Observer-based adaptive neural network event-triggered quantized control for active suspensions with actuator saturation

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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引用次数: 0

Abstract

This paper proposes an adaptive neural network event-triggered and quantized output feedback control scheme for quarter vehicle active suspensions with actuator saturation. The scheme uses neural networks to approximate the unknown parts of the active suspension. When the system states of the suspension are not entirely available, a state observer is designed to estimate the unknown states. The measurable system states, partially estimated observer states, neural network weights, and a filtered virtual control are sequentially event-triggered, quantified, and transmitted to the controller via in-vehicle networks. The problem of non-differentiable virtual control is solved using dynamic surface control technology in the backstepping quantized control design. Integrating a Gaussian error function and a first-order auxiliary subsystem compensates for the nonlinearity caused by asymmetric saturation. Theoretical analysis proves that all error signals of the closed-loop active suspension system are semi-globally uniformly ultimately bounded, and the Zeno phenomenon can be ruled out. Simulation results validate the effectiveness of the proposed control method.
基于观测器的自适应神经网络事件触发量化控制,用于具有致动器饱和度的主动悬挂系统
本文针对致动器饱和的四分之一车辆主动悬架提出了一种自适应神经网络事件触发和量化输出反馈控制方案。该方案利用神经网络对主动悬架的未知部分进行近似。当悬架的系统状态不完全可用时,设计一个状态观测器来估计未知状态。可测量的系统状态、部分估计的观测器状态、神经网络权重和滤波虚拟控制依次被事件触发、量化,并通过车载网络传输到控制器。在反步进量化控制设计中,利用动态表面控制技术解决了无差别虚拟控制问题。整合高斯误差函数和一阶辅助子系统可补偿非对称饱和引起的非线性问题。理论分析证明,闭环主动悬架系统的所有误差信号都是半全局均匀终界的,可以排除芝诺现象。仿真结果验证了所提控制方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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