具有输入饱和的无人直升机柔性固定时间性能自适应跟踪控制

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Zhiyang He, Shuang Shi, Haibo Wang
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引用次数: 0

摘要

针对具有外部干扰和输入饱和的六自由度无人直升机,提出了一种能够保证柔性定时预定性能的自适应跟踪控制策略。与大多数具有输入饱和的规定性能控制方法忽略输入约束与性能约束之间的冲突不同,该策略通过引入灵活的固定时间规定性能函数实现了一种权衡。此外,在饱和阶段临时调整所需的跟踪信号,以增强跟踪任务的可行性。利用双曲正切函数来解决饱和输入的问题。为了补偿包含外部扰动的复合扰动和双曲正切函数的估计误差,采用参数自适应估计方法逼近双曲正切函数的上界。结合反步技术,研究了轨迹跟踪控制器在固定时间内保证跟踪误差满足规定性能的控制方法。仿真对比验证了所提控制方案的有效性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Flexible-Fixed-Time-Performance-Based Adaptive Tracking Control for Unmanned Helicopter With Input Saturation

This article proposes an adaptive tracking control strategy for a 6-DOF unmanned helicopter with external disturbances and input saturation, which is able to guarantee the flexible fixed-time prescribed performance. Different from the majority of prescribed performance control methods with input saturation, which neglect the conflict between input and performance constraints, the strategy offered achieves a trade-off by introducing a flexible fixed-time prescribed performance function. Besides, the desired tracking signals are temporarily adjusted during the saturation phase to enhance the feasibility of the tracking task. A hyperbolic tangent function is utilized to tackle the problem of saturated input. To compensate for the composite disturbances that involve external disturbances and the estimation error of the hyperbolic tangent function, a parameter adaptive estimation method is adopted to approximate its upper bound. Combining with the backstepping technology, the trajectory tracking controller is investigated to guarantee that tracking errors meet the prescribed performance within fixed time. Comparative simulations illustrate the effectiveness and merits of the proposed control scheme.

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来源期刊
International Journal of Robust and Nonlinear Control
International Journal of Robust and Nonlinear Control 工程技术-工程:电子与电气
CiteScore
6.70
自引率
20.50%
发文量
505
审稿时长
2.7 months
期刊介绍: Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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