Delay compensation agile control method and device for rope cluster driven manipulator

Abstract

The present application provides a delay compensation agile control method and device for a rope cluster driven manipulator, which relates to the field of manipulator control technology. The method includes: determining the preliminary prediction value corresponding to the unknown motion state of the rope cluster driven manipulator according to the preset delay prediction mode and delay determination parameters; determining the effective observation value of the delay prediction offset based on the preliminary prediction value, the preset auxiliary dynamic equation and the preset disturbance observation equation; determining the expected time-varying trajectory of the joint of the rope cluster driven manipulator according to the control task requirements, and determining the trajectory tracking error and the sliding mode variable of the delay prediction offset according to the current position data of the joint in the rope cluster driven manipulator and the expected time-varying trajectory of the joint; determining the fixed time convergence equivalent control quantity of the delay compensation according to the preset control differential tracking data, the expected time-varying trajectory of the joint, the trajectory tracking error and the sliding mode variable, so as to perform delay compensation agile control according to the equivalent control quantity.

Description

Technical Field

[0001] The present application relates to the technical field of robot arm control, for example, to a delay-compensated agile control method and device for a rope-cluster driven robot arm. Background Art

[0002] With the advancement of science and technology and the development of social economy, the application scenarios of robotic arms are becoming more and more extensive. Traditional robotic arms have played an important role in industrial production, environmental detection, military research and other fields, but their limitations are also becoming increasingly prominent. Traditional robotic arms mostly use rigid structures, resulting in a lack of flexibility, and the working area often needs to be isolated. In addition, the motor reducer arranged at the joint increases the volume, weight and moment of inertia of the joint, resulting in bloated joints, increased self-load, and low load-bearing efficiency. These limitations restrict the further development of robotic arms in more fields such as aerospace and medical care, and rope cluster driven robotic arms have emerged. As a new type of robotic arm, the rope cluster driven robotic arm adopts a combination of lightweight ropes and passive joints. The driving force is transmitted through the rope, and the drive system is more compact and lightweight, which helps to reduce the volume and weight of the robotic arm, can move flexibly in complex environments, and complete more complex movements in a small space. The rope cluster driven robotic arm has better flexibility and flexibility, which is conducive to improving the adaptability and operation ability of the robotic arm in complex non-structural environments. In recent years, it has been widely used in serial / parallel robotic arms, exoskeleton robots and soft robot systems.

[0003] In the related art, most control methods of rope cluster driven manipulators are designed by system models. In the process of controlling rope cluster driven manipulators, it is necessary to design models required for each link and use a large amount of resources to perform complex calculation processes, resulting in low control efficiency. Summary of the invention

[0004] The present application aims to provide a delay-compensated agile control method and device for a rope-cluster driven robotic arm.

[0005] According to one aspect of the present application, a delay-compensated agile control method for a rope cluster-driven manipulator is proposed, comprising: determining a preliminary prediction value corresponding to an unknown motion state of the rope cluster-driven manipulator according to a preset delay prediction method and delay determination parameters; determining an effective observation value of a delay prediction offset based on the preliminary prediction value, a preset auxiliary dynamic equation and a preset disturbance observation equation; determining the expected time-varying trajectory of the joint motion of the rope cluster-driven manipulator according to control task requirements, and determining a trajectory tracking error and a sliding mode variable of the delay prediction offset according to current position data of the joints in the rope cluster-driven manipulator and the expected time-varying trajectory of the joint motion; determining a fixed-time convergence equivalent control quantity for delay compensation according to preset control differential tracking data, the expected time-varying trajectory of the joint motion, the trajectory tracking error and the sliding mode variable, so as to perform delay-compensated agile control according to the equivalent control quantity.

[0006] According to one aspect of the present application, a delay-compensated agile control device for a rope cluster-driven manipulator is proposed, comprising: a preliminary prediction module, for determining a preliminary prediction value corresponding to an unknown motion state of the rope cluster-driven manipulator according to a preset delay prediction method and delay determination parameters; an effective observation module, for determining an effective observation value of a delay prediction offset based on the preliminary prediction value, a preset auxiliary dynamic equation and a preset disturbance observation equation; an error variable determination module, for determining the expected time-varying trajectory of the joint motion of the rope cluster-driven manipulator according to control task requirements, and determining the trajectory tracking error and the sliding mode variables of the delay prediction offset according to the current position data of the joints in the rope cluster-driven manipulator and the expected time-varying trajectory of the joint motion; a fixed-time convergence equivalent control quantity determination module, for determining the fixed-time convergence equivalent control quantity of delay compensation according to preset control differential tracking data, the expected time-varying trajectory of the joint motion, the trajectory tracking error and the sliding mode variables, so as to perform delay-compensated agile control according to the equivalent control quantity.

[0007] According to one aspect of the present application, an electronic device is proposed, which includes: a processor; a memory storing a computer program, and when the computer program is executed by the processor, the processor executes the delay-compensated agile control method of the rope cluster-driven robotic arm as described above.

[0008] According to one aspect of the present application, a non-transitory computer-readable medium is provided, on which readable instructions are stored. When the instructions are executed by a processor, the processor executes the delay-compensated agile control method for a rope cluster-driven manipulator as described above.

[0009] It should be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the present application.

[0010] Beneficial effects:

[0011] Through the above-mentioned embodiments provided by the present application, there is no need to establish an accurate dynamic model. The connecting rod dynamic model and the motor dynamic model of the rope cluster drive manipulator system are summarized as lumped uncertainties, that is, unknown states of motion, and the control objectives of the system are achieved through the estimated values ​​of the unknown states of motion, that is, the predicted values, which simplifies the solution and calculation process of the dynamic model and improves the practicality and efficiency of the control strategy. By comprehensively considering the delay prediction offset and trajectory tracking error, the sliding mode variable is used to reflect the change of the system state. This design enables the control system to dynamically adjust the control strategy to adapt to the motion control requirements under different delay conditions. This flexibility enhances the system's adaptability to delay changes and ensures stability and performance under various delay conditions. With the help of a disturbance observer, that is, an observer established based on the disturbance observation equation for observation compensation, the design and implementation process of the control strategy is simplified, avoiding the difficulty of accurately modeling and calculating complex dynamic models in traditional control strategies, and reducing the complexity and calculation amount of the control algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without exceeding the scope of protection required by the present application.

[0013] Figure 1 A flow chart of a delay-compensated agile control method for a rope cluster-driven manipulator provided in an embodiment of the present application;

[0014] Figure 2 A fast trajectory tracking control block diagram of a rope cluster driven manipulator provided in an embodiment of the present application;

[0015] Figure 3 A schematic diagram of the time-varying desired trajectory of each joint angle of a rope cluster driven manipulator provided in an embodiment of the present application;

[0016] Figure 4 A schematic diagram of the convergence of sliding mode variables related to each joint provided in an embodiment of the present application;

[0017] Figure 5 A schematic diagram of specific error conditions of each joint tracking of a rope cluster driven robotic arm provided in an embodiment of the present application;

[0018] Figure 6 A schematic diagram of the offset compensation effect on delay estimation provided by an embodiment of the present application;

[0019] Figure 7 A schematic diagram of the change of the rope driving force provided in an embodiment of the present application;

[0020] Figure 8 A block diagram of a delay-compensated agile control device for a rope-cluster driven manipulator provided in an embodiment of the present application;

[0021] Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this application will be comprehensive and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted.

[0023] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0024] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0025] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0026] It should be understood that although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another component. Therefore, the first component discussed below can be referred to as the second component without departing from the teachings of the concepts of the present application. As used herein, the term "and / or" includes any one of the associated listed items and all combinations of one or more.

[0027] For specific implementation methods, please refer to the following embodiments.

[0028] Figure 1 This is a flow chart of a delay compensation agile control method for a rope cluster driven manipulator provided in an embodiment of the present application. The method of this embodiment can be applied to a control server. Figure 1 As shown, the method includes: step S10, step S11, step S12 and step S13.

[0029] In step S10, a preliminary prediction value corresponding to the unknown motion state of the rope cluster driven manipulator is determined according to a preset delay prediction method and delay determination parameters.

[0030] In the present application, the delay prediction method can be a pre-set method for making a preliminary estimate of the value of an unknown dynamic. The delay determination parameters can be parameters related to the delay prediction method, which can be directly or indirectly substituted into the delay prediction method, and can include parameters such as delay time. The rope cluster drive manipulator can also be called a rope drive manipulator. It is a new type of manipulator that uses rope transmission to arrange all drive units such as motors on the base. The unknown state of the movement of the rope cluster drive manipulator can be an uncertain state of movement other than the movement of the manipulator as required, which may include external interference.

[0031] According to the example embodiment, the pre-set and stored delay prediction method, delay determination parameters, and unknown motion state of the rope cluster driven robotic arm can be obtained, and then the delay determination parameters and the unknown motion state can be processed according to the delay prediction method to obtain a preliminary prediction value corresponding to the unknown motion state.

[0032] In step S11, based on the preliminary prediction value, the preset auxiliary dynamic equation and the preset disturbance observation equation, the effective observation value of the delay prediction offset is determined.

[0033] In the present application, the auxiliary dynamic equation may be a pre-set equation for assisting in determining the specific circumstances of the unknown motion state. The disturbance observation equation may be an equation for constructing a disturbance observer, which may characterize the association between the approximation offset estimate and disturbances other than task requirements. The approximation offset estimate is the value closest to the delay estimate offset, and the delay estimate offset may be obtained by subtracting the preliminary prediction value from the position dynamics.

[0034] The auxiliary dynamic equation and the perturbation observation equation may be associated equations. In some implementations, the relevant first parameter at the previous moment may be substituted into the auxiliary dynamic equation to calculate the second parameter required for the perturbation observation equation, and the second parameter may be used as the current first parameter required for solving the effective observation value at the current moment in the perturbation observation equation. Then, the current first parameter is substituted into the auxiliary dynamic equation to calculate the second parameter required for the perturbation observation equation at the next moment.

[0035] The valid observation value can be used to represent a more accurate observation value corresponding to the preliminary prediction value taking into account the delay prediction offset.

[0036] According to the example embodiment, the preliminary prediction value may be substituted according to the equation logic corresponding to the auxiliary dynamic equation and the disturbance observation equation to obtain the effective observation value.

[0037] In step S12, according to the control task requirements, the expected time-varying trajectory of the joints of the rope cluster driven manipulator is determined, and according to the current position data of the joints in the rope cluster driven manipulator and the expected time-varying trajectory of the joints, the sliding mode variables of the trajectory tracking error and the delay prediction offset are determined.

[0038] In this application, the control task requirement can be used to represent the requirements for the motion process of the rope cluster driven manipulator, including the starting position and ending position of the manipulator motion. The time-varying trajectory of the expected joint motion is the expected, time-varying motion path of the manipulator joint during the motion process. The current position data of the joint can be used to represent the actual position of the joint at the current moment. The trajectory tracking error can be used to represent the deviation between the actual position of the joint and the expected position.

[0039] The sliding mode variable is usually represented as s(x), which is a switching function in sliding mode control. It is designed according to the actual characteristics of the control system and is defined as a switching surface or sliding mode. In the state space, if there is a sliding manifold s(x)=0, the manifold divides the state space into two parts. Once the system moves near the manifold, it will be "attracted" and remain in this area. At this time, the area is called the sliding mode area, and the movement of the system in the sliding mode area is called sliding mode motion. Sliding mode variables are usually used to describe the deviation between the actual motion trajectory of the joint and the time-varying trajectory of the desired motion, and are used to guide control strategies to reduce this deviation.

[0040] According to the example embodiment, the simulation motion of the manipulator can be performed first according to the control task requirements to obtain the expected time-varying trajectory of the joint motion of the rope cluster driven manipulator. Then, the current position data of the joint in the rope cluster driven manipulator can be obtained through the sensor, and the trajectory tracking error can be obtained by subtracting the current position data from the expected time-varying trajectory of the joint motion. Then, the calculation method of the sliding mode variable can be obtained, and the trajectory tracking error can be substituted into the calculation method to obtain the sliding mode variable.

[0041] In step S13, a fixed-time convergence equivalent control quantity for delay compensation is determined according to preset control differential tracking data, time-varying trajectory of desired joint motion, trajectory tracking error and sliding mode variable, so as to perform delay compensation agile control according to the equivalent control quantity.

[0042] In the present application, the control differential tracking data may be pre-acquired and stored related parameters for the output state of the motor torque controller, which may include a function to be tracked, a tracking state output, a differential generating function, and other default functions. The fixed time convergence equivalent control quantity may be used to represent the value of the equivalent control input of the motor torque controller. By determining the value, the expected dynamic convergence properties of the sliding mode variable within a fixed time may be obtained, thereby ensuring that the joint trajectory error converges quickly to the zero neighborhood. Among them, the fixed time may be pre-set based on the control requirements.

[0043] According to an example embodiment, a calculation method for a fixed-time convergent equivalent control quantity can be obtained, and data processing is performed on the control differential tracking data, the time-varying trajectory of the desired joint motion, the trajectory tracking error and the sliding mode variable to obtain a fixed-time convergent equivalent control quantity for delay compensation, so as to perform delay compensated agile control according to the equivalent control quantity.

[0044] This application does not need to establish an accurate dynamic model. The connecting rod dynamic model and the motor dynamic model of the rope cluster driven manipulator system are summarized as lumped uncertainties, that is, unknown states of motion, and the control objectives of the system are achieved through the estimated values ​​of the unknown states of motion, that is, the predicted values, which simplifies the solution and calculation process of the dynamic model and improves the practicality and efficiency of the control strategy. By comprehensively considering the delay prediction offset and trajectory tracking error, the sliding mode variable is used to reflect the change of the system state. This design enables the control system to dynamically adjust the control strategy to adapt to the motion control requirements under different delay conditions. This flexibility enhances the system's adaptability to delay changes and ensures stability and performance under various delay conditions. With the help of a disturbance observer, that is, an observer established based on the disturbance observation equation for observation compensation, the design and implementation process of the control strategy is simplified, avoiding the difficulty of accurately modeling and calculating complex dynamic models in traditional control strategies, reducing the complexity and calculation amount of the control algorithm, and thus realizing agile control of delay compensation for rope cluster driven manipulators.

[0045] According to some embodiments, the above steps may also include a process for determining a time delay prediction method, which can be specifically implemented through the following steps: obtaining a pre-constructed dynamic equation of a rope cluster-driven robotic arm, and determining a dynamic mapping relationship between the motor driving torque and the robotic arm joint according to the dynamic equation; determining a time delay prediction method based on the dynamic mapping relationship between the motor driving torque and the robotic arm joint and an unknown motion state.

[0046] In this application, we first obtain the pre-built dynamic equations of the rope cluster driven manipulator:

[0047] ;

[0048] .

[0049] Combining the above equations, we can get The equation, that is, the dynamic mapping relationship:

[0050]

[0051] in, represents the torque provided by the drive motor, is the torque provided by the drive rope, and Represent the position vectors of the joints and motors respectively. is the inertia matrix of the drive motor, represents the corresponding damping matrix. is the mass inertia matrix, represents the Coriolis / centrifugal force matrix, and are the gravity and friction force vectors, The unknown uncertainties of the concentration include external disturbances.

[0052] The unknown states of motion can be divided into the unknown states of the link dynamics part and the unknown states of the motor dynamics part. We can first build a lumped uncertain dynamic equivalent model of the rope cluster driven manipulator system:

[0053]

[0054] in, is a positive definite diagonal matrix, Integrate the unknown dynamic part for the closed-loop system, that is, the unknown state of motion, which is expressed as:

[0055]

[0056] in, Corresponding to the connecting rod dynamics part, Corresponding to the motor dynamics part.

[0057] Based on this, the delay prediction method can be determined based on the mapping relationship and the unknown motion state, namely:

[0058]

[0059] Among them, t is the current time, L is the time delay constant, when L is small, Infinitely close to . It can represent the initial prediction value, is the time delay gain.

[0060] This application combines the dynamic mapping relationship between the motor drive torque and the mechanical arm joints, as well as the unknown motion state, to determine a more reasonable delay prediction method. This prediction method not only takes into account the current motion state of the mechanical arm, but also fully considers the influence of the dynamic characteristics, thereby enhancing the accuracy of the delay prediction.

[0061] According to some embodiments, the delay determination parameters include the delay time and the position vector of the joint. Step S10 can be specifically implemented by the following steps: substituting the delay time and the position vector of the joint into the delay prediction method to determine the preliminary prediction value corresponding to the unknown motion state at the current moment.

[0062] In this application, the delay time corresponds to t in the above step, and the position vector of the joint corresponds to q in the above step, based on: , a preliminary prediction value can be obtained.

[0063] This application can more accurately predict the unknown motion state of the robot arm at the current moment by substituting the two key parameters of delay time and joint position vector into the time delay prediction method. This prediction not only takes into account the time delay effect, but also combines the actual position information of the robot arm joints, thereby significantly improving the accuracy and reliability of the prediction.

[0064] According to some embodiments, step S11 can be specifically implemented through the following steps: based on the approximate offset prediction value, preliminary prediction value and auxiliary dynamic equation at the previous moment, determine the auxiliary variables and convergence adjustment variables at the current moment; based on the auxiliary variables and convergence adjustment variables, preliminary prediction value and disturbance observation equation at the current moment, determine the effective observation value at the current moment.

[0065] In this application, when L is small, Infinitely close to , although close, there is a certain delay prediction offset, that is, , a dynamic equivalent model considering the delay prediction offset can be established, namely:

[0066]

[0067] The model and are equivalent.

[0068] The auxiliary dynamic equation is:

[0069]

[0070] in, is the estimated value of the approximate delay prediction offset, that is, the approximate offset prediction value, is an auxiliary variable, To preset auxiliary parameters, is the convergence adjustment variable, is the symbolic function, and is a preset positive real number.

[0071] Substituting the approximate offset prediction value of the previous moment into the above auxiliary dynamic equation, we get and As an auxiliary variable and convergence adjustment variable at the current moment.

[0072] The perturbation observation equation is:

[0073]

[0074] in, and is a preset real number, For the adaptive parameters, the following adaptive law is adopted to update:

[0075]

[0076] The calculated auxiliary variables and convergence adjustment variables at the current moment can be substituted into the disturbance observation equation to obtain the effective observation value at the current moment, that is, .

[0077] By introducing auxiliary dynamic equations, the present application can consider the impact of dynamic changes in the system on the predicted value, thereby adjusting the preliminary predicted value to make it closer to the actual value. This adjustment helps to reduce prediction errors and improve the accuracy of predictions. The introduction of the disturbance observation equation enables this method to consider the impact of external disturbance factors on the system. By observing and considering these disturbances, the system can better adapt to environmental changes, enhance robustness, and ensure that the prediction results can maintain a certain accuracy under different conditions. By determining the auxiliary variables and convergence adjustment variables at the current moment, dynamic adjustment of the predicted value is achieved. This adjustment mechanism can adjust the predicted value in real time according to changes in the system state to make it more in line with the actual situation. By combining the preliminary predicted value, the auxiliary dynamic equation and the disturbance observation equation, this method optimizes the entire prediction process and obtains a reasonable closed-loop system.

[0078] According to some embodiments, step S12 can be specifically implemented through the following steps: determining the initial position, target position, robot arm motion posture and trajectory requirements from the control task requirements; simulating the movement process of the rope cluster driven robot arm from the initial position to the target position according to the robot arm motion posture and trajectory requirements to determine the expected time-varying trajectory of the joint motion; determining the trajectory tracking error and sliding mode variable according to the current position data of the joint and the expected time-varying trajectory of the joint motion.

[0079] In this application, the initial position can be used to indicate the position of the robot arm before the joint moves, the target position can be used to indicate the position that the joint moves to, and the robot arm motion posture can be used to indicate the trajectory style in which the robot arm moves, such as linear motion. In general, the trajectory requirement can be that the final joint desired motion time-varying trajectory is smooth and the first and second order derivatives exist.

[0080] According to the exemplary embodiment, according to the motion posture and trajectory requirements of the robot arm, the motion process of the rope cluster driving the robot arm from the initial position to the target position is simulated, and the time-varying trajectory of the joint desired motion can be obtained. The trajectory tracking error can be calculated by comparing the current position data of the joint with the time-varying trajectory of the joint's expected motion. and sliding mode variables .

[0081] This application uses simulation technology to simulate the movement process of the rope cluster driven manipulator from the initial position to the target position, which can fully consider the movement characteristics, dynamic characteristics and possible external environmental factors of the manipulator, so as to formulate a more reasonable and efficient joint expected movement time-varying trajectory. This helps to reduce energy consumption during the movement process and improve the movement performance of the manipulator. By comparing the current position data of the joint and the time-varying trajectory of the joint expected movement, the trajectory tracking error can be calculated in real time, thereby realizing accurate monitoring of the movement state of the manipulator.

[0082] According to some embodiments, when calculating the sliding mode variables and trajectory tracking errors, it can be specifically achieved through the following steps: based on the time-varying trajectory of the expected joint movement, determine the expected joint position data at multiple moments; subtract the expected joint position data from the corresponding current position data of the joint to determine the trajectory tracking error; determine the sliding mode variables based on the trajectory tracking error.

[0083] In this application, the expected joint position data at multiple moments can be determined from the time-varying trajectory of the expected joint movement. Then, the actual position data of the joint at different moments, that is, the current position data, can be subtracted to obtain the trajectory tracking error corresponding to each moment. , then the sliding mode variables can be calculated as follows :

[0084]

[0085]

[0086] in, is the preset sliding mode coefficient, and .

[0087] This application can accurately calculate the trajectory tracking error by comparing the joint expected position data and the corresponding joint current position data. This error value directly reflects the deviation between the actual movement of the robot arm joint and the expected trajectory, providing a quantitative indicator for evaluating the trajectory tracking performance of the robot arm.

[0088] According to some embodiments, step S13 can be specifically implemented through the following steps: determining the function to be tracked, the tracking state output and the differential generating function from the control differential tracking data; determining the state error corresponding to the tracking state output based on the function to be tracked and the tracking state output; generating an error dynamic equation corresponding to the state error based on the tracking state output, the state error and the differential generating function; determining a fixed time convergence equivalent control quantity based on the error dynamic equation, the time-varying trajectory of the expected joint motion, the trajectory tracking error and the sliding mode variable, so as to perform delay compensation agile control according to the equivalent control quantity.

[0089] In the present application, the control differential tracking data can be set based on the control differential tracker structure, wherein the control differential tracker structure is:

[0090] ;

[0091] .

[0092] in, is the function to be traced, , For different tracking status outputs, the function and Generated by:

[0093] ;

[0094] .

[0095] in, , , are greater than zero, is the differential generating function, and:

[0096]

[0097] The function is admissible, that is, there is a constant value The following conditions must be met:

[0098] Condition 1: ;

[0099] Condition 2: ;

[0100] Condition 3: .

[0101] is a real number domain, and the state error corresponding to the tracking state output can be ,in,

[0102] ;

[0103] .

[0104] Combining the above functions , and The calculation formula is to determine:

[0105]

[0106] Based on this the error dynamics equation is generated:

[0107] ;

[0108] .

[0109] Then, based on the formula of this application, the fixed time convergence equivalent control quantity can be obtained The method of determining is:

[0110] ;

[0111] .

[0112] Substitute the parameters in the above formula and calculate the equivalent control quantity.

[0113] In some implementations, the parameter group , to obtain the sliding mode variables The fixed time convergence effect of , the upper bound of the convergence time can be calculated by the following formula:

[0114]

[0115] This application can effectively reduce the trajectory tracking error by accurately calculating the fixed time convergence equivalent control quantity and performing delay compensation agile control, so that the actual motion trajectory of the joint is closer to the expected time-varying trajectory, thereby improving the motion accuracy and stability of the entire system. By constructing the error dynamic equation, the dynamic characteristics of the system can be more comprehensively reflected, and the appropriate equivalent control quantity can be determined accordingly. This control strategy based on dynamic equations is often more effective and accurate than static or simple feedback control.

[0116] According to other embodiments, reference may be made to Figure 2The fast trajectory tracking control block diagram of the rope cluster driven manipulator shown in the figure inputs the expected trajectory and the current joint motion information of the manipulator into the sliding mode error variable calculation module. According to the convergence requirements of the specific task, the equivalent control input is determined by the fixed time convergence equivalent input module. The delay estimation is determined by using the control input torque and the manipulator joint information at the previous moment. The offset compensation is determined based on the auxiliary offset observer. The equivalent control input, delay estimation, and offset compensation are superimposed to determine the final output of the controller. The control effect of the control method is illustrated by combining an example of a three-joint rope cluster driven manipulator.

[0117] The expected trajectory of the given rope cluster driven robot arm joint is as follows:

[0118]

[0119]

[0120]

[0121] Sliding mode coefficient is 3, the time delay constant L is 0.01s, and the time delay gain is 0.4,. Time control parameter group, for , the corresponding fixed convergence time can be calculated as , the observer related parameters are set as: is 5, is 3, is 25, is 5.

[0122] After simulation, we can get Figure 3 The schematic diagram of the rope cluster driven robot arm's joint angles tracking the time-varying desired trajectory is shown in FIG. Figure 4 Schematic diagram of the convergence of sliding mode variables related to each joint shown in Figure 5 The schematic diagram of the specific error of each joint tracking of the rope cluster driven robot arm is shown. Figure 6 The schematic diagram of the offset compensation effect on the delay estimation is shown as well as Figure 7 Schematic diagram of the change of rope driving force shown.

[0123] refer to Figure 3 , where 3-a corresponds to joint 1, 3-b corresponds to joint 2, and 3-c corresponds to joint 3. Under the control method of the present application, joints 1, 2, and 3 can all quickly and accurately track the time-varying desired trajectory, demonstrating the good tracking performance of the controller. Figure 4It can be determined that under the action of the controller, each sliding mode variable can converge to the zero neighborhood within a given time, and under the condition of friction mutation caused by joint trajectory reversal, the sliding mode variable can still be quickly pulled back to the zero neighborhood, which reflects the rapidity of the controller. Figure 5 During the control and tracking process, the errors of each joint can remain converged and the error is less than 0.05rad, which reflects the high-precision characteristics of the controller. Figure 6 , in addition to compensating for the offset in the steady state, the sudden offset during the joint trajectory change can still be observed and compensated. Figure 7 , which can reflect the boundedness and continuity. The realization of the above fast and high-precision control effect does not rely on the complex dynamic model of the computing system, which helps to reduce the computing burden of the controller.

[0124] The following describes an apparatus embodiment of the present application, which can be used to execute the method embodiment of the present application. For details not disclosed in the apparatus embodiment of the present application, reference can be made to the method embodiment of the present application.

[0125] Figure 8 This is a block diagram of a delay compensation agile control device for a rope cluster driven manipulator provided in an embodiment of the present application. Figure 8 As shown, the delay compensation agile control device 800 of the rope cluster driven manipulator includes a preliminary prediction module 801, an effective observation module 802, an error variable determination module 803 and a fixed time convergence equivalent control quantity determination module 804.

[0126] The preliminary prediction module 801 is used to determine the preliminary prediction value corresponding to the unknown motion state of the rope cluster driven manipulator according to the preset delay prediction method and delay determination parameters;

[0127] An effective observation module 802 is used to determine an effective observation value of the delay prediction offset based on the preliminary prediction value, the preset auxiliary dynamic equation and the preset disturbance observation equation;

[0128] The error variable determination module 803 is used to determine the expected time-varying trajectory of the joints of the rope cluster driven manipulator according to the control task requirements, and determine the sliding mode variables of the trajectory tracking error and the delay prediction offset according to the current position data of the joints in the rope cluster driven manipulator and the expected time-varying trajectory of the joints;

[0129] The fixed time convergence equivalent control quantity determination module 804 is used to determine the fixed time convergence equivalent control quantity of delay compensation according to the preset control differential tracking data, the time-varying trajectory of the desired joint motion, the trajectory tracking error and the sliding mode variable, so as to perform delay compensation agile control according to the equivalent control quantity.

[0130] Optionally, the delay compensation agile control device 800 for the rope cluster driven manipulator further includes a delay prediction mode determination module 805, which is used to:

[0131] Obtain the pre-built dynamic equation of the rope cluster driven manipulator, and determine the dynamic mapping relationship between the motor driving torque and the manipulator joint according to the dynamic equation;

[0132] Based on the dynamic mapping relationship between the motor driving torque and the robot arm joint and the unknown motion state, the delay prediction method is determined.

[0133] Optionally, the delay determination parameters include the delay time and the position vector of the joint; the preliminary prediction module 801 is specifically used for:

[0134] Substitute the delay time and the position vector of the joint into the time delay prediction method to determine the preliminary prediction value corresponding to the unknown state of the motion at the current moment.

[0135] Optionally, the effective observation module 802 is specifically used for:

[0136] Based on the approximate offset prediction value, the preliminary prediction value and the auxiliary dynamic equation at the previous moment, the auxiliary variables and the convergence adjustment variables at the current moment are determined;

[0137] According to the auxiliary variables and convergence adjustment variables, preliminary prediction values ​​and disturbance observation equations at the current moment, the effective observation values ​​at the current moment are determined.

[0138] Optionally, the error variable determination module 803 is specifically used for:

[0139] Determine the initial position, target position, robot motion posture and trajectory requirements from the control task requirements;

[0140] According to the motion posture and trajectory requirements of the robot arm, the motion process of the rope cluster driven robot arm from the initial position to the target position is simulated to determine the expected time-varying trajectory of the joint motion;

[0141] According to the current position data of the joint and the time-varying trajectory of the joint's expected motion, the trajectory tracking error and the sliding mode variable are determined.

[0142] Optionally, the error variable determination module 803 is specifically used to:

[0143] Determine the expected position data of the joints at multiple moments based on the expected time-varying trajectory of the joints;

[0144] Subtract the joint expected position data from the corresponding joint current position data to determine the trajectory tracking error;

[0145] The sliding mode variables are determined based on the trajectory tracking error.

[0146] Optionally, the fixed time convergence equivalent control amount determination module 804 is specifically used for:

[0147] determining a function to be tracked, a tracking state output and a derivative generating function from the control derivative tracking data;

[0148] Determine the state error corresponding to the tracking state output according to the function to be tracked and the tracking state output;

[0149] Generate an error dynamic equation corresponding to the state error according to the tracking state output, state error and differential generating function;

[0150] According to the error dynamic equation, the time-varying trajectory of the joint desired motion, the trajectory tracking error and the sliding mode variable, the fixed-time convergence equivalent control quantity is determined to perform delay-compensated agile control according to the equivalent control quantity.

[0151] The device performs functions similar to the method provided above. For other functions, please refer to the previous description and will not be repeated here.

[0152] Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application, such as Fig. 9 As shown, the electronic device 900 of this embodiment may include: a memory 901 and a processor 902 .

[0153] The memory 901 stores a computer program. When the computer program is executed by the processor 902, the processor 902 executes the method in the above embodiment.

[0154] The processor 902 and the memory 901 are connected, for example, via a bus.

[0155] Optionally, the electronic device 900 may further include a transceiver. It should be noted that in actual applications, the number of transceivers is not limited to one, and the structure of the electronic device 900 does not constitute a limitation on the embodiments of the present application.

[0156] The processor 902 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor 902 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0157] The bus may include a path to transmit information between the above components. The bus may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0158] The memory 901 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0159] The memory 901 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 902. The processor 902 is used to execute the application code stored in the memory 901 to implement the contents shown in the above method embodiment.

[0160] The electronic devices include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs, desktop computers, etc. It can also be a server, etc. Fig. 9 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0161] The electronic device of this embodiment can be used to execute the method of any of the above embodiments, and its implementation principle and technical effect are similar, which will not be described in detail here.

[0162] The present application also provides a non-transitory computer-readable storage medium having computer-readable instructions stored thereon. When the aforementioned instructions are executed by a processor, the processor executes the method in the above embodiment.

[0163] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a non-transient computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0164] The embodiments of the present application are described in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and its core idea of ​​the present application. At the same time, changes or deformations made by those skilled in the art based on the ideas of the present application, the specific implementation methods and the scope of application of the present application, all belong to the scope of protection of the present application. In summary, the content of this specification should not be construed as a limitation on the present application.

Claims

1. A delay-compensated agile control method for a rope-cluster driven manipulator, characterized in that: include: Determine a preliminary prediction value corresponding to the unknown motion state of the rope cluster driven manipulator according to a preset time delay prediction method and a time delay determination parameter; The preliminary prediction value is used to characterize the interference in the unknown motion state, and the unknown motion state is used to characterize the uncertain motion state other than the motion of the rope cluster driven manipulator as required; Based on the preliminary prediction value, the preset auxiliary dynamic equation and the preset disturbance observation equation, determining an effective observation value of the delay prediction offset; the effective observation value is used to characterize a more accurate observation value corresponding to the preliminary prediction value when the delay prediction offset is taken into account; Determine the expected time-varying trajectory of the joints of the rope cluster driven manipulator according to the control task requirements, and determine the trajectory tracking error and the sliding mode variables of the delay prediction offset according to the current position data of the joints in the rope cluster driven manipulator and the expected time-varying trajectory of the joints; Determine a fixed-time convergence equivalent control quantity for delay compensation according to preset control differential tracking data, the desired time-varying trajectory of the joint, the trajectory tracking error and the sliding mode variable, so as to perform delay compensation agile control according to the equivalent control quantity; The auxiliary dynamic equation is: ,in, To approximate the offset prediction value, , is an auxiliary variable, To preset auxiliary parameters, , is the convergence adjustment variable, is the symbolic function, and is a preset positive real number, The torque provided to the drive motor is is the position vector of the driving motor, is a positive definite diagonal matrix, is the preliminary predicted value; The disturbance observation equation is: ,in, and is a preset real number, is the adaptive parameter, is the disturbance observation variable, + Used to characterize the valid observation value.

2. The method according to claim 1, characterized in that Also includes: Obtaining a pre-constructed dynamic equation of the rope cluster driven mechanical arm, and determining a dynamic mapping relationship between the motor driving torque and the mechanical arm joint according to the dynamic equation; The delay prediction method is determined based on the dynamic mapping relationship between the motor driving torque and the robot arm joint and the unknown motion state.

3. The method according to claim 1, characterized in that The time delay determination parameters include the delay time and the position vector of the joint; the preliminary prediction value corresponding to the unknown motion state of the rope cluster driven manipulator is determined according to the preset time delay prediction method and the time delay determination parameters, including: The delay time and the position vector of the joint are substituted into the time delay prediction method to determine the preliminary prediction value corresponding to the unknown motion state at the current moment.

4. The method according to claim 1, characterized in that: The determining the effective observation value of the delay prediction offset based on the preliminary prediction value, the preset auxiliary dynamic equation and the preset disturbance observation equation includes: Determine the auxiliary variable and the convergence adjustment variable at the current moment based on the approximate deviation prediction value at the previous moment, the preliminary prediction value and the auxiliary dynamic equation; The effective observation value at the current moment is determined according to the auxiliary variables and convergence adjustment variables at the current moment, the preliminary prediction value and the disturbance observation equation.

5. The method according to claim 1, characterized in that The method of determining the expected time-varying trajectory of the joint motion of the rope cluster driven manipulator according to the control task requirements, and determining the trajectory tracking error and the sliding mode variable of the delay prediction offset according to the current position data of the joints in the rope cluster driven manipulator and the expected time-varying trajectory of the joint motion, comprises: Determine the initial position, target position, robot arm motion posture and trajectory requirements from the control task requirements; According to the motion posture of the robot arm and the trajectory requirement, a motion process of the rope cluster driving the robot arm from the initial position to the target position is simulated to determine the time-varying trajectory of the expected motion of the joint; The trajectory tracking error and the sliding mode variable are determined according to the current position data of the joint and the time-varying trajectory of the expected movement of the joint.

6. The method according to claim 5, characterized in that The determining the trajectory tracking error and the sliding mode variable according to the current position data of the joint and the time-varying trajectory of the expected movement of the joint comprises: Determining the joint expected position data at multiple moments based on the joint expected motion time-varying trajectory; Subtracting the joint expected position data from the corresponding current position data of the joint to determine the trajectory tracking error; The sliding mode variable is determined according to the trajectory tracking error.

7. The method according to any one of claims 1 to 6, characterized in that: The method of determining a fixed-time convergence equivalent control quantity for delay compensation according to preset control differential tracking data, the desired joint motion time-varying trajectory, the trajectory tracking error and the sliding mode variable, so as to perform delay compensation agile control according to the equivalent control quantity, comprises: Determining a function to be tracked, a tracking state output and a differential generating function from the control differential tracking data; Determining a state error corresponding to the tracking state output according to the function to be tracked and the tracking state output; Generate an error dynamic equation corresponding to the state error according to the tracking state output, the state error and the differential generating function; The fixed time convergence equivalent control quantity is determined according to the error dynamic equation, the time-varying trajectory of the desired joint motion, the trajectory tracking error and the sliding mode variable, so as to perform delay compensation agile control according to the equivalent control quantity.

8. A delay-compensated agile control device for a rope-cluster driven manipulator, characterized in that: include: A preliminary prediction module is used to determine a preliminary prediction value corresponding to an unknown motion state of the rope cluster driven manipulator according to a preset delay prediction method and a delay determination parameter; the preliminary prediction value is used to characterize interference in the unknown motion state, and the unknown motion state is used to characterize an uncertain motion state other than the motion performed by the rope cluster driven manipulator as required; An effective observation module, used to determine an effective observation value of the delay prediction offset based on the preliminary prediction value, a preset auxiliary dynamic equation and a preset disturbance observation equation; the effective observation value is used to characterize a more accurate observation value corresponding to the preliminary prediction value when the delay prediction offset is taken into account; An error variable determination module is used to determine the expected time-varying trajectory of the joints of the rope cluster driven manipulator according to the control task requirements, and determine the trajectory tracking error and the sliding mode variables of the delay prediction offset according to the current position data of the joints in the rope cluster driven manipulator and the expected time-varying trajectory of the joints; A fixed-time convergence equivalent control quantity determination module is used to determine a fixed-time convergence equivalent control quantity for delay compensation according to preset control differential tracking data, the desired joint motion time-varying trajectory, the trajectory tracking error and the sliding mode variable, so as to perform delay compensation agile control according to the equivalent control quantity; The auxiliary dynamic equation is: ,in, To approximate the offset prediction value, , is an auxiliary variable, To preset auxiliary parameters, , is the convergence adjustment variable, is the symbolic function, and is a preset positive real number, The torque provided to the drive motor is is the position vector of the driving motor, is a positive definite diagonal matrix, is the preliminary predicted value; The disturbance observation equation is: ,in, and is a preset real number, is the adaptive parameter, is the disturbance observation variable, + Used to characterize the valid observation value.

9. An electronic device, characterized in that: include: processor; A memory storing a computer program, which, when executed by the processor, enables the processor to execute the delay-compensated agile control method for a rope-cluster-driven manipulator as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that: Computer-readable instructions are stored thereon, and when the instructions are executed by a processor, the processor executes the delay-compensated agile control method for a rope-cluster-driven manipulator as described in any one of claims 1-7.

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