Robust adaptive dynamics parameter compensation based control method and system for rope-driven manipulator and robot

Abstract

The application provides a rope-driven manipulator control method and system based on robust adaptive dynamic parameter compensation and a robot. The method comprises: obtaining an operation task corresponding to the rope-driven manipulator; determining a desired joint torque of a joint of the rope-driven manipulator based on the operation task, a pre-established joint control outer loop module, and a robust adaptive PD control law, wherein the robust adaptive PD control law is used to compensate for uncertain system parameters in a dynamic model corresponding to the rope-driven manipulator, and the uncertain system parameters include a load mass of the rope-driven manipulator, a rope elastic coefficient, and a rope friction coefficient; determining a motor control instruction based on the desired joint torque and a pre-established motor control inner loop module; and controlling the rope-driven manipulator to operate to perform the operation task based on the motor control instruction. In this way, the control accuracy and robustness of the rope-driven manipulator under the conditions of uncertain system parameters and external disturbances can be improved.

Description

Technical Field

[0001] This invention relates to the field of robotic arm control technology, and more specifically, to a rope-driven robotic arm control method, system, and robot based on robust adaptive dynamic parameter compensation. Background Technology

[0002] With social development and changes in population structure, the demand for service robots such as nursing robots is increasing. Traditional industrial robotic arms, due to their strong structural rigidity, large mass and inertia, and insufficient safety in human-robot interaction, are difficult to apply directly to nursing scenarios that require high compliance and safety.

[0003] Rope-driven robotic arms, as a novel configuration, place heavy components such as drive motors on a base and remotely drive joint movements via lightweight, flexible ropes. This design helps reduce the mass and inertia of the robotic arm's moving parts, increases the load-to-weight ratio, and enhances the safety of human-robot interaction by utilizing the inherent flexibility of the ropes, thus showing great potential in fields such as nursing robots.

[0004] However, controlling a rope-driven robotic arm faces significant challenges. First, the dynamic model of a rope-driven robotic arm is highly nonlinear and exhibits strong coupling effects. Second, system parameters such as load mass, rope elasticity, and friction coefficient change constantly, especially in heavy-load operation scenarios. Changes in load can cause significant drifts in inertial parameters and gravity terms, leading to increased uncertainty in the dynamic model. Furthermore, in actual operation, there are also issues such as unmodeled dynamics and external environmental disturbances.

[0005] In existing control methods, the traditional PD / PID controller has a simple structure, but due to uncertain system parameters and external disturbances, the rope-driven robotic arm cannot stably and accurately perform corresponding operation tasks (such as high-precision tracking tasks). Summary of the Invention

[0006] In view of this, the purpose of this application is to provide a rope-driven robotic arm control method, system and robot based on robust adaptive dynamic parameter compensation, which can improve the control accuracy and robustness of the rope-driven robotic arm under conditions of uncertain system parameters and external disturbances.

[0007] To achieve the above technical objectives, the technical solution adopted in this application is as follows:

[0008] In a first aspect, embodiments of this application provide a control method for a rope-driven robotic arm based on robust adaptive dynamic parameter compensation, the method comprising:

[0009] Obtain the operation task corresponding to the rope-driven robotic arm;

[0010] Based on the operation task and the pre-established joint control outer loop module, the desired joint torque of the rope-driven manipulator joint is determined by the robust adaptive PD control law. The robust adaptive PD control law is used to compensate for the uncertain system parameters in the dynamic model corresponding to the rope-driven manipulator. The uncertain system parameters include the load mass of the rope-driven manipulator, the rope elasticity coefficient, and the rope friction coefficient.

[0011] Based on the pre-established inner loop module of motor control, the motor control command is determined through the desired joint torque;

[0012] Based on the motor control commands, the rope-driven robotic arm is controlled to operate in order to perform the operation task.

[0013] In conjunction with the first aspect, in some optional implementations, based on the operational task and a pre-established joint control outer loop module, the desired joint torque of the cable-driven robotic arm joint is determined through a robust adaptive PD control law, including:

[0014] Based on the operation task, determine the desired pose of the end effector of the rope-driven robotic arm;

[0015] Based on the desired pose, the desired joint angles of the joints in the cable-driven robotic arm are determined;

[0016] Based on the desired joint angle, the desired joint torque is determined by the joint control outer loop module according to the robust adaptive PD control law.

[0017] Based on a pre-established adaptive update law, the desired joint torque is optimized to obtain the optimized desired joint torque, which is then used as the desired joint torque of the cable-driven robotic arm joint.

[0018] In conjunction with the first aspect, in some optional implementations, based on the desired joint angle, the desired joint torque is determined by the joint control outer loop module according to a robust adaptive PD control law, including:

[0019] The desired joint torque is determined by the outer loop module of the joint control according to the formula corresponding to the robust adaptive PD control law, and is expressed as:

[0020]

[0021] In the formula, Indicates the desired joint torque; , , The inertia matrix, Coriolis force and centrifugal force matrix, and gravity term represent the known parts in the dynamic model; and For reference joint angular acceleration and reference joint angular velocity; Kd Let be a symmetric positive definite differential gain matrix; s is a sliding mode variable, expressed as... where e=qq d This represents the joint position tracking error, where q refers to the actual joint angle. d The expected joint angle; For joint velocity tracking error, It is a positive definite diagonal matrix; This is the regression matrix; For online estimates of the parameter vector of an uncertain system; K s is a symmetric positive definite robust gain matrix; sgn(s) is the sign function, used to provide robust control to suppress disturbances.

[0022] In conjunction with the first aspect, in some optional implementations, the desired joint torque is optimized based on a pre-established adaptive update law to obtain the optimized desired joint torque, which is then used as the desired joint torque of the cable-driven robotic arm joint, including:

[0023] Based on the formula corresponding to the adaptive update law, update , is represented as:

[0024]

[0025] In the formula, express The rate of change of update, It is a symmetric positive definite adaptive gain matrix. This is the transpose of the regression matrix;

[0026] Based on the updated The optimized desired joint torque is obtained and used as the desired joint torque of the cable-driven robotic arm joint.

[0027] In conjunction with the first aspect, in some alternative implementations, the regression matrix Y is related to the vector of the uncertain system parameters. The linear parameterization relationship that satisfies the dynamic model is expressed as:

[0028]

[0029] in, The uncertain part is inertia. The uncertainties are the Coriolis force and the centrifugal force. This represents the uncertain part of the gravity term. For the regression matrix, is the true vector of the uncertain system parameters.

[0030] In conjunction with the first aspect, in some optional implementations, based on a pre-established motor control inner loop module, motor control commands are determined through the desired joint torque, including:

[0031] According to the motor control inner loop module, the desired joint torque is converted into the desired motor rotation angle. , is represented as:

[0032]

[0033] In the formula, q j R is the current joint angle. j R is the radius of the joint rotation axis of the cable-driven robotic arm. m K is the radius of the motor rotation shaft of the rope-driven robotic arm. s,c The rope stiffness of the rope-driven robotic arm;

[0034] Based on the desired motor rotation angle, a motor control command is generated, expressed as follows:

[0035]

[0036] In the formula, u represents the motor control command, which is the parameter input to the motor driver in the rope-driven robotic arm; J m Let be the moment of inertia of the motor of the rope-driven robotic arm. e is the desired motor angular acceleration obtained based on the desired motor rotation angle. m and These are the tracking errors of the motor angle and angular velocity of the rope-driven robotic arm, respectively. , , This is the actual angle of the motor; for The derivative of represents the actual angular velocity of the motor; for The derivative of K represents the desired motor angular velocity; p,m and K d,m This refers to the feedback gain of the motor controller in the rope-driven robotic arm. The parameter corresponding to the rope preload length of the rope-driven robotic arm.

[0037] Secondly, embodiments of this application also provide a cable-driven robotic arm control system based on robust adaptive dynamic parameter compensation, the system comprising:

[0038] The acquisition module is used to acquire the operation tasks corresponding to the rope-driven robotic arm;

[0039] The first determining module is used to determine the desired joint torque of the rope-driven manipulator joint based on the operation task and the pre-established joint control outer loop module, through a robust adaptive PD control law. The robust adaptive PD control law is used to compensate for uncertain system parameters in the dynamic model corresponding to the rope-driven manipulator. The uncertain system parameters include the load mass of the rope-driven manipulator, the rope elasticity coefficient, and the rope friction coefficient.

[0040] The second determining module is used to determine the motor control command based on the pre-established motor control inner loop module and the desired joint torque.

[0041] The control module is used to control the operation of the rope-driven robotic arm to perform the operation task based on the motor control commands.

[0042] Thirdly, embodiments of this application also provide a robot, which includes a cable-driven robotic arm, a processor, and a memory. The memory stores a computer program, and when the computer program is executed by the processor, the robot performs the above-described method.

[0043] The invention employing the above technical solution has the following advantages:

[0044] In the technical solution provided in this application, a robust adaptive PD control law is used to compensate for uncertain system parameters in the dynamic model corresponding to the rope-driven manipulator. For parameterizable structural uncertainties such as "load mass, rope elasticity coefficient, and rope friction coefficient," the adaptive law compensates through multi-variable compensation rather than single-parameter compensation, reducing model dependence and minimizing the impact of "modeling errors." This allows the rope-driven manipulator to actively adapt to changes in uncertain system parameters and maintain high control accuracy. Simultaneously, a robust compensation term is innovatively introduced to work in conjunction with the adaptive law. This robust compensation term effectively suppresses residual errors from adaptive compensation, unmodeled dynamics caused by unparalleled high-frequency rope vibrations, and non-structural disturbances caused by external physical interaction torques, thus enhancing the robustness of the rope-driven manipulator's control system. Furthermore, combining PD control with the synergistic mechanism of adaptive and robust compensation helps achieve asymptotic convergence of tracking errors while ensuring control stability, thereby improving control accuracy. The joint control outer loop module and the motor control inner loop module work together, and the cascaded control architecture of the inner and outer loops decouples the dynamic compensation at the joint level from the precise drive at the motor level, facilitating control implementation and debugging. Attached Figure Description

[0045] This application can be further illustrated by the non-limiting embodiments given in the accompanying drawings. It should be understood that the following drawings only illustrate certain embodiments of this application and should not be considered as limiting the scope. For those skilled in the art, other related drawings can be obtained from these drawings without any inventive effort.

[0046] Figure 1 This is a flowchart illustrating the rope-driven robotic arm control method based on robust adaptive dynamic parameter compensation provided in an embodiment of this application.

[0047] Figure 2 This is a diagram showing the overall control framework of the rope-driven robotic arm provided in an embodiment of this application.

[0048] Figure 3 This is a control logic block diagram of the joint control outer loop module provided in an embodiment of this application. Detailed Implementation

[0049] The present application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that similar or identical parts are referred to by the same reference numerals in the drawings or description. Implementations not shown or described in the drawings are forms known to those skilled in the art. In the description of this application, terms such as "first" and "second" are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0050] Please refer to the reference. Figures 1 to 3 This application provides a control method for a tethered robotic arm based on robust adaptive dynamic parameter compensation (hereinafter referred to as "this method"). This method can be applied to tethered robotic arms or robots equipped with tethered robotic arms, and the steps of the method can be executed or implemented by the tethered robotic arm (or robot). The tethered robotic arm or robot equipped with a tethered robotic arm is a conventional structure or device, and is not specifically limited here. Please refer to... Figure 1 This method may include the following steps:

[0051] Step 110: Obtain the operation task corresponding to the rope-driven robotic arm;

[0052] Step 120: Based on the operation task and the pre-established joint control outer loop module, the desired joint torque of the cable-driven manipulator joint is determined by the robust adaptive PD (Proportional Derivative) control law. The robust adaptive PD control law is used to compensate for uncertain system parameters in the dynamic model corresponding to the cable-driven manipulator and to suppress unmodeled dynamics and external disturbances. The uncertain system parameters include the load mass of the cable-driven manipulator, the elastic coefficient of the cable, and the coefficient of friction of the cable.

[0053] Step 130: Based on the pre-established motor control inner loop module, determine the motor control command through the desired joint torque;

[0054] Step 140: Based on the motor control command, control the rope-driven robotic arm to operate in order to perform the operation task.

[0055] In the above implementation, based on the operation task corresponding to the rope-driven manipulator and the pre-established joint control outer loop module, the desired joint torque of the rope-driven manipulator joint is determined through a robust adaptive PD control law. During the calculation of the desired joint torque, the adaptive law compensates for uncertain system parameters in the dynamic model corresponding to the rope-driven manipulator. For uncertain system parameters such as "load mass, rope elasticity coefficient, and rope friction coefficient," the robust adaptive PD control law uses multi-variable compensation, rather than single-parameter compensation, to reduce model dependence and the impact of "modeling error," enabling the rope-driven manipulator to actively adapt to changes in uncertain system parameters and maintain high control accuracy. Simultaneously, the introduced robust term effectively suppresses unmodeled dynamics and external disturbances, further improving system stability. Furthermore, combining PD control with adaptive compensation and robust control helps to achieve asymptotic convergence of tracking error while ensuring control stability, thus improving control accuracy. The joint control outer loop module and the motor control inner loop module cooperate with each other; the cascaded control architecture of the inner and outer loops decouples the dynamic compensation at the joint level from the precise drive at the motor level, facilitating control implementation and debugging.

[0056] The steps of this method will be explained in detail below:

[0057] In this embodiment, the dynamic model of the cable-driven robotic arm can be represented as follows:

[0058] (1)

[0059] In the formula, This represents the current joint angle of the corresponding joint in the cable-driven robotic arm. and These are its first and second derivatives, respectively. The inertial matrix is ​​positive definite and symmetric. The matrix represents the Coriolis force and the centrifugal force. For gravity, This is the driving torque acting on the joint. This torque is generated by the tension difference between the two driving cables: Where T1 and T2 are the tensions of the two drive ropes, respectively, and R... j Let be the radius of the articulated wheel.

[0060] This dynamic model has an important characteristic: it is linear with respect to physical parameters such as link mass, inertia, center of mass position, and load mass. These uncertain or time-varying physical / system parameters are then combined to form a parameter vector. Therefore, the terms in the equation (Equation 1) of the dynamic model can be decomposed into known parts and... The uncertain part of linear correlation.

[0061] (2)

[0062] (3)

[0063] (4)

[0064] Based on the linearization property, the uncertain part can be represented as a known regression matrix Y and parameter vector. The product of these can be expressed as:

[0065] (5)

[0066] Where a and b are arbitrary vectors.

[0067] In step 110, the method for obtaining the operation task corresponding to the tethered robotic arm can be flexibly determined according to the actual situation. For example, the user can input the operation task into the tethered robotic arm, so that the robotic arm can obtain the operation task. The operation task can be a one-time task or a repetitive task. In addition, the type of operation task can be flexibly set according to the actual situation. For example, the operation task can be a task of grasping / lifting an object, or a task of controlling the end effector of the tethered robotic arm to track a corresponding target. The target to be tracked can be flexibly determined according to the actual situation, and is not specifically limited here.

[0068] The operation task may include the desired pose that the end effector of the tethered robotic arm is expected to reach. For example, if the operation task is a grasping task, the operation task may include the relative position of the grasped object and the base of the tethered robotic arm. The method for obtaining this position is conventional and will not be elaborated here.

[0069] The end effector of a rope-driven robotic arm can be a gripper for grasping objects, a suction cup for picking up objects, an electromagnet, or other conventional mechanisms.

[0070] In step 120, the uncertain system parameters may include, but are not limited to, the load mass of the rope-driven robotic arm, the elastic coefficient of the rope, and the coefficient of friction of the rope, which are uncertain or unknown dynamic parameters.

[0071] The goal of the joint control outer loop module is to enable the actual joint angle q to accurately track the desired joint angle q.d .

[0072] In step 120, based on the operation task and the pre-established joint control outer loop module, the desired joint torque of the cable-driven robotic arm joint is determined through a robust adaptive PD control law, which may include:

[0073] Step 121: Based on the operation task, determine the desired pose of the end effector of the rope-driven robotic arm;

[0074] Step 122: Based on the desired pose, determine the desired joint angles of the joints in the cable-driven robotic arm;

[0075] Step 123: Based on the desired joint angle, the desired joint torque is determined by the joint control outer loop module according to the robust adaptive PD control law;

[0076] Step 124: Based on the pre-established adaptive update law, optimize the desired joint torque to obtain the optimized desired joint torque, which is used as the desired joint torque of the cable-driven robotic arm joint.

[0077] In this embodiment, the desired pose can be understood as the position and orientation of the end effector relative to the tethered robotic arm when the end effector reaches the object to be grasped; the desired joint angle can be understood as the joint angle of the corresponding joint of the tethered robotic arm when the end effector is in the desired orientation. The methods of determining the desired position based on the task and the desired joint angle based on the desired position are conventional and will not be elaborated upon here.

[0078] Please refer to the reference. Figure 2 and Figure 3 , Figure 2 The study showcases an inner and outer dual-loop structure comprising a robust adaptive joint control loop and a motor control loop. Figure 2 The joint control loop in the middle is the joint control outer loop module. Figure 3 This demonstrates that the desired joint torque is composed of a PD control term, known dynamic compensation, adaptive compensation, and a newly added robust compensation term. In step 123, based on the desired joint angle, the desired joint torque is determined by the joint control outer loop module according to the robust adaptive PD control law, which may include:

[0079] The desired joint torque is determined by the outer loop module of the joint control according to the formula corresponding to the robust adaptive PD control law, and is expressed as:

[0080] (6)

[0081] In the formula, Indicates the desired joint torque; , , In the dynamic model, the inertia matrix, Coriolis force and centrifugal force matrix, and gravity term represent the known components; q refers to the actual joint angle; the reference velocity is defined. and reference acceleration , where q d For the desired joint angle; K d Let be a symmetric positive definite differential gain matrix; s is a sliding mode variable, expressed as... where e=qq d This refers to the joint position tracking error; For joint velocity tracking error, It is a positive definite diagonal matrix; This is the regression matrix; For online estimates of the parameter vector of an uncertain system; K s is a symmetric positive definite robust gain matrix; sgn(s) is a sign function used to provide robust control to suppress disturbances, which can be understood as external disturbances, such as task contact forces or accidental collisions.

[0082] The joint control outer loop module can be understood as a software functional module that performs the corresponding calculations based on formula (6). The robust adaptive PD control law is the above formula (6). Among them, the part containing M0, C0, and G0 is the model feedforward term, -K d s is the PD feedback control term. For adaptive compensation terms, and -K s sgn(s) represents the robust sliding mode term. The effectiveness of this control law stems from the synergistic effect of its two core components: adaptive compensation and robust compensation, which together address uncertainties and disturbances in the rope-driven robotic arm system. Firstly, the adaptive compensation component consists of the adaptive compensation term... The adaptive update law, namely the following formula (7), is used to handle the structured uncertainty in the rope-driven robotic arm system. The structured uncertainty refers to unknown dynamics that can be linearly parameterized. For example, structured uncertainty may include varying load mass and unknown rope friction coefficient. The adaptive update law estimates parameters online. This allows the dynamic model to approximate the real physical system in real time, thereby actively offsetting most of the model errors caused by parameter variations. However, adaptive compensation alone cannot effectively handle unstructured uncertainties and instantaneous errors in the adaptive process. Therefore, this application innovatively introduces a robust sliding mode term -K. s We use sgn(s) to solve this problem.

[0083] Unstructured uncertainty can include:

[0084] 1) Residual model errors not fully compensated by the adaptive update law;

[0085] 2) Unmodeled dynamics that cannot be linearly parameterized, which may include the high-frequency flexible vibration effect of the rope;

[0086] 3) External disturbances, such as physical interaction torques, which may include task contact forces or accidental collisions.

[0087] Ultimately, adaptive compensation and robust compensation work together: the adaptive mechanism handles the main, parameterizable uncertainties, which helps reduce the total amount of disturbances that require robust terms to suppress, thus improving the robustness gain K. s It can be set to a small value, which effectively suppresses residual disturbances while avoiding severe control signal chattering, thus achieving superior accuracy and stability compared to single adaptive or single robust control.

[0088] In step 124, the purpose of the adaptive update law is to make Able to approximate the true value .

[0089] In step 124, based on a pre-established adaptive update law, the desired joint torque is optimized to obtain the optimized desired joint torque, which is used as the desired joint torque of the cable-driven robotic arm joint, including:

[0090] Based on the formula corresponding to the adaptive update law, update , is represented as:

[0091] (7)

[0092] In the formula, express The rate of change of update, It is a symmetric positive definite adaptive gain matrix. This is the transpose of the regression matrix;

[0093] Based on the updated The optimized desired joint torque is obtained and used as the desired joint torque of the cable-driven robotic arm joint.

[0094] Understandably, the updated result Substitute into the above formula (6) to replace Then, torque calculations are performed to obtain the optimized desired joint torque. Lyapunov stability theory proves that the closed-loop system of the cable-driven robotic arm is stable under the control law (Equation 6) and the update law (Equation 7), and the tracking error e and It will asymptotically converge to zero.

[0095] In this embodiment, the vector of the regression matrix and the uncertain system parameters satisfies the linear parameterization relationship of the dynamic model, expressed as:

[0096] (8)

[0097] in, The uncertain part is inertia. The uncertainties are the Coriolis force and the centrifugal force. This represents the uncertain part of the gravity term. Let be the vector of the uncertain system parameters.

[0098] Please refer to Figure 2 , Figure 2 The rope control loop in the diagram is the inner loop module of the motor control. In step 130, based on the pre-established inner loop module of the motor control, the motor control command is determined through the desired joint torque, including:

[0099] According to the motor control inner loop module, the desired joint torque is converted into the desired motor rotation angle. , is represented as:

[0100] (9)

[0101] In the formula, q j R is the current joint angle. j R is the radius of the joint rotation axis of the cable-driven robotic arm. m K is the radius of the motor rotation shaft of the rope-driven robotic arm. s,c The rope stiffness of the rope-driven robotic arm;

[0102] Based on the desired motor rotation angle, a motor control command is generated, expressed as follows:

[0103] (10)

[0104] In the formula, u represents the motor control command, which is the parameter input to the motor driver in the rope-driven robotic arm; J m Let be the moment of inertia of the motor of the rope-driven robotic arm. e is the desired motor angular acceleration obtained based on the desired motor rotation angle. m and These are the tracking errors of the motor angle and angular velocity of the rope-driven robotic arm, respectively. , , This is the actual angle of the motor; for The derivative of represents the actual angular velocity of the motor; for The derivative of K represents the desired motor angular velocity; p,m and K d,m This refers to the feedback gain of the motor controller in the rope-driven robotic arm. The parameter corresponding to the rope preload length of the rope-driven robotic arm.

[0105] Understandably, the motor control inner loop module can be understood as a software functional module that performs the corresponding calculations based on the above formula (9). To maintain rope tension and generate... Expected motor rotation angle It can be calculated from the above formula (9). Through the analysis of... By taking the derivative, we can obtain the desired angular velocity of the motor. and angular acceleration In practical applications, sliding mode differentiators and other methods can be used to... Estimate its derivative in real time.

[0106] The calculation method of motor control commands can be used as a motor control law, which ensures that the actual rotation angle of the motor can quickly and accurately track the desired rotation angle.

[0107] In step 140, the motor driver of the rope-driven robotic arm receives the motor control command and controls the operation of the rope-driven robotic arm based on the motor control command, so that the rope-driven robotic arm can accurately perform the corresponding operation task (such as grasping an object at a specified position).

[0108] In summary, this method requires establishing a dynamic model of the cable-driven robotic arm: utilizing Lagrangian mechanics, and comprehensively considering rigid body dynamics, motor dynamics, and cable elasticity, a nonlinear dynamic equation (dynamic model) describing the relationship between joint motion and torque is established. A cascaded dual-loop control architecture is designed: this architecture decomposes the complex control task into two cooperating control loops, an outer loop module for joint control and an inner loop module for motor control. A robust adaptive compensation PD control law for the outer loop of joint control is designed: this control law integrates the stability of PD control, the accuracy of adaptive compensation, and the ability of robust terms to suppress disturbances. The key to this control law is utilizing the inherent parameter linearization characteristics of robot dynamics to decompose the dynamic model into known parts and uncertain parts linearly related to uncertain parameters. An adaptive update law is designed: uncertain dynamic parameters, including load changes, are estimated online in real time, and the estimated values ​​are used for compensation terms to generate a desired joint torque that can offset system nonlinearity, coupling effects, and parameter uncertainties. A control law for the inner loop of motor control is designed: the goal of this inner loop is to accurately execute the commands issued by the outer loop. It first solves for the desired motor rotation angle, angular velocity, and angular acceleration required to drive the desired joint torque based on the desired joint torque output from the outer loop. Then, a controller incorporating feedforward and feedback is designed to generate the final motor control signal, ensuring that the motor accurately tracks the desired motion, while maintaining the necessary pretension of the rope through coordinated control to prevent slack.

[0109] Based on the above design, this method enhances robustness to large-scale parameter variations in the control process of the rope-driven robotic arm: by directly estimating and compensating for structured uncertainties such as inertia and gravity related to the load through an online adaptive law, the controller of the rope-driven robotic arm can actively adapt to drastic load changes, maintain high control accuracy, and enhance the ability to suppress disturbances. Through the synergistic effect of the adaptive law and the robust term, this method can effectively cope with residual unmodeled dynamics and external disturbances, ensuring control performance under complex or non-ideal working conditions. Structured and modular design: The cascaded control architecture with inner and outer loops decouples the dynamic compensation at the joint level from the precise drive at the motor level, making the controller design clear and easy to implement and debug.

[0110] This application also provides a robust adaptive dynamic parameter compensation-based cable-driven robotic arm control system (hereinafter referred to as the control system). The control system includes at least one software functional module that can be stored in memory or embedded in the robot / drive robotic arm's operating system (OS) in the form of software or firmware. A processor is used to execute the executable modules stored in memory, such as the software functional modules and computer programs included in the control system.

[0111] In this embodiment, the control system includes various units, and the functions of each unit are as follows:

[0112] The acquisition module can be used to execute the corresponding operation steps of step 110, for example, to acquire the operation task corresponding to the rope-driven robotic arm;

[0113] The first determining module can be used to execute the corresponding operation steps of step 120. For example, it can be used to determine the expected joint torque of the rope-driven manipulator joint based on the operation task and the pre-established joint control outer loop module, through a robust adaptive PD control law. The robust adaptive PD control law is used to compensate for the uncertain system parameters in the dynamic model corresponding to the rope-driven manipulator. The uncertain system parameters include the load mass of the rope-driven manipulator, the rope elasticity coefficient, and the rope friction coefficient.

[0114] The second determining module can be used to execute the corresponding operation steps of step 130, for example, to determine the motor control command based on the desired joint torque using the pre-established motor control inner loop module.

[0115] The control module can be used to execute the corresponding operation steps of step 140, for example, to control the operation of the rope-driven robotic arm to perform the operation task based on the motor control command.

[0116] This application also provides a robot, which may include a cable-driven robotic arm, a processor, and a memory. The memory stores a computer program, and when the computer program is executed by the processor, the robot performs the above-described method.

[0117] In this embodiment, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor. For example, it can be a Central Processing Unit (CPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0118] The memory can be, but is not limited to, random access memory, read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, etc. In this embodiment, the memory can be used to store operation tasks, robust adaptive PD control laws, etc. Of course, the memory can also be used to store programs, which the processor executes after receiving execution instructions.

[0119] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the robot described above can be referred to the corresponding steps in the aforementioned method, and will not be elaborated further here.

[0120] Based on the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by hardware or by using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application can be embodied in the form of a software product. This software product can be stored in a non-volatile storage medium (such as CD-ROM, USB flash drive, mobile hard drive, etc.) and includes several instructions to cause a computer device (such as a personal computer, robot, or network device, etc.) to execute the methods described in the various implementation scenarios of this application.

[0121] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can also be implemented in other ways. The system and method embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, program segment, or part of code, which includes one or more executable instructions for implementing a specified logical function. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions. The functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0122] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A control method for a cable-driven robotic arm based on robust adaptive dynamic parameter compensation, characterized in that, The method includes: Obtain the operation task corresponding to the rope-driven robotic arm; Based on the aforementioned operational task and the pre-established joint control outer loop module, the desired joint torque of the cable-driven robotic arm joint is determined through a robust adaptive PD control law, including: Based on the operation task, determine the desired pose of the end effector of the rope-driven robotic arm; Based on the desired pose, the desired joint angles of the joints in the cable-driven robotic arm are determined; Based on the desired joint angle, the desired joint torque is determined by the joint control outer loop module according to the robust adaptive PD control law. Based on the pre-established adaptive update law, the desired joint torque is optimized to obtain the optimized desired joint torque, which is used as the desired joint torque of the rope-driven manipulator joint. The robust adaptive PD control law is used to compensate for the uncertain system parameters in the dynamic model corresponding to the rope-driven manipulator. The uncertain system parameters include the load mass of the rope-driven manipulator, the rope elasticity coefficient, and the rope friction coefficient. Based on the pre-established motor control inner loop module, the motor control command is determined through the desired joint torque, including: According to the motor control inner loop module, the desired joint torque is converted into the desired motor rotation angle. , represented as: In the formula, q j R is the current joint angle. j R is the radius of the joint rotation axis of the cable-driven robotic arm. m K is the radius of the motor rotation shaft of the rope-driven robotic arm. s,c The rope stiffness of the rope-driven robotic arm; Indicates the desired joint torque; Based on the desired motor rotation angle, a motor control command is generated, expressed as follows: In the formula, u represents the motor control command, which is the parameter input to the motor driver in the rope-driven robotic arm; J m Let be the moment of inertia of the motor of the rope-driven robotic arm. e is the desired motor angular acceleration obtained based on the desired motor rotation angle. m and These are the tracking errors of the motor angle and angular velocity of the rope-driven robotic arm, respectively. , , This is the actual angle of the motor; for The derivative of represents the actual angular velocity of the motor; for The derivative of K represents the desired motor angular velocity; p,m and K d,m This refers to the feedback gain of the motor controller in the rope-driven robotic arm. The parameter corresponding to the rope preload length of the rope-driven robotic arm; Based on the motor control commands, the rope-driven robotic arm is controlled to operate in order to perform the operation task.

2. The method according to claim 1, characterized in that, Based on the desired joint angle, the desired joint torque is determined by the joint control outer loop module according to the robust adaptive PD control law, including: The desired joint torque is determined by the outer loop module of the joint control according to the formula corresponding to the robust adaptive PD control law, and is expressed as: In the formula, Indicates the desired joint torque; , , The inertia matrix, Coriolis force and centrifugal force matrix, and gravity term represent the known parts in the dynamic model; and For reference joint angular acceleration and reference joint angular velocity; K d Let be a symmetric positive definite differential gain matrix; s is a sliding mode variable, expressed as... where e=qq d This represents the joint position tracking error, where q refers to the actual joint angle. d The expected joint angle; For joint velocity tracking error, It is a positive definite diagonal matrix; This is the regression matrix; K is an online estimate of the parameter vector of an uncertain system; s is a symmetric positive definite robust gain matrix; sgn(s) is the sign function, used to provide robust control to suppress disturbances.

3. The method according to claim 2, characterized in that, Based on a pre-established adaptive update law, the desired joint torque is optimized to obtain the optimized desired joint torque, which is used as the desired joint torque of the cable-driven robotic arm joint, including: Based on the formula corresponding to the adaptive update law, update... , represented as: In the formula, express The rate of change of update, It is a symmetric positive definite adaptive gain matrix. This is the transpose of the regression matrix; Based on the updated The optimized desired joint torque is obtained and used as the desired joint torque of the cable-driven robotic arm joint.

4. The method according to claim 2, characterized in that, The vector of the regression matrix Y and the uncertain system parameters The linear parameterization relationship that satisfies the dynamic model is expressed as: in, The uncertain part is inertia. The uncertainties are the Coriolis force and the centrifugal force. This represents the uncertain part of the gravity term. For the regression matrix, This is the true vector of the uncertain system parameters.

5. A cable-driven robotic arm control system based on robust adaptive dynamic parameter compensation, characterized in that, The system for performing the method as described in any one of claims 1 to 4 includes: The acquisition module is used to acquire the operation tasks corresponding to the rope-driven robotic arm; The first determining module is used to determine the desired joint torque of the cable-driven robotic arm joint based on the operation task and a pre-established joint control outer loop module, using a robust adaptive PD control law, including: Based on the operation task, determine the desired pose of the end effector of the rope-driven robotic arm; Based on the desired pose, the desired joint angles of the joints in the cable-driven robotic arm are determined; Based on the desired joint angle, the desired joint torque is determined by the joint control outer loop module according to the robust adaptive PD control law. Based on the pre-established adaptive update law, the desired joint torque is optimized to obtain the optimized desired joint torque, which is used as the desired joint torque of the rope-driven manipulator joint. The robust adaptive PD control law is used to compensate for the uncertain system parameters in the dynamic model corresponding to the rope-driven manipulator. The uncertain system parameters include the load mass of the rope-driven manipulator, the rope elasticity coefficient, and the rope friction coefficient. The second determining module is used to determine the motor control command based on the pre-established motor control inner loop module and the desired joint torque, including: According to the motor control inner loop module, the desired joint torque is converted into the desired motor rotation angle. , represented as: In the formula, q j R is the current joint angle. j R is the radius of the joint rotation axis of the cable-driven robotic arm. m K is the radius of the motor rotation shaft of the rope-driven robotic arm. s,c The rope stiffness of the rope-driven robotic arm; Indicates the desired joint torque; Based on the desired motor rotation angle, a motor control command is generated, expressed as follows: In the formula, u represents the motor control command, which is the parameter input to the motor driver in the rope-driven robotic arm; J m Let be the moment of inertia of the motor of the rope-driven robotic arm. e is the desired motor angular acceleration obtained based on the desired motor rotation angle. m and These are the tracking errors of the motor angle and angular velocity of the rope-driven robotic arm, respectively. , , This is the actual angle of the motor; for The derivative of represents the actual angular velocity of the motor; for The derivative of K represents the desired motor angular velocity; p,m and K d,m This refers to the feedback gain of the motor controller in the rope-driven robotic arm. The parameter corresponding to the rope preload length of the rope-driven robotic arm; The control module is used to control the operation of the rope-driven robotic arm to perform the operation task based on the motor control commands.

6. A robot, characterized in that, The robot includes a cable-driven robotic arm, a processor, and a memory. The memory stores a computer program that, when executed by the processor, causes the robot to perform the method as described in any one of claims 1 to 4.

Images