RBF-based mobile manipulator self-adaptive control method

A mobile manipulator and adaptive control technology, applied in manipulators, program-controlled manipulators, manufacturing tools, etc., can solve the problems of complex structure, non-integrity and nonlinearity of mobile manipulators, and achieve satisfactory tracking characteristics, strong robustness, The effect of improving tracking accuracy

Inactive Publication Date: 2019-01-11
上海神添实业有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, due to the complex structure of the mobile manipulator, strong coupling, nonlinearity, non-integrity and other problems, the precise control of the mobile manipulator is quite challenging.

Method used

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  • RBF-based mobile manipulator self-adaptive control method
  • RBF-based mobile manipulator self-adaptive control method
  • RBF-based mobile manipulator self-adaptive control method

Examples

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Embodiment

[0105] The method of the present invention is illustrated below through a specific embodiment. Such as image 3 As shown, in this example, the mobile manipulator is composed of a two-wheel differential mobile platform and a two-degree-of-freedom serial manipulator.

[0106] right and For each element of , a 200-node RBF neural network is used. The parameters are taken respectively: Γ k =diag[10],Q k =diag[10],N k =diag[10],T k =10, K=diag[30].

[0107] Kinematic control parameters of the mobile platform: k x =5,k y =4,k φ =10.

[0108]The ideal speed of the mobile platform is:

[0109] [v d ω d ] T =[0.1πcos(0.1πt)0.1πcos(0.1πt)] T ;

[0110] The ideal position of the manipulator joint is:

[0111] [q 1d q 2d ] T =[sin(0.1πt)sin(0.1πt)] T .

[0112] The Simulink simulation program of the neural network for the motion control of the mobile manipulator is established, and the motion output of the mobile manipulator is simulated as follows: Figure...

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Abstract

The invention discloses an RBF neural network based mobile manipulator self-adaptive control method. The method comprises the following steps of S1, establishing a standard mobile manipulator dynamical model; S2, constructing an RBF neural network of the robot dynamic model; S3, designing a mobile manipulator trajectory tracking method with the adaptive capability through the constructed neural network; S4, automatically identifying unknown mobile platform and manipulator dynamic parameters through online learning, and conducting closed-loop identification and compensation on the unknown dynamic parameters, wherein the parameters of the RBF neural network can be updated on line, and finally, the feasibility and effectiveness of the simulation verification control method are verified. Through the method, output errors caused by the unknown dynamic parameters and external disturbance can be eliminated completely without an accurate robot dynamic model; the deficiency that a model-based robot control scheme cannot be implemented without the accurate dynamic model is made up; and the dynamic performance of a mobile manipulator and the trajectory tracking precision of a joint space areimproved.

Description

technical field [0001] The invention relates to the field of stable control of a mobile manipulator, in particular to an adaptive control method for a mobile manipulator based on an RBF neural network. Background technique [0002] At present, robot technology is developing in the direction of high speed, high precision and intelligence. Therefore, higher requirements are put forward for the control accuracy of robots. At present, most of the typical robot control adopts PID control. For applications where the trajectory accuracy is not so high, PID control can meet the requirements. However, if the control accuracy of the robot is to be improved, a control method considering the dynamic model of the robot must be constructed, such as torque control, dynamic feedforward control, etc. These control methods are based on a complete dynamic model of the robot. However, the dynamic models obtained by various typical modeling methods are only ideal results. In actual situations...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): B25J9/16
CPCB25J9/1605B25J9/161B25J9/163
Inventor 钱阳吴雄君刘剑韩非
Owner 上海神添实业有限公司
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