具有量化输入机械臂系统的预设性能自适应神经网络控制

doi:10.16180/j.cnki.issn1007-7820.2024.12.005

Abstract

Abstract:

In view of the single-link manipulator system with quantized input, a predefined performance adaptive neural network control method based on Funnel control is proposed in this study. Different from the traditional Funnel control scheme, this method can ensure that the system reaches the predefined performance index within the predetermined time by constructing a new performance function, and the neural network and dynamic surface control technology are used to solve the unknown nonlinear items in the system and the differential explosion problem in the traditional backstepping control method. Theoretical analysis shows that the proposed control scheme not only eliminates the negative effects caused by quantized inputs, but also ensures the stability of the system. By adjusting the design parameters in the MATLAB simulation experiment, the predetermined time convergence of the tracking error is realized, which verifies the effectiveness of the proposed control method.

Key words: adaptive control, dynamic surface control, Funnel control, radial basis function neural network, prescribed performance, input quantization, manipulator system, external disturbance

CLC Number:

TP273

CHU Donggang, LIU Ye. Predefined Performance Adaptive Neural Network Control of Manipulator System with Quantized Input[J].Electronic Science and Technology, 2024, 37(12): 32-36.

Figures/Tables 6

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Table 1.

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Figure 5.

References 18

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参数	含义	取值	单位
M	连杆质量	1.00	kg
L	连杆长度	1.00	m
g	重力加速度	9.80	m·s^-2
N	电机转动惯量	0.01	m·s^-2
G	自然阻尼系数	1.00	N·m·rad^-1
B	弹性系数	1.00	-
ℓ	摩擦系数	0.01	-

Predefined Performance Adaptive Neural Network Control of Manipulator System with Quantized Input

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