Importance measure analysis of design variables and uncertain parameters in multidisciplinary systems

Yan, Yuhua; Wang, Xiaojun; Li, Yifei; Zeng, Linxi; Li, Yunlong; Wang, Lei

doi:10.1016/j.apm.2022.01.029

Cited by 4 publications

(1 citation statement)

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“…Systems uncertainty is a common situation that can lead to a significant degradation of control performance and may even affect the stability of the systems [27,28]. To end it, many scholars have conducted extensive research on uncertain systems [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Network Prescribed Time Control for Constrained Multi-Robotics Systems with Parametric Uncertainties

Tang,

Lin,

Liu

et al. 2024

Mathematics

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This study designed an adaptive neural network (NN) control method for a category of multi-robotic systems with parametric uncertainties. In practical engineering applications, systems commonly face design challenges due to uncertainties in their parameters. Especially when a system’s parameters are completely unknown, the unpredictability caused by parametric uncertainties may increase control complexity, and even cause system instability. To address these problems, an adaptive NN compensation mechanism is proposed. Moreover, using backstepping and barrier Lyapunov functions (BLFs), guarantee that state constraints can be ensured. With the aid of the transform function, systems’ convergence speeds were greatly improved. Under the implemented control strategy, the prescribed time control of multi-robotic systems with parametric uncertainties under the prescribed performance was achieved. Finally, the efficacy of the proposed control strategy was verified through the application of several cases.

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