Adaptive super twisting observer-based prescribed time integral sliding mode tracking control of uncertain robotic manipulators

IF 3.9 4区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS International Journal of Adaptive Control and Signal Processing Pub Date : 2024-04-30 DOI:10.1002/acs.3824
Hesong Shen, Tangzhong Song, Lijin Fang, Huaizhen Wang, Yue Zhang
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Abstract

A novel integral sliding mode control (ISMC) strategy combined with an adaptive super twisting observer (ASTO) for an uncertain robotic manipulator tracking control system is presented in this article. The comprehensive uncertainties including both parameter perturbations and external disturbances are considered during the controller design. Firstly, a new nominal control law with prescribed time convergent property based on time varying scaling function is presented for the system without uncertainties. Then this nominal control law constitutes the prescribed time convergent sliding surface for ISMC. As the reaching phase is eliminated in ISMC, leading to the prescribed time stability of the whole control system without uncertainties. Secondly, take the system uncertainties (both the matched and unmatched uncertainties) into consideration, two ASTOs are designed for handling them. So, the lumped uncertainties of the robotic manipulator control system can be well estimated and compensated in finite time with the help of backstepping method. Besides, the finite time convergent adaptive switching gains of the ASTO make the system stable without knowing the bounds of the uncertainties exactly and suppress the chattering phenomenon of control input. Finally, the proposed control algorithm is validated by simulation and experiment on a robotic manipulator. Also, from a quantitative analysis, we testify the proposed control scheme outperforms the compared one in all of the discussed cases of simulation part.

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基于规定时间积分滑模跟踪控制的不确定机器人操纵器的自适应超扭曲观测器
摘要 本文介绍了一种结合自适应超扭曲观测器(ASTO)的新型积分滑模控制(ISMC)策略,用于不确定的机器人机械手跟踪控制系统。在控制器设计过程中,考虑了包括参数扰动和外部干扰在内的综合不确定性。首先,针对无不确定性系统提出了一种基于时变缩放函数的、具有规定时间收敛特性的新标称控制律。然后,该标称控制法则构成了 ISMC 的规定时间收敛滑动面。由于在 ISMC 中消除了到达阶段,导致整个无不确定性控制系统的规定时间稳定性。其次,考虑到系统的不确定性(包括匹配不确定性和非匹配不确定性),设计了两个 ASTO 来处理它们。因此,借助反步进方法,可以在有限时间内很好地估计和补偿机器人机械手控制系统的成组不确定性。此外,ASTO 的有限时间收敛自适应开关增益使系统在不确切知道不确定性边界的情况下保持稳定,并抑制了控制输入的颤振现象。最后,所提出的控制算法通过仿真和机器人机械手实验得到了验证。此外,通过定量分析,我们证明在模拟部分讨论的所有情况下,所提出的控制方案都优于比较方案。
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来源期刊
CiteScore
5.30
自引率
16.10%
发文量
163
审稿时长
5 months
期刊介绍: The International Journal of Adaptive Control and Signal Processing is concerned with the design, synthesis and application of estimators or controllers where adaptive features are needed to cope with uncertainties.Papers on signal processing should also have some relevance to adaptive systems. The journal focus is on model based control design approaches rather than heuristic or rule based control design methods. All papers will be expected to include significant novel material. Both the theory and application of adaptive systems and system identification are areas of interest. Papers on applications can include problems in the implementation of algorithms for real time signal processing and control. The stability, convergence, robustness and numerical aspects of adaptive algorithms are also suitable topics. The related subjects of controller tuning, filtering, networks and switching theory are also of interest. Principal areas to be addressed include: Auto-Tuning, Self-Tuning and Model Reference Adaptive Controllers Nonlinear, Robust and Intelligent Adaptive Controllers Linear and Nonlinear Multivariable System Identification and Estimation Identification of Linear Parameter Varying, Distributed and Hybrid Systems Multiple Model Adaptive Control Adaptive Signal processing Theory and Algorithms Adaptation in Multi-Agent Systems Condition Monitoring Systems Fault Detection and Isolation Methods Fault Detection and Isolation Methods Fault-Tolerant Control (system supervision and diagnosis) Learning Systems and Adaptive Modelling Real Time Algorithms for Adaptive Signal Processing and Control Adaptive Signal Processing and Control Applications Adaptive Cloud Architectures and Networking Adaptive Mechanisms for Internet of Things Adaptive Sliding Mode Control.
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