A variable admittance robot control method and related devices that guarantees passivity

Abstract

The disclosure provides a variable admittance robot control method and related equipment for ensuring passivity, and belongs to the technical field of control. Specifically, the method comprises the following steps: obtaining real-time state parameters and expected admittance parameters of a robot system; constructing an adaptive variable admittance control law comprising an error feedback term and a low-pass filter; calculating an adaptive gain term according to the real-time state parameters and the expected admittance parameters; establishing a constraint inequality group about the gain of the error feedback term, the parameters of the low-pass filter and the adaptive gain term based on the passivity condition of the robot system; solving an optimal error feedback gain in the feasible region of the constraint inequality group with the goal of maximizing the gain of the error feedback term; and substituting the adaptive gain term and the optimal error feedback gain into the adaptive variable admittance control law to calculate a final control torque and output the final control torque to a robot actuator. Through the scheme of the disclosure, time-varying admittance parameters can be adaptively adapted, stable human-machine interaction can be ensured, and high control precision can be achieved.

Description

Technical Field

[0001] This disclosure relates to the field of control technology, and in particular to a variable admittance robot control method and related equipment that ensures passivity. Background Technology

[0002] Currently, variable admittance or impedance control is widely used in physical human-machine / environment-robot interactions, and has significant application value in interactive robot systems such as collaborative robots, remotely operated robots, and rehabilitation robots. The core challenge facing variable admittance control is the competitive contradiction between passivity (interaction stability) and control accuracy. Most current mainstream methods do not address passivity, only performing some adaptive control on the admittance parameter. Under certain conditions, such as when the admittance parameter is very small or experiences abrupt changes, the controller cannot guarantee passivity, leading to system divergence.

[0003] It is evident that there is an urgent need for a variable admittance robot control method that guarantees passivity while maintaining high control precision and adaptability. Summary of the Invention

[0004] In view of this, the present disclosure provides a variable admittance robot control method and related equipment that ensures passivity, at least partially solving the problems of poor control accuracy and adaptability in the prior art.

[0005] In a first aspect, embodiments of this disclosure provide a variable admittance robot control method that ensures passivity, including: Step 1: Obtain the real-time state parameters and desired admittance parameters of the robot system. The real-time state parameters include at least the interaction torque, joint position and joint velocity, and the desired admittance parameters include the desired inertia, desired damping and desired stiffness of the system. Step 2: Construct an adaptive variable admittance control law that includes an error feedback term and a low-pass filter, wherein the error feedback term is generated based on sliding mode variables and smoothed by a low-pass filter; Step 3: Calculate the adaptive gain term based on the real-time state parameters and the desired admittance parameters. The adaptive gain term includes stiffness adaptive gain, damping adaptive gain and interactive torque adaptive gain. Step 4: Based on the passive nature of the robot system, establish a set of constraint inequalities for the gain of the error feedback term, the parameters of the low-pass filter, and the adaptive gain term. Step 5: With the goal of maximizing the gain of the error feedback term, solve for the optimal error feedback gain within the feasible region of the system of constraint inequalities; if a feasible solution exists, the optimal error feedback gain is adopted; if not, the gain of the error feedback term is set to zero. Step 6: Substitute the adaptive gain term and the optimal error feedback gain into the adaptive variable admittance control law, calculate the final control torque, and output it to the robot actuator.

[0006] According to a specific implementation of this disclosure, the expression of the adaptive variable admittance control law is as follows:

[0007] in, To control torque, For stiffness adaptive gain, For damping adaptive gain, For interactive torque adaptive gain, This refers to the joint position. For joint velocity, For interactive torque, For error feedback gain, For sliding mode variables, This is the transfer function of the low-pass filter.

[0008] According to a specific implementation of this disclosure, the update rate of the adaptive gain term is:

[0009] in, The coefficients are updated and all are greater than 0.

[0010] According to a specific implementation of this disclosure, the balance point of the update rate is calculated as follows:

[0011] in, , and These are the system inertia, damping, and stiffness, respectively. , and These are the inertia, damping, and stiffness of the desired admittance, respectively.

[0012] According to a specific implementation of an embodiment of this disclosure, a passive constraint clamp is applied to the calculated adaptive gain: .

[0013] According to a specific implementation of this disclosure, the expression for the system of constraint inequalities is:

[0014] in, .

[0015] According to a specific implementation of an embodiment of this disclosure, step 5 specifically includes: Construct optimization problem And satisfy the set of constraint inequalities and the preset upper limit of gain. Numerical optimization methods are used to solve the optimization problem. If a feasible solution exists, output the optimal value. If there is no feasible solution, then let .

[0016] According to a specific implementation of an embodiment of this disclosure, the sliding mode variable is defined as follows: Position error , The desired trajectory.

[0017] According to a specific implementation of an embodiment of this disclosure, the desired trajectory is obtained by solving the desired admittance model:

[0018] in, , and These are the expected inertia, expected damping, and expected stiffness, which vary with time, respectively.

[0019] According to one specific implementation of the present disclosure, the desired inertia, desired damping, and desired stiffness change stepwise or continuously with time.

[0020] According to a specific implementation of this disclosure, the transfer function of the low-pass filter is: .

[0021] According to a specific implementation of this disclosure, the robot system is a single-degree-of-freedom rigid manipulator, and its dynamic model is as follows:

[0022] in, This refers to joint acceleration.

[0023] Secondly, embodiments of this disclosure provide a variable admittance robot control system that ensures passivity, comprising: The parameter acquisition module is used to acquire the real-time state parameters and expected admittance parameters of the robot system. The real-time state parameters include at least interaction torque, joint position and joint velocity, and the expected admittance parameters include the expected inertia, expected damping and expected stiffness of the system. A control law construction module is used to construct an adaptive variable admittance control law that includes an error feedback term and a low-pass filter, wherein the error feedback term is generated based on a sliding mode variable and smoothed by a low-pass filter; An adaptive gain calculation module is used to calculate an adaptive gain term based on real-time state parameters and desired admittance parameters, wherein the adaptive gain term includes stiffness adaptive gain, damping adaptive gain and interactive torque adaptive gain. The constraint establishment module is used to establish a set of constraint inequalities for the gain of the error feedback term, the parameters of the low-pass filter, and the adaptive gain term based on the passive condition of the robot system. The optimization solution module is used to solve for the optimal error feedback gain within the feasible region of the system of constraint inequalities with the goal of maximizing the gain of the error feedback term. If a feasible solution exists, the optimal error feedback gain is adopted; otherwise, the gain of the error feedback term is set to zero. The control output module is used to substitute the adaptive gain term and the optimal error feedback gain into the adaptive variable admittance control law, calculate the final control torque, and output it to the robot actuator.

[0024] Thirdly, embodiments of this disclosure also provide an electronic device, the electronic device comprising: At least one processor; and, The memory is communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the passive variable admittance robot control method in the first aspect or any implementation thereof.

[0025] Fourthly, embodiments of this disclosure also provide a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the passive variable admittance robot control method in the first aspect or any implementation thereof.

[0026] Fifthly, embodiments of this disclosure also provide a computer program product, which includes a computing program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions that, when executed by a computer, cause the computer to perform the passive variable admittance robot control method in the first aspect or any implementation thereof.

[0027] The passive variable admittance robot control method in this embodiment includes: Step 1, acquiring real-time state parameters and desired admittance parameters of the robot system, wherein the real-time state parameters include at least interaction torque, joint position, and joint velocity, and the desired admittance parameters include the desired inertia, desired damping, and desired stiffness of the system; Step 2, constructing an adaptive variable admittance control law including an error feedback term and a low-pass filter, wherein the error feedback term is generated based on sliding mode variables and smoothed by a low-pass filter; Step 3, calculating an adaptive gain term based on the real-time state parameters and desired admittance parameters, wherein the adaptive gain term... The process includes: Step 4, establishing a set of constraint inequalities for the gain of the error feedback term, the parameters of the low-pass filter, and the adaptive gain term, based on the passivity condition of the robot system; Step 5, solving for the optimal error feedback gain within the feasible region of the constraint inequalities with the goal of maximizing the gain of the error feedback term; if a feasible solution exists, the optimal error feedback gain is adopted; if not, the gain of the error feedback term is set to zero; Step 6, substituting the adaptive gain term and the optimal error feedback gain into the adaptive variable admittance control law, calculating the final control torque, and outputting it to the robot actuator.

[0028] The beneficial effects of the embodiments of this disclosure are as follows: By constructing an error feedback type adaptive variable admittance control law with a low-pass filter through the scheme of this disclosure, and using the strict passivity condition of the system as a constraint, the optimal error feedback gain is solved online. This scheme can adaptively match the desired inertia, damping and stiffness parameters that change step or continuously with time in real time. Since the passivity constraint ensures that the robot system will not generate active energy injection during the interaction with the human body, it completely avoids unstable phenomena such as vibration divergence or impact, and realizes stable and safe physical human-machine interaction. At the same time, by maximizing the error feedback gain and introducing filtering and smoothing processing, the maximum position error can still be controlled within a small range when the interaction torque and admittance parameters change drastically, thereby achieving the technical effect of high control accuracy and good control smoothness at the same time. Attached Figure Description

[0029] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 A flowchart illustrating a variable admittance robot control method that ensures passivity, provided as an embodiment of this disclosure; Figure 2A schematic diagram of a variable admittance robot control system that ensures passivity, provided as an embodiment of this disclosure; Figure 3 A schematic diagram of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0031] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0032] The following specific examples illustrate the implementation of this disclosure. Those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. This disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0033] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0034] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this disclosure. The illustrations only show the components related to this disclosure and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0035] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0036] This disclosure provides a variable admittance robot control method that ensures passivity. The method can be applied to the variable admittance control process in interactive robot system scenarios such as human-robot collaborative robots, remotely operated robots, and rehabilitation robots.

[0037] See Figure 1 This is a flowchart illustrating a variable admittance robot control method that ensures passivity, provided by an embodiment of this disclosure. Figure 1 As shown, the method mainly includes the following steps: Step 1: Obtain the real-time state parameters and desired admittance parameters of the robot system. The real-time state parameters include at least the interaction torque, joint position and joint velocity, and the desired admittance parameters include the desired inertia, desired damping and desired stiffness of the system. In practical implementation, for scenarios involving the physical interaction between a single-degree-of-freedom robot and a human / environment, a dynamic model can be established:

[0038] in, , and These are the system inertia, damping, and stiffness, respectively. The position of the robot's joint interaction end; This refers to the output torque of the motor. The torque is the interaction torque between the robot and the human or environment.

[0039] Then, the variable admittance control problem is defined, and the desired variable admittance model is:

[0040] in, , and Let be the desired system inertia, damping, and stiffness, respectively, all of which are time-varying variables and satisfy . , , , , , ; For the desired robot joint position, Torque for human-computer interaction.

[0041] The control objective is to design a controller that minimizes the error between the robot's actual closed-loop admittance and the desired admittance, and minimizes the position error. It converges to zero while ensuring the system's passivity, thus achieving stable and secure human-computer interaction.

[0042] Step 2: Construct an adaptive variable admittance control law that includes an error feedback term and a low-pass filter, wherein the error feedback term is generated based on sliding mode variables and smoothed by a low-pass filter; In practical implementation, a simple adaptive variable admittance controller based on state feedback is as follows:

[0043] To improve convergence accuracy, a fixed gain error feedback term is added:

[0044] in, This represents the error feedback gain. Introducing error feedback can accelerate error convergence, but it may compromise the passivity of the system.

[0045] To smooth the control signal, a low-pass filter is added to the error feedback. ,get

[0046] in, The filtering parameter can smooth the control signal, but it will severely damage the passive nature of the system.

[0047] Step 3: Calculate the adaptive gain term based on the real-time state parameters and the desired admittance parameter. The adaptive gain term includes stiffness adaptive gain, damping adaptive gain, and interactive torque adaptive gain; their update law is: (1) in, For sliding mode variables, The coefficients are updated and all are greater than 0. Here are the filtering parameters for the sliding mode variables. Furthermore, to achieve perfect admittance matching, we have...

[0048] Step 4: Based on the passive nature of the robot system, establish a set of constraint inequalities for the gain of the error feedback term, the parameters of the low-pass filter, and the adaptive gain term. In practical implementation, an adaptive variable admittance control strategy that ensures passivity is proposed, with the following core requirements: 1. Constrained adaptive gain , and This makes them satisfy the passive condition; 2. Maximize the error feedback gain under the premise of passive constraint. It balances admittance matching accuracy and interaction stability.

[0049] The process of deriving the passivity constraint and solving for the optimal controller parameters is as follows: Derivation of passive constraints for each controller; For an adaptive variable admittance controller based on state feedback, under the condition of passivity, we have (2) For an adaptive variable admittance controller with error feedback, under the condition of passivity, we have

[0050] For an adaptive variable admittance controller with filtered error feedback, under the condition of passivity, we have (3) (4) Step 5: With the goal of maximizing the gain of the error feedback term, solve for the optimal error feedback gain within the feasible region of the system of constraint inequalities; if a feasible solution exists, the optimal error feedback gain is adopted; if not, the gain of the error feedback term is set to zero. In practice, the steps for solving the optimal controller parameters are as follows: Step 1: Calculate according to the adaptive update law (1) , and And constrained to satisfy (2) passivity condition; Step 2: Construct optimization goals

[0051] Step 3: Solve the optimization problem; if a solution exists, take the optimal value. If there is no solution, then let It degenerates into an adaptive variable admittance controller based on state feedback.

[0052] Step 6: Substitute the adaptive gain term and the optimal error feedback gain into the adaptive variable admittance control law, calculate the final control torque, and output it to the robot actuator.

[0053] In practice, the adaptive gain term and the optimal error feedback gain can be substituted into the adaptive variable admittance control law to calculate the final control torque and output it to the robot actuator, thereby realizing the physical interaction between the robot and the human / environment.

[0054] The passive variable admittance robot control method provided in this embodiment constructs an error feedback adaptive variable admittance control law with a low-pass filter and solves for the optimal error feedback gain online under the constraint of strict system passivity. This scheme can adaptively match the desired inertia, damping, and stiffness parameters that change stepwise or continuously with time in real time. Since the passivity constraint ensures that the robot system will not actively inject energy during human interaction, it completely avoids unstable phenomena such as vibration divergence or impact, and realizes stable and safe physical human-machine interaction. At the same time, by maximizing the error feedback gain and introducing filtering and smoothing processing, the maximum position error can still be controlled within a small range when the interaction torque and admittance parameters change drastically, thus achieving the technical effect of high control accuracy and good control smoothness at the same time.

[0055] The method of this disclosure will be further described below with reference to a specific embodiment: 1) A single-degree-of-freedom rigid robotic arm was selected as the experimental platform, and a six-dimensional force sensor was installed at the interactive end to collect the human-machine interaction torque in real time. The motor is equipped with an encoder to collect joint position data. With angular velocity The sampling frequency of the control system is set to 1000Hz.

[0056] 2) System modeling and parameter identification The system model is as follows:

[0057] Under no load, a sweep frequency signal with an amplitude of 10 Nm and a frequency of 0-2 Hz is applied to the robot joint. The joint angles and velocities were collected, and the results were identified using the least squares method. Inertia is Damping is stiffness is Here we assume the stiffness is zero; in actual implementation, the stiffness can be any positive number.

[0058] 3) System adjustment parameters Sliding mode parameters Filtering time constant upper limit of error feedback gain Adaptive coefficient update , , , , and .

[0059] 4) Setting the desired admittance parameter Expected inertia Damping Stiffness It exhibits both step-change and continuous sinusoidal variation characteristics, with the switching time of the step-change being... , , , The details are as follows: 0~20s: , , ; 20~50s: , , ; 50~80s: , , ; 80~100s: , , ; 5) Adaptive gain calculation and passive constraint Calculate sliding mode variables: Position error , Solved using the expected admittance model:

[0060] Calculate adaptive gain according to the update law:

[0061] Where the ideal gain , , .

[0062] Apply passive constraints: , and If the constraint is exceeded, the gain will be clamped to the boundary value.

[0063] 6) Solving for the optimal error feedback gain Define the optimization goal: The constraint condition is the passive condition of the filter-type controller:

[0064] Solving optimization problems: If a feasible solution exists, then the optimal value is taken. If there is no solution, then let Degeneracy is the basis of adaptive controller.

[0065] 7) Control output and closed-loop operation Substituting the optimal parameters, the output is a filter-type adaptive control law:

[0066] Obtain the motor's operating torque This enables stable physical interaction between the robot and the human body, updating parameters in real time until the operation ends (100s).

[0067] 8) Experimental Results and Verification 8.1) Passive Verification During the process, the system is strictly passive, and there is no divergent vibration or impact during human-computer interaction, meeting the requirements for safe interaction.

[0068] 8.2) Control accuracy verification Maximum position error Sum of squared errors ; When the admittance is stepped, the error convergence time is less than High admittance matching accuracy.

[0069] 8.3) Control smoothness verification Maximum change in control signal The signal is smooth without abrupt changes, and the motor runs without vibration.

[0070] 8.4) Comparison Conclusion The method proposed in this embodiment simultaneously meets the three requirements of passivity, high precision, and smooth control, and is superior to pure adaptive controllers, fixed gain error feedback controllers, and unconstrained filter controllers.

[0071] For a corresponding method embodiment, see [link to relevant documentation]. Figure 2 This disclosure also provides a variable admittance robot control system 20 that ensures passivity, comprising: The parameter acquisition module 201 is used to acquire the real-time state parameters and expected admittance parameters of the robot system. The real-time state parameters include at least interaction torque, joint position and joint velocity, and the expected admittance parameters include the expected inertia, expected damping and expected stiffness of the system. The control law construction module 202 is used to construct an adaptive variable admittance control law that includes an error feedback term and a low-pass filter, wherein the error feedback term is generated based on a sliding mode variable and is smoothed by a low-pass filter; The adaptive gain calculation module 203 is used to calculate the adaptive gain term based on the real-time state parameters and the desired admittance parameters, wherein the adaptive gain term includes stiffness adaptive gain, damping adaptive gain and interactive torque adaptive gain. The constraint establishment module 204 is used to establish a set of constraint inequalities for the gain of the error feedback term, the parameters of the low-pass filter, and the adaptive gain term based on the passive condition of the robot system. The optimization solution module 205 is used to solve for the optimal error feedback gain within the feasible region of the system of constraint inequalities with the goal of maximizing the gain of the error feedback term; if a feasible solution exists, the optimal error feedback gain is adopted; if not, the gain of the error feedback term is set to zero. The control output module 206 is used to substitute the adaptive gain term and the optimal error feedback gain into the adaptive variable admittance control law, calculate the final control torque, and output it to the robot actuator.

[0072] Figure 2 The system shown can execute the contents of the above method embodiments. For the parts not described in detail in this embodiment, please refer to the contents recorded in the above method embodiments, and they will not be repeated here.

[0073] See Figure 3 This disclosure also provides an electronic device 30, which includes at least one processor and a memory communicatively connected to the at least one processor. The memory stores instructions executable by the at least one processor, which, when executed, enable the at least one processor to perform the passive variable admittance robot control method described in the foregoing method embodiments.

[0074] This disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the passive variable admittance robot control method described in the foregoing method embodiments.

[0075] This disclosure also provides a computer program product, which includes a computing program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions that, when executed by a computer, cause the computer to perform the passive variable admittance robot control method described in the foregoing method embodiments.

[0076] The following is for reference. Figure 3 The diagram illustrates a structural schematic of an electronic device 30 suitable for implementing embodiments of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0077] like Figure 3As shown, the electronic device 30 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device 30. The processing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0078] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 30 to communicate wirelessly or wiredly with other devices to exchange data. Although electronic device 30 with various devices is shown in the figure, it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0079] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 309, or installed from a storage device 308, or installed from a ROM 302. When the computer program is executed by the processing device 301, it performs the functions defined in the methods of embodiments of this disclosure.

[0080] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0081] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0082] The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, enable the electronic device to perform the relevant steps of the above-described method embodiments.

[0083] Alternatively, the aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, enable the electronic device to perform the relevant steps of the above method embodiments.

[0084] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0085] 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 disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0086] The units described in the embodiments of this disclosure can be implemented in software or in hardware.

[0087] It should be understood that the various parts of this disclosure can be implemented in hardware, software, firmware, or a combination thereof.

[0088] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A variable admittance robot control method that ensures passivity, characterized in that, include: Step 1: Obtain the real-time state parameters and desired admittance parameters of the robot system. The real-time state parameters include at least the interaction torque, joint position and joint velocity, and the desired admittance parameters include the desired inertia, desired damping and desired stiffness of the system. Step 2: Construct an adaptive variable admittance control law that includes an error feedback term and a low-pass filter, wherein the error feedback term is generated based on sliding mode variables and smoothed by a low-pass filter; Step 3: Calculate the adaptive gain term based on the real-time state parameters and the desired admittance parameters. The adaptive gain term includes stiffness adaptive gain, damping adaptive gain and interactive torque adaptive gain. Step 4: Based on the passive nature of the robot system, establish a set of constraint inequalities for the gain of the error feedback term, the parameters of the low-pass filter, and the adaptive gain term. Step 5: With the goal of maximizing the gain of the error feedback term, solve for the optimal error feedback gain within the feasible region of the system of constraint inequalities; if a feasible solution exists, the optimal error feedback gain is adopted; if not, the gain of the error feedback term is set to zero. Step 6: Substitute the adaptive gain term and the optimal error feedback gain into the adaptive variable admittance control law, calculate the final control torque, and output it to the robot actuator.

2. The method according to claim 1, characterized in that, The expression for the adaptive variable admittance control law is: in, To control torque, For stiffness adaptive gain, For damping adaptive gain, For interactive torque adaptive gain, This refers to the joint position. For joint velocity, For interactive torque, For error feedback gain, For sliding mode variables, It is a low-pass filter. These are the filter parameters.

3. The method according to claim 2, characterized in that, The update rate of the adaptive gain term is: in, The coefficients are updated and all are greater than 0.

4. The method according to claim 3, characterized in that, The equilibrium point of the update rate is calculated as follows: in, , and These are the system inertia, damping, and stiffness, respectively. , and These are the inertia, damping, and stiffness of the desired admittance, respectively.

5. The method according to claim 4, characterized in that, Apply passive constraint clamping to the calculated adaptive gain: 。 6. The method according to claim 5, characterized in that, The expression for the system of constraint inequalities is: in, 。 7. The method according to claim 6, characterized in that, Step 5 specifically includes: Construct optimization problem And satisfy the set of constraint inequalities and the preset upper limit of gain. Numerical optimization methods are used to solve the optimization problem. If a feasible solution exists, output the optimal value. If there is no feasible solution, then let .

8. The method according to claim 7, characterized in that, The sliding mode variable is defined as follows: Position error , The desired trajectory.

9. The method according to claim 8, characterized in that, The desired trajectory is obtained by solving the desired admittance model: in, , and These are the expected inertia, expected damping, and expected stiffness, which vary with time, respectively.

10. The method according to claim 9, characterized in that, The desired inertia, desired damping, and desired stiffness change either stepwise or continuously with time.

11. The method according to claim 10, characterized in that, The transfer function of the low-pass filter is .

12. The method according to claim 11, characterized in that, The robot system is a single-degree-of-freedom rigid manipulator, and its dynamic model is as follows: in, This refers to joint acceleration.

13. A variable admittance robot control system that ensures passivity, characterized in that, include: The parameter acquisition module is used to acquire the real-time state parameters and expected admittance parameters of the robot system. The real-time state parameters include at least interaction torque, joint position and joint velocity, and the expected admittance parameters include the expected inertia, expected damping and expected stiffness of the system. A control law construction module is used to construct an adaptive variable admittance control law that includes an error feedback term and a low-pass filter, wherein the error feedback term is generated based on a sliding mode variable and smoothed by a low-pass filter; An adaptive gain calculation module is used to calculate an adaptive gain term based on real-time state parameters and desired admittance parameters, wherein the adaptive gain term includes stiffness adaptive gain, damping adaptive gain and interactive torque adaptive gain. The constraint establishment module is used to establish a set of constraint inequalities for the gain of the error feedback term, the parameters of the low-pass filter, and the adaptive gain term based on the passive condition of the robot system. The optimization solution module is used to solve for the optimal error feedback gain within the feasible region of the system of constraint inequalities with the goal of maximizing the gain of the error feedback term. If a feasible solution exists, the optimal error feedback gain is adopted; otherwise, the gain of the error feedback term is set to zero. The control output module is used to substitute the adaptive gain term and the optimal error feedback gain into the adaptive variable admittance control law, calculate the final control torque, and output it to the robot actuator.

14. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed, enables the at least one processor to perform the passive variable admittance robot control method according to any one of claims 1-12.

15. A non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the passive variable admittance robot control method according to any one of claims 1-12.

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