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Fixed-time predetermined performance recurrent neural network mechanical arm control method

A technology of cyclic neural network and control method, which is applied in the field of fixed-time predetermined performance cyclic neural network manipulator control, which can solve the problems that the tracking error convergence cannot be guaranteed, and transient and steady-state performance cannot be considered.

Active Publication Date: 2019-11-15
NORTHWESTERN POLYTECHNICAL UNIV
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AI Technical Summary

Problems solved by technology

However, these control methods fail to consider transient and steady-state performance, and cannot guarantee that the tracking error converges along a pre-specified performance function

Method used

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  • Fixed-time predetermined performance recurrent neural network mechanical arm control method
  • Fixed-time predetermined performance recurrent neural network mechanical arm control method
  • Fixed-time predetermined performance recurrent neural network mechanical arm control method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0270] Example: Robotic Arm Driven by a DC Motor

[0271] Taking the manipulator driven by DC motor as an example to illustrate the effectiveness of the above fixed time predetermined performance cyclic neural network control method in realizing the ideal track of the driven manipulator. The mathematical model of the manipulator composed of the mechanical subsystem (1) and the electrical subsystem (2) can be expressed as:

[0272]

[0273] The system parameters are selected as L=0.05,K B =0.5, R=0.5, ΔI=0.1cos(t). The dead zone model can be written as:

[0274]

[0275] A neural network control method for a fixed time predetermined performance of a DC motor-driven mechanical arm of the present embodiment includes the following steps:

[0276] (1) Determine the control target: the reference output signal is selected as The predetermined performance function is chosen as The control target is determined as the system output can track the reference output of the sy...

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Abstract

The invention relates to a fixed-time predetermined performance recurrent neural network mechanical arm control method. The method comprises the steps that (1) a mathematical model of a mechanical armdriven by a direct-current motor is established, and an actuator model with an unknown nonlinear dead zone is established; (2) system reference output is determined, and a performance function, whicha tracking error needs to meet, is designed; (3) a fixed-time predetermined performance recurrent neural network controller, a neural network weight update law and a fixed-time differentiator are designed so that system output can track a reference output trajectory in fixed time, and meanwhile the system tracking error is limited within a performance boundary range designated in advance; and (4)stability analysis is performed on a control system, and controller parameters are determined according to the stability analysis result. Through the method, fixed-time predetermined performance trajectory tracking can be realized, so that tracking time is shortened, control precision is improved, and transient performance and steady performance of the system in the control process are guaranteed.

Description

technical field [0001] The invention relates to the field of industrial control, in particular to a method for controlling a mechanical arm of a cyclic neural network with predetermined performance at fixed time. Background technique [0002] High-performance motion control is critical to many industrial applications. High-performance motion control requires the motor to be able to drive the load (mechanical arm) to move along a predetermined trajectory, which puts forward high requirements on tracking time, tracking accuracy, and system transient and steady-state tracking performance. Traditional control methods are based on feed-forward neural networks. However, feed-forward neural network is a static mapping and cannot express dynamic mapping without delay. In addition, the function approximation performance of the feedforward neural network is sensitive to the training data, and its function approximation performance will deteriorate when its input is greatly perturbed...

Claims

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Application Information

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IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 倪骏康
Owner NORTHWESTERN POLYTECHNICAL UNIV
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