Data-driven enhanced control method for flexible dual-arm humanoid robot

Abstract

The application discloses a data-driven enhanced control method for a flexible dual-arm humanoid robot, and belongs to the technical field of robot motion control, and comprises the following steps: step S1, based on motion information of the flexible robot, a system model containing unknown dynamic parameters is converted into a linear discrete-time data model with equivalent characteristics; step S2, an improved projection estimation algorithm is designed to realize real-time estimation of pseudo partial derivative parameters of the robot system; step S3, a finite-time performance index function is designed, and a data-driven enhanced controller is obtained based on an optimal control principle; and step S4, a data-driven high-order control barrier function is constructed based on the linear time data model, and a data-driven control law satisfying stability and preset control performance simultaneously is obtained by solving a quadratic programming problem. The application effectively simplifies a modeling step, improves modeling efficiency, and takes into account finite-time convergence performance and optimal control performance of the flexible dual-arm robot.

Description

Technical Field

[0001] This invention belongs to the field of robot motion control technology, specifically providing a data-driven enhanced control method for a flexible dual-arm humanoid robot. Background Technology

[0002] With the continuous advancement of automation technology, the application fields of intelligent robots are becoming increasingly widespread, and their control tasks are becoming more and more complex. Flexible humanoid robots, capable of mimicking human behavior and movements to complete various complex tasks, have demonstrated immense value and potential in both research and application. However, the motion control of the two arms of humanoid robots has always been a major challenge in the field of robot motion control.

[0003] The dynamics of flexible dual-arm robots are particularly complex due to their highly nonlinear and strongly coupled characteristics, posing significant challenges to dynamic modeling and control. Traditional control strategies typically rely on precise parameter settings and extensive experimental debugging, processes that are not only time-consuming but also costly. Furthermore, the robot's flexible joints are prone to structural resonance during acceleration, deceleration, or changes in external load, leading to mechanical vibrations. This not only affects the accuracy of trajectory tracking but also introduces additional disturbances into the control system, significantly increasing the difficulty of maintaining high-precision motion control while ensuring overall system stability. Therefore, exploring data-driven control strategies is especially important when the dynamics of flexible dual-arm humanoid robots are incomplete or completely unknown.

[0004] It is important to note that traditional model predictive control and other algorithms heavily rely on accurate mathematical models of the robot. While they can model the dynamic characteristics of the system under certain assumptions, this process is very time-consuming and relatively complex, making direct controller analysis difficult. Furthermore, existing robot control methods are mostly based on asymptotic convergence frameworks, whose convergence speed is difficult to guarantee. This, to some extent, limits the motion performance of flexible dual-arm robots, especially when there are uncertainties in the system model. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, this invention provides a data-driven enhanced control method for flexible dual-arm humanoid robots that effectively simplifies modeling steps, improves modeling efficiency, and balances finite-time convergence performance and optimal control performance. First, a dynamic model of the flexible robot is established, and a linear discrete-time data model with equivalent properties is built by combining real-time feedback of motion control information. Second, in each control cycle, an improved projection estimation algorithm is used to estimate the pseudo-partial derivative parameters of the robot system online in real time. Based on this, an adjustable step size parameter and a penalty factor are introduced to enhance the adaptability and flexibility of the estimation algorithm under different control tasks. Finally, based on the constructed linear discrete-time data model, a finite-time performance exponential function and a data-driven high-order control barrier function are designed. By solving a quadratic programming optimization problem, a data-driven enhanced control law is generated that simultaneously ensures system stability and meets preset control performance.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] A data-driven augmented control method for a flexible dual-arm humanoid robot includes the following steps:

[0008] Step S1: Based on the motion information of the flexible robot, the system model containing unknown dynamic parameters is transformed into a linear discrete-time data model with equivalent properties;

[0009] Step S2: Design an improved projection estimation algorithm to estimate the pseudo-partial derivative parameters of the robot system in real time;

[0010] Step S3: Design a finite-time performance exponential function and obtain a data-driven enhanced controller based on the optimal control principle;

[0011] Step S4: Construct a data-driven high-order control barrier function based on a linear time data model, and obtain a data-driven control law that simultaneously satisfies stability and preset control performance by solving a quadratic programming problem.

[0012] Furthermore, in step S1, firstly, a dynamic model of the flexible dual-arm humanoid robot system in the joint space is established; secondly, a parameter projection estimation algorithm is designed using the system motion control information to estimate the pseudo-partial derivative parameters of the system online. Establish an equivalent linear time data model.

[0013] In step S1, the following dynamic model of the flexible joint robot is constructed:

[0014] (1);

[0015] in, These represent the robot's unknown inertia matrix, Coriolis force matrix, and gravity matrix, respectively. Indicates time, For the system Time-based control input, Representing robot system Position, velocity, and acceleration at any given moment;

[0016] Based on the real-time motion control information of the system, a discrete linear time data model is established:

[0017] (2);

[0018] in, Represents robots Position output at any given time Represents robots Time-based control input, These are pseudo-partial derivative parameters for the robot's discrete-time system, used to construct the robot's dynamic data model.

[0019] Furthermore, in step S2, in each control cycle, based on the robot's control input... and control output We design an estimation criterion function for the pseudo-partial derivative parameter and obtain the estimation formula by solving for the extrema. Based on this, we design an adjustable step size parameter and a penalty factor to enhance the flexibility of the estimation algorithm.

[0020] In step S2, the following estimation criterion function is constructed:

[0021] (3);

[0022] in, Represents robots Position output at any given time Yes The estimated value, Yes The estimated value, It is a penalty factor that makes the estimated value smoother. , Represents robots Time-based control input;

[0023] After minimization, the following estimation formula is obtained:

[0024] (4);

[0025] in, This is the step size parameter used to adjust the estimation algorithm.

[0026] Furthermore, in step S3, based on the linear time data model, a finite-time performance exponential function is constructed by designing convergence ratio parameters; on this basis, the data-driven control law is solved online using the optimal control principle, so as to enhance the rapid convergence of the robot system without relying on detailed system dynamics information.

[0027] In step S3, the following finite-time performance function is designed:

[0028] (5);

[0029] in, These are the convergence ratio parameters of the designed robot, used to adjust the convergence speed of the controller. These are weighting parameters designed to limit variations in the control input signal, resulting in a smoother control input. yes Real-time tracking signal;

[0030] Based on the optimal control principle, the following data-driven enhanced control law is obtained:

[0031] (6);

[0032] in, These are the control parameters designed to make the robot controller more general.

[0033] In step S4, a performance constraint function is constructed based on the performance constraint function. Higher-order data-driven control barrier function To ensure error performance constraints with higher degrees of freedom, a data-driven control law that simultaneously satisfies stability and preset control performance is generated in real time by solving a quadratic programming problem that unifies the data-driven enhanced controller and the high-order control barrier function.

[0034] In step S4, for the flexible joint robot, the following data-driven obstacle control function is constructed:

[0035] (7);

[0036] in, Yes abbreviation of symbols, , , It is a K-type function. , It is the error boundary. It is the error performance constraint function. yes Real-time tracking signal;

[0037] Finally, by solving a quadratic programming problem that integrates the control function and the data-driven model, the optimal control input is generated in real time.

[0038] (8);

[0039] in, These are the upper and lower limits of the control input, respectively. The nominal control input is obtained from equation (6). This is the optimal control input.

[0040] The technical concept of this invention is as follows: First, a dynamic model of the flexible robot is established, and a linear discrete-time data model with equivalent properties is established by combining real-time feedback of motion control information. Second, in each control cycle, an improved projection estimation algorithm is used to estimate the pseudo-partial derivative parameters of the system online in real time. On this basis, an adjustable step size parameter and a penalty factor are introduced to enhance the adaptability and flexibility of the estimation algorithm under different control tasks. Finally, based on the constructed linear discrete-time data model, a finite-time performance exponential function and a data-driven high-order control barrier function are designed, and a data-driven enhanced control law that simultaneously ensures system stability and meets preset control performance is generated by solving a quadratic programming optimization problem.

[0041] Compared with the prior art, the beneficial effects of the present invention are mainly reflected in:

[0042] 1) This invention establishes a linear discrete-time data model for high-precision motion control of flexible dual-arm robots. Compared with the complex and time-consuming modeling process in traditional control methods, the method of this invention effectively simplifies the modeling steps, improves modeling efficiency, and provides a more efficient and accurate control framework for the high-precision motion of flexible dual-arm robots.

[0043] 2) Compared with traditional asymptotic convergence control methods, this invention balances the finite-time convergence performance and optimal control performance of the flexible dual-arm robot. By designing a finite-time performance exponential function, it ensures that the system can quickly stabilize within a specified time, and at the same time, it establishes a data-driven high-order control barrier function to strictly guarantee that the system meets the error constraint requirements throughout the entire operation. Attached Figure Description

[0044] Figure 1 This is a comparison curve of the tracking control effect of the robot's left arm joint in this invention;

[0045] Figure 2 This is a comparison curve of the tracking control effect of the robot's right arm joint in this invention;

[0046] Figure 3This is a flowchart of the data-driven enhanced control method for a flexible dual-arm humanoid robot according to the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0048] Reference Figures 1-3 A data-driven augmented control method for a flexible dual-arm humanoid robot includes the following steps:

[0049] Step S1 transforms the system model containing unknown dynamic parameters into a linear discrete-time data model with equivalent properties based on the motion information of the flexible robot.

[0050] In step S1, firstly, a dynamic model of the flexible dual-arm humanoid robot system in the joint space is established; secondly, a parameter projection estimation algorithm is designed using the system motion control information to estimate the pseudo-partial derivative parameters of the system online. To establish an equivalent linear time data model, the process is as follows:

[0051] Construct the following dynamic model of the flexible joint robot:

[0052] (1);

[0053] in, These represent the robot's unknown inertia matrix, Coriolis force matrix, and gravity matrix, respectively. Indicates time, For the system Time-based control input, Representing robot system Position, velocity, and acceleration at any given moment;

[0054] 1.2) Further, based on the real-time motion control information of the system, a discrete linear time data model is established:

[0055] (2);

[0056] in, Represents robots Position output at any given time Represents robots Time-based control input, For the pseudo-partial derivative parameters of the robot's discrete-time system, The pseudo-partial derivative parameter of the robot's discrete-time system is set to its initial value. .

[0057] Step S2: Design an improved projection estimation algorithm to estimate the pseudo-partial derivative parameters of the robot system in real time;

[0058] In step S2, during each control cycle, based on the robot's control input... and control output Design an estimation criterion function for the pseudo-partial derivative parameters, and obtain the estimation formula by solving for the extrema. Based on this, design an adjustable step size parameter and a penalty factor to enhance the flexibility of the estimation algorithm; the process is as follows:

[0059] Construct the following estimation criterion function:

[0060] (3);

[0061] in, , Represents robots Time-based control input, Represents robots Position output at any given time Yes The estimated value, Yes To make the estimated value smoother, the penalty factor is set to... ;

[0062] After minimization, the following estimation formula is obtained:

[0063] (4);

[0064] The step size parameter of the estimation algorithm is set as follows: ;

[0065] Design a reset algorithm to enhance the stability of the robot system:

[0066] (9)

[0067] The threshold parameter is set to .

[0068] Step S3: Design a finite-time performance exponential function and obtain a data-driven enhanced controller based on the optimal control principle;

[0069] In step S3, based on the linear-time data model, a finite-time performance exponential function is constructed by designing convergence scaling parameters. Furthermore, based on the optimal control principle, the rapid convergence of the robot system is enhanced without relying on detailed system dynamics information. The process is as follows:

[0070] 3.1) Design the following finite-time performance function:

[0071] (5);

[0072] The convergence ratio parameter is set to... The weight parameters are set to ;

[0073] 3.2) Based on the optimal control principle, the following data-driven enhanced control law is obtained:

[0074] (6);

[0075] The controller parameters are set as follows: .

[0076] Step S4: Construct a data-driven high-order control barrier function based on a linear-time data model, and obtain a data-driven control law that simultaneously satisfies stability and preset control performance by solving a quadratic programming problem.

[0077] In step S4, a performance constraint function is constructed based on the performance constraint function. Higher-order data-driven control barrier function To ensure error performance constraints with higher degrees of freedom, a quadratic programming problem based on a unified data-driven enhanced controller and a higher-order control barrier function is solved to generate a data-driven control law that simultaneously satisfies stability and preset control performance in real time. The process is as follows:

[0078] 4.1) For flexible joint robots, construct the following data-driven obstacle control function:

[0079] (7);

[0080] in, Yes abbreviation of symbols, , , It is a K-type function. , ; It is the error boundary. ; It is the error performance constraint function. yes Real-time tracking signal;

[0081] Finally, by solving a quadratic programming problem that integrates the control function and the data-driven model, the optimal control input is generated in real time.

[0082] (8);

[0083] in, These are the upper and lower limits of the control input, respectively. The nominal control input is obtained from equation (6). This is the optimal control input.

[0084] In this embodiment, the robot's sampling period T is set as... That is, using each An equivalent dynamic data model is constructed from the input and output information obtained from the system, and then the control input is calculated and sent to the robot. The comparison curves of the tracking control effect of the robot's left arm joint are shown below. Figure 1 As shown in the figure. The comparison curves of the tracking control effect of the robot's right arm joint are as follows. Figure 2 As shown in the figure, the experimental results demonstrate that, compared to traditional model predictive control algorithms, the proposed data-driven enhanced controller exhibits better steady-state accuracy and transient response. Throughout the control process, the robot's motion trajectory under this invention is relatively smooth, and no overshoot or oscillation occurs.

[0085] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A data-driven augmented control method for a flexible dual-arm humanoid robot, characterized in that, The method includes the following steps: Step S1: Based on the motion information of the flexible robot, the system model containing unknown dynamic parameters is transformed into a linear discrete-time data model with equivalent properties; Step S2: Design an improved projection estimation algorithm to estimate the pseudo-partial derivative parameters of the robot system in real time. The improved projection estimation algorithm is as follows: In each control cycle, based on the robot's control input... and control output We design an estimation criterion function for the pseudo-partial derivative parameter and obtain the estimation formula by solving the extremum operation. Based on this, we design an adjustable step size parameter and a penalty factor to enhance the flexibility of the estimation algorithm. Step S3: Design a finite-time performance exponential function and obtain a data-driven enhanced controller based on the optimal control principle. Based on the linear-time data model, construct the finite-time performance exponential function by designing the convergence proportional parameter. On this basis, use the optimal control principle to solve the data-driven control law online to enhance the fast convergence of the robot system without relying on detailed system dynamics information. Step S4: Construct a data-driven high-order control obstacle function based on a linear-time data model, and obtain a data-driven control law that simultaneously satisfies stability and preset control performance by solving a quadratic programming problem. The process is as follows: Based on the performance constraint function, construct a data-driven control law with... Higher-order data-driven control barrier function To ensure error performance constraints with higher degrees of freedom, a data-driven control law that simultaneously satisfies stability and preset control performance is generated in real time by solving a quadratic programming problem that unifies the data-driven enhanced controller and the high-order control barrier function.

2. The data-driven enhanced control method for a flexible dual-arm humanoid robot as described in claim 1, characterized in that, In step S1, firstly, a dynamic model of the flexible dual-arm humanoid robot system in the joint space is established; secondly, a parameter projection estimation algorithm is designed using the system motion control information to estimate the pseudo-partial derivative parameters of the system online. Establish an equivalent linear time data model.

3. The data-driven enhanced control method for a flexible dual-arm humanoid robot as described in claim 2, characterized in that, In step S1, the following dynamic model of the flexible joint robot is constructed: （1）； in, These represent the robot's unknown inertia matrix, Coriolis force matrix, and gravity matrix, respectively. Indicates time, For the system Time-based control input, Representing robot system Position, velocity, and acceleration at any given moment; Based on the real-time motion control information of the system, a discrete linear time data model is established: （2）； in, Represents robots Position output at any given moment Represents robots Time-based control input, These are pseudo-partial derivative parameters for the robot's discrete-time system, used to construct the robot's dynamic data model.

4. The data-driven enhanced control method for a flexible dual-arm humanoid robot as described in claim 3, characterized in that, In step S2, the following estimation criterion function is constructed: （3）； in, Represents robots Position output at any given moment Yes The estimated value, Yes The estimated value, It is a penalty factor that makes the estimated value smoother. , Represents robots Time-based control input; After minimization, the following estimation formula is obtained: （4）； in, This is the step size parameter used to adjust the estimation algorithm.

5. The data-driven enhanced control method for a flexible dual-arm humanoid robot as described in claim 4, characterized in that, In step S3, the following finite-time performance function is designed: （5）； in, These are the convergence ratio parameters of the designed robot, used to adjust the convergence speed of the controller. These are weighting parameters designed to limit variations in the control input signal, resulting in a smoother control input. yes Real-time tracking signal; Based on the optimal control principle, the following data-driven enhanced control law is obtained: （6）； in, These are the control parameters designed to make the robot controller more general.

6. The data-driven enhanced control method for a flexible dual-arm humanoid robot as described in claim 5, characterized in that, In step S4, for the flexible joint robot, the following structure is constructed: Level data-driven control barrier function: （7）； in, Yes abbreviation of symbols, , , It is a K-type function. , It is the error boundary. It is the error performance constraint function. yes Real-time tracking signal; Finally, by solving a quadratic programming problem that integrates the control function and the data-driven model, the optimal control input is generated in real time. （8）； in, These are the upper and lower limits of the control input, respectively. The nominal control input is obtained from equation (6). This is the optimal control input.

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