Adaptive Neural Control of Time-Varying Constrained Direct-Current Motor System with Input Saturation

被引：0

作者：

Li, Dapeng ^{[1
]}

Han, Honggui ^{[1
]}

Qiao, Junfei ^{[1
]}

机构：

[1] Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China

来源：

2022 41ST CHINESE CONTROL CONFERENCE (CCC) | 2022年

基金：

北京市自然科学基金; 美国国家科学基金会;

关键词：

Adaptive neural control; nonlinear mappings; direct-current motor system; input saturation; DC MOTOR; MANIPULATOR; DRIVES;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper focuses on the problem of the time-varying constrained direct-current (DC) motor system with input saturation. The radial basis function neural network with less parameters approach is introduced as an identifier to estimate the unknown dynamics in DC motor system. To avoid repeated verification of the feasibility conditions on the virtual control, the nonlinear mapping is employed in each step of backstepping procedure, the prescribed transient performance on tracking error as well as the constraint on system states are directly achieved even when the actuator saturation is taken into account. Based on the Lyapunov analysis, the developed control strategy can ensure that all the closed-loop signals are bounded, the constraints on full system states and tracking error are achieved. The simulation example is used to illustrate the effectiveness of the developed control strategy.

引用

页码：363 / 368

页数：6

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