用于惯性稳定跟踪平台抑制瞬态峰值扰动的非线性加速度扰动观测器。

IF 6.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS ISA transactions Pub Date : 2025-05-01 Epub Date: 2025-03-01 DOI:10.1016/j.isatra.2025.02.024
Qihui Bian , Qingqing Miao , Zhiyong Yu , Tao Tang , Haotong Ma
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引用次数: 0

摘要

提出了一种高带宽非线性加速度扰动观测器(NOADOB),用于抑制惯性稳定跟踪平台(ISTP)速度变换引起的瞬态峰值扰动。该方法主要基于提取加速度峰值信号并应用饱和函数。观测器结构采用加速度测量,结合速度环输入和控制量获取峰值信号。与一般的扰动观测器不同,采用加速度峰值提取方法快速捕捉误差变化,有效降低信号噪声对观测器实现的影响。为了权衡观测器带宽和系统稳定性,采用非线性饱和函数限制滤波器输出的部分补偿方案。分析了饱和阈值与滤波器带宽之间的关系,以达到最佳平衡。理论和实验证明了NOADOB对误差峰的衰减效果。此外,该方法加速了对峰值扰动的动态响应,突破了精确建模的限制,提高了抗干扰性能。
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Nonlinear acceleration disturbance observer to reject transient peak disturbances for an inertial stabilization-tracking platform
A high-bandwidth nonlinear acceleration disturbance observer (NOADOB) is proposed to reject transient peak disturbances mainly arising from velocity commutation of an inertial stabilization-tracking platform (ISTP). The approach is essentially based on extracting acceleration peak signals and applying a saturation function. The observer structure employs acceleration measurements and combines velocity loop input and control quantity to acquire peak signals. Differing from the general disturbance observer, the extraction of acceleration peaks is used to quickly capture the change in error and effectively reduce the impact of signal noise for the observer implementation. To trade off the observer bandwidth and the system stability, a partial compensatory scheme of limiting the filter output with a nonlinear saturation function is adopted. The relationship between the saturation threshold and the filter bandwidth is analyzed for optimal balance. It is theoretically and experimentally proved the effectiveness of NOADOB in attenuating error peaks. Moreover, the proposal accelerates the dynamic response to peak disturbances, breaks through the constraint of accurate modeling, and improves disturbance rejection performance.
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来源期刊
ISA transactions
ISA transactions 工程技术-工程:综合
CiteScore
11.70
自引率
12.30%
发文量
824
审稿时长
4.4 months
期刊介绍: ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.
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