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CN-122008185-A - Human-shaped robot servo constraint robust control method and system based on Stackelberg game

CN122008185ACN 122008185 ACN122008185 ACN 122008185ACN-122008185-A

Abstract

The embodiment of the invention provides a servo constraint robust control method and a servo constraint robust control system for a humanoid robot based on a Stackelberg game, and belongs to the technical field of robot servo control and robust control. The control method comprises the steps of constructing a double-arm system of the humanoid robot, constructing a dynamics model of the humanoid robot with uncertainty according to the double-arm system of the humanoid robot, determining a servo constraint equation of the dynamics model, constructing a robust controller, constructing a control parameter setting model based on a Stackelberg game, carrying out optimization determination on key control parameters in the robust controller according to the control parameter setting model under engineering constraint to obtain an optimal parameter solution, and inputting the optimal parameter solution into the double-arm system of the humanoid robot to obtain optimal performance. According to the invention, the controller parameters are optimized and set through the Stackelberg game, so that the system has high-precision track tracking capability, strong robustness and stable control input under an uncertain environment.

Inventors

  • LI XIANLIANG
  • ZHEN SHENGCHAO
  • ZHENG HONGMEI

Assignees

  • 合肥工业大学

Dates

Publication Date
20260512
Application Date
20251225

Claims (9)

  1. 1. The servo constraint robust control method of the humanoid robot based on the Stackelberg game is characterized by comprising the following steps of: Constructing a double-arm system of the humanoid robot; constructing a humanoid robot dynamics model with uncertainty according to the humanoid robot double-arm system; determining a servo constraint equation of the dynamic model; Constructing a robust controller; Constructing a control parameter setting model based on a Stackelberg game, and carrying out optimization determination under engineering constraint on key control parameters in a robust controller according to the control parameter setting model so as to obtain an optimal parameter solution; and inputting the optimal parameter solution to the humanoid robot double-arm system to obtain optimal performance.
  2. 2. The control method according to claim 1, wherein constructing a humanoid robot dynamics model with uncertainty from the humanoid robot double-arm system comprises: Constructing a humanoid robot dynamics model according to a formula (1), ,(1) Wherein, the As an inertial matrix of the system, As the centrifugal force of the system of the coriolis, As the gravitational term of the system, The torque is controlled for the system and, As an uncertainty parameter of the system, For the angle of the joint, In order to achieve the angular velocity of the joint, For the angular acceleration of the joint, In order to be able to take time, Is an uncertainty parameter of the system.
  3. 3. The control method according to claim 2, characterized in that determining a servo constraint equation of the dynamics model comprises: Simplifying the double-arm system of the humanoid robot into a system of two plane two-degree-of-freedom mechanical arms; Determining the expected motion trail of the two-plane two-degree-of-freedom mechanical arm according to a formula (2), ,(2) A first order constraint is obtained according to equation (3), ,(3) Obtaining a second order constraint according to equation (4), ,(4) Wherein, the In order for the motion profile to be desired, In order to constrain the matrix, For the angle of the joint in the system, For the angular velocity of the joints in the system, For the angular acceleration of the joints in the system, To expect first order constraint and , Is a desired second order constraint and , Respectively the dimension of the desired first-order constraint, the dimension of the desired second-order constraint, and 。
  4. 4. A control method according to claim 3, characterized in that constructing a robust controller comprises: Determining a system control matrix according to formulas (5) to (8), ,(5) ,(6) ,(7) ,(8) Wherein, the As a term of the nominal constraint force, In order to correct the term(s), In order for the feedback term to be robust, Is a positive definite matrix of the matrix and the matrix, As the weight coefficient of the light-emitting diode, Is a constant control parameter, which is a control parameter, , As a first intermediate parameter, a second intermediate parameter, In the form of a symmetric term, As a first control parameter, In order to have a smooth switching function, For the weighted constrained error vector(s), As the centrifugal force of the system of the coriolis, Is the gravitational term of the system.
  5. 5. The control method according to claim 1, wherein constructing a control parameter tuning model based on a jackbelg game, and performing optimization determination under engineering constraint on key control parameters in a robust controller according to the control parameter tuning model, to obtain an optimal parameter solution, comprises: Constructing a control parameter setting model based on a Stackelberg game; determining that the first control parameter is a leader of the game according to the robust controller; Determining that the second control parameter is a follower of the game according to the robust controller; And the leader and the follower perform a Stackelberg game according to the control parameter setting model so as to perform optimization setting on the control parameters, and acquire an optimal parameter solution.
  6. 6. The control method according to claim 5, wherein constructing a control parameter tuning model based on a jackbelg game comprises: The cost function of the leader is determined according to equation (9), ,(9) The cost function of the follower is determined according to equation (10), ,(10) Wherein, the As a function of the cost of the leader, Is the transient performance index of the system, As a function of the cost of the follower, Is the steady-state performance index of the system, Is that The operation is carried out by the method, As a first control parameter, As a second control parameter, Is a fuzzy number with uncertainty.
  7. 7. The control method of claim 6, wherein the leader and follower perform a jackbelg game to optimally set control parameters according to the control parameter setting model, and obtaining an optimal parameter solution, comprising: the leader selects parameters in a first decision set; the follower determines a policy to minimize a cost function of the follower based on the leader-selected parameters; the leader minimizes the objective function of the leader according to the strategy to obtain the optimal solution of the leader; the follower updates the strategy according to the optimal solution of the leader to obtain the optimal solution of the follower in the second decision set.
  8. 8. The control method of claim 7, wherein the leader and follower perform a jackbelg game to optimally set control parameters according to the control parameter setting model and obtain an optimal parameter solution, further comprising: Verifying the optimal solution under sufficient conditions; Outputting the optimal parameter solution under the condition that verification is passed; And (3) under the condition that verification is not passed, carrying out the Stackelberg game again, and carrying out optimization setting on the control parameters to obtain an optimal parameter solution.
  9. 9. A servo-constrained robust control system for a humanoid robot based on a jackberg game, characterized in that the system comprises a processor for executing the control method according to any one of claims 1 to 8.

Description

Human-shaped robot servo constraint robust control method and system based on Stackelberg game Technical Field The invention relates to the technical field of robot servo control and robust control, in particular to a humanoid robot servo constraint robust control method and system based on a Stackelberg game. Background Humanoid robots are widely used in the fields of service, manufacturing, entertainment, man-machine interaction, and the like. The humanoid robot arms typically perform highly synchronized and force sensitive operations in a constrained, typically unstructured, three-dimensional environment, which inherently involve closed-chain interactions and require the end effector to maintain precise pose as compared to performing tasks with a single arm, thus presenting challenges in dynamic coupling of the system, robust coordinated control, and other aspects. The core for realizing the accurate operation is high-precision track following control. However, the double arm robot is inevitably affected by various uncertainty factors in actual operation, such as dynamic changes of load, sensor measurement errors, unmodeled dynamics and external disturbances, etc. These uncertainties can significantly reduce the tracking performance of the system and even lead to system instability. In terms of dynamic modeling, conventional energy-based lagrangian approaches suffer from deficiencies in dealing with non-ideal constraints. While the Udwadia-Kalaba (U-K) equation provides a unified and accurate framework for modeling such constrained mechanical systems and translates the trajectory tracking problem into a servo constraint following problem, how to effectively deal with system uncertainty under this framework remains a critical issue. Prior studies have attempted to combine U-K methods with robust control to suppress unknown disturbances, but their controller designs often rely on experience or trial-and-error to set parameters, lacking systematic optimization theory guidance. In addition, existing robust control parameter optimization methods focus on a single design objective and a single parameter, and it is difficult to balance the complex trade-off relationship between system performance (e.g., transient response, steady state accuracy) and control cost (e.g., energy consumption). Although gambling theory (e.g., nash gambling) has been introduced into the control field to solve the multi-objective optimization problem, how to build a non-cooperative gambling model and to use leader-follower decision sequence characteristics described by the tuckelberg gambling theory to cooperatively optimize a plurality of key control parameters has not been fully studied on the specific problem of servo constraint tracking of a dual-arm robot. Disclosure of Invention The embodiment of the invention aims to provide a servo constraint robust control method and system for a humanoid robot based on a Stackelberg game, which solve the problems of low tracking precision, robustness and overall control efficiency of a double-arm humanoid robot in a complex uncertain environment. In order to achieve the above purpose, the embodiment of the invention provides a humanoid robot servo constraint robust control method based on a Stackelberg game, which comprises the following steps: Constructing a double-arm system of the humanoid robot; constructing a humanoid robot dynamics model with uncertainty according to the humanoid robot double-arm system; determining a servo constraint equation of the dynamic model; Constructing a robust controller; Constructing a control parameter setting model based on a Stackelberg game, and carrying out optimization determination under engineering constraint on key control parameters in a robust controller according to the control parameter setting model so as to obtain an optimal parameter solution; and inputting the optimal parameter solution to the humanoid robot double-arm system to obtain optimal performance. Optionally, constructing a humanoid robot dynamics model with uncertainty according to the humanoid robot double-arm system includes: Constructing a humanoid robot dynamics model according to a formula (1), ,(1) Wherein, the As an inertial matrix of the system,As the centrifugal force of the system of the coriolis,As the gravitational term of the system,The torque is controlled for the system and,As an uncertainty parameter of the system,For the angle of the joint,In order to achieve the angular velocity of the joint,For the angular acceleration of the joint,In order to be able to take time,Is an uncertainty parameter of the system. Optionally, determining a servo constraint equation of the dynamics model includes: Simplifying the double-arm system of the humanoid robot into a system of two plane two-degree-of-freedom mechanical arms; Determining the expected motion trail of the two-plane two-degree-of-freedom mechanical arm according to a formula (2), ,(2) A first order constraint is obta