具有非仿射非线性故障和不确定性的切换多智能体系统的冲突约束控制。

IF 10.5 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Xiyue Guo,Huaguang Zhang,Xiaohui Yue,Tianbiao Wang
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引用次数: 0

摘要

研究了具有非仿射非线性故障的切换多智能体系统的冲突约束控制方法。现有的状态约束研究通常假设参考信号始终处于约束集内。然而,在实际操作中,约束可能在系统运行过程中被动态检测,并与预定义的参考信号发生冲突,从而导致约束边界的短暂违反。当参考信号不能保持在规定范围内时,许多基于BLF和非线性变换的反演控制方法失效。为了解决这一问题,采用虚拟圆方法构造了一种新的SRS,并提出了一种冲突约束控制方法。通过将普通Lyapunov函数与径向基函数神经网络相结合,可以有效地补偿开关行为和非仿射非线性故障的影响。在坐标变换中还引入了移位函数,进一步放宽了对初值的约束。通过多次仿真实验验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Conflict Constrained Control for Switched Multiagent Systems With Nonaffine Nonlinear Faults and Uncertainties.
This article investigates a conflict-constrained control method for switched multiagent systems with nonaffine nonlinear faults. Existing studies on state constraints often assume that the reference signal always stays within the constraint set. However, in practice, constraints may be dynamically detected during system operation and conflict with predefined reference signals, causing brief violations of the constraint boundaries. When the reference signal cannot remain within the prescribed range, many backstepping control methods based on BLF and nonlinear transformations become ineffective. To address this, a new SRS is constructed by using a virtual circle approach, and a conflict-constrained control method is proposed. By combining a common Lyapunov function and the radial basis function neural network, the effects of switching behavior and nonaffine nonlinear faults can be effectively compensated. A shift function is also introduced in the coordinate transformation to further relax constraints on initial values. The proposed method is validated through multiple simulation experiments.
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来源期刊
IEEE Transactions on Cybernetics
IEEE Transactions on Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
25.40
自引率
11.00%
发文量
1869
期刊介绍: The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.
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