
obtaining disturbance observation compensation quantity and error feedback of a robot in a motion process;
And processing disturbance observation compensation quantity, error feedback and control moment by using a pre-established observer dynamic model, and determining the target angular acceleration.
In one embodiment, the processor when executing the computer program further performs the steps of:
inputting the control moment and disturbance observation compensation quantity into an observer dynamic model, and determining initial angular acceleration;
And adjusting the initial angular acceleration according to the error feedback, and determining the target angular acceleration.
In one embodiment, the processor when executing the computer program further performs the steps of:
and carrying out fitting processing on the motion information in a pre-established radial basis function neural network, and determining disturbance observation compensation quantity.
In one embodiment, the processor when executing the computer program further performs the steps of:
and adjusting the radial basis neural network by using a weight matrix adaptive law algorithm so as to dynamically compensate the comprehensive disturbance by using the disturbance observation compensation quantity output by the radial basis neural network.
In one embodiment, the error feedback comprises at least one of a linear compensation error feedback and a nonlinear compensation error feedback, the processor when executing the computer program further implementing the steps of:
acquiring a speed tracking error;
and/or determining linear compensation error feedback according to the speed tracking error and the linear feedback control law.
In one embodiment, the processor when executing the computer program further performs the steps of:
Inputting the speed information into an observer dynamics model, and determining a speed estimator;
based on the velocity estimate and the velocity information, a velocity tracking error is determined.
In one embodiment, the processor when executing the computer program further performs the steps of:
And adjusting the speed tracking error by using the Lyapunov function to obtain the adjusted speed tracking error.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
The method comprises the steps of acquiring motion information of a rotary joint of a robot from a sensor, wherein the motion information is generated by the robot under the action of control moment and driving moment;
obtaining disturbance observation compensation quantity and error feedback of a robot in a motion process;
And processing disturbance observation compensation quantity, error feedback and control moment by using a pre-established observer dynamic model, and determining the target angular acceleration.
In one embodiment, the computer program when executed by the processor further performs the steps of:
inputting the control moment and disturbance observation compensation quantity into an observer dynamic model, and determining initial angular acceleration;
And adjusting the initial angular acceleration according to the error feedback, and determining the target angular acceleration.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and carrying out fitting processing on the motion information in a pre-established radial basis function neural network, and determining disturbance observation compensation quantity.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and adjusting the radial basis neural network by using a weight matrix adaptive law algorithm so as to dynamically compensate the comprehensive disturbance by using the disturbance observation compensation quantity output by the radial basis neural network.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring a speed tracking error;
and/or determining linear compensation error feedback according to the speed tracking error and the linear feedback control law.
In one embodiment, the motion information comprises speed information, and the computer program when executed by the processor further performs the steps of:
Inputting the speed information into an observer dynamics model, and determining a speed estimator;
based on the velocity estimate and the velocity information, a velocity tracking error is determined.
In one embodiment, the computer program when executed by the processor further performs the steps of:
And adjusting the speed tracking error by using the Lyapunov function to obtain the adjusted speed tracking error.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of:
The method comprises the steps of acquiring motion information of a rotary joint of a robot from a sensor, wherein the motion information is generated by the robot under the action of control moment and driving moment;
obtaining disturbance observation compensation quantity and error feedback of a robot in a motion process;
And processing disturbance observation compensation quantity, error feedback and control moment by using a pre-established observer dynamic model, and determining the target angular acceleration.
In one embodiment, the computer program when executed by the processor further performs the steps of:
inputting the control moment and disturbance observation compensation quantity into an observer dynamic model, and determining initial angular acceleration;
And adjusting the initial angular acceleration according to the error feedback, and determining the target angular acceleration.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and carrying out fitting processing on the motion information in a pre-established radial basis function neural network, and determining disturbance observation compensation quantity.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and adjusting the radial basis neural network by using a weight matrix adaptive law algorithm so as to dynamically compensate the comprehensive disturbance by using the disturbance observation compensation quantity output by the radial basis neural network.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring a speed tracking error;
and/or determining linear compensation error feedback according to the speed tracking error and the linear feedback control law.
In one embodiment, the motion information comprises speed information, and the computer program when executed by the processor further performs the steps of:
Inputting the speed information into an observer dynamics model, and determining a speed estimator;
based on the velocity estimate and the velocity information, a velocity tracking error is determined.
In one embodiment, the computer program when executed by the processor further performs the steps of:
And adjusting the speed tracking error by using the Lyapunov function to obtain the adjusted speed tracking error.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile memory and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (RESISTIVE RANDOM ACCESS MEMORY, reRAM), magneto-resistive Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (PHASE CHANGE Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computation, an artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) processor, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the present application.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
Claims (12)
1. A method for determining angular acceleration of a rotational joint of a robot, the method comprising:
the method comprises the steps of acquiring motion information of a rotary joint of a robot from a sensor, wherein the motion information is generated by the robot under the action of control moment and driving moment;
obtaining disturbance observation compensation quantity and error feedback of the robot in the motion process;
and processing the disturbance observation compensation quantity, the error feedback and the control moment by using a pre-established observer dynamics model to determine the target angular acceleration.
2. The method of claim 1, wherein said processing said disturbance observer compensation quantity, said error feedback, and said control torque with a pre-established observer dynamics model to determine a target angular acceleration comprises:
inputting the control moment and the disturbance observation compensation quantity into the observer dynamic model, and determining initial angular acceleration;
and adjusting the initial angular acceleration according to the error feedback, and determining the target angular acceleration.
3. The method of claim 1, wherein the method of determining the disturbance observer compensation quantity comprises:
And fitting the motion information in a pre-established radial basis function neural network to determine the disturbance observation compensation quantity.
4. A method according to claim 3, characterized in that the method further comprises:
And adjusting the radial basis function neural network by using a weight matrix adaptive law algorithm so as to dynamically compensate the comprehensive disturbance by using the disturbance observation compensation quantity output by the radial basis function neural network.
5. The method of claim 1, wherein the error feedback comprises at least one of linear compensation error feedback and nonlinear compensation error feedback, the determining of the error feedback comprising:
acquiring a speed tracking error;
Determining the nonlinear compensation error feedback according to the speed tracking error and a fractional order sliding mode control law; and/or determining the linear compensation error feedback according to the speed tracking error and a linear feedback control law.
6. The method of claim 5, wherein the motion information comprises velocity information, and wherein the obtaining a velocity tracking error comprises:
Inputting the speed information into the observer dynamics model, determining a speed estimator;
the speed tracking error is determined based on the speed estimate and the speed information.
7. The method according to any one of claims 5 to 6, further comprising:
Adjusting the speed tracking error by using a Lyapunov function to obtain an adjusted speed tracking error;
Correspondingly, the nonlinear compensation error feedback is determined according to the speed tracking error and a fractional order sliding mode control law, and/or the linear compensation error feedback is determined according to the speed tracking error and a linear feedback control law, comprising:
determining the nonlinear compensation error feedback according to the adjusted speed tracking error and a fractional order sliding mode control law; and/or determining the linear compensation error feedback according to the regulated speed tracking error and a linear feedback control law.
8. A robot rotational joint angular acceleration determining apparatus, the apparatus comprising:
the operation information acquisition module is used for acquiring the motion information of the rotary joint of the robot from the sensor, wherein the motion information is generated by the robot under the action of control moment and driving moment;
the error feedback acquisition module is used for acquiring disturbance observation compensation quantity and error feedback of the robot in the motion process;
And the angular acceleration determining module is used for processing the disturbance observation compensation quantity, the error feedback and the control moment by utilizing a pre-established observer dynamics model to determine target angular acceleration.
9. The system is characterized by comprising a robot module and an observation algorithm module, wherein the robot module is in communication connection with the observation algorithm module, and comprises a robot power assembly and a sensor assembly, and the robot power assembly is in communication connection with the sensor assembly;
The observation algorithm module is used for determining target angular acceleration at the rotary joint of the robot based on the control moment and the motion information;
The robot power assembly is used for generating motion under the action of the control moment and the driving moment;
The sensor assembly is used for acquiring the motion information.
10. An electronic device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 7 when the computer program is executed.
11. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
12. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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