Robot self-adaptive impedance control method based on biological heuristic neural network

Abstract

The invention belongs to the field of robot control and nonlinear systems, and particularly relates to a robot self-adaptive impedance control method based on a biological heuristic neural network. The problem that real-time accurate control of a robot in a complex nonlinear system cannot be realized in the prior art is solved. The robot self-adaptive impedance control method comprises the steps that initial control moment, expected impedance and movement trails of a system are obtained; a dynamic equation and an expected impedance model of n-degree-of-freedom mechanical arm system containingimpedance are built and a t-moment system real state and an expected state of the robot are correspondingly obtained; a self-adaptive controller is built and a (t+1) moment control moment is obtainedbased on full-state feedback and the biological heuristic neural network; and states are circularly obtained and self-adaptive impedance control and movement control are carried out, until a robot mechanical arm completes the movement trails. According to the robot self-adaptive impedance control method based on the biological heuristic neural network, by combining with a biological heuristic neural network structure and delay feedback, a Hebbian algorithm with an award value adjustment and a structure combining network estimation and full-state feedback are adopted, the system is stable, andthe control precision is high.

Description

Technical field [0001] The invention belongs to the field of robot control and nonlinear systems, and specifically relates to a robot adaptive impedance control method based on a biological heuristic neural network. Background technique [0002] Impedance control is used to solve the problem of safe interaction between the robot and the environment. Robotic impedance control is a complex problem in the field of robotic applications. Due to the complexity of the robot structure, the actual robot system often has a lot of uncertainties, such as strict nonlinearity, unknown environment, unknown system parameters and so on. [0003] In view of the uncertain system impedance control problem, traditional methods are mainly divided into two types: feedback control, such as PID control; predictive control, such as model control. PID controller (proportional-integral-derivative controller) is a common feedback loop component in industrial control applications. It is composed of proportion...

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Problems solved by technology

[0005] In order to solve the above problems in the prior art, that is, the prior art cannot realize the real-time precise impedance control of the robot in the complex nonlinear system, the present invention provides a robot adaptive impedance control method based on a biologically inspired neural network, The method includes:

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