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基于模糊免疫计算的欠驱动系统状态空间 MRAC 律鲁棒化。

Robustification of the state-space MRAC law for under-actuated systems via fuzzy-immunological computations.

机构信息

Department of Electrical Engineering, 66896National University of Computer and Emerging Sciences, Lahore, Pakistan.

Department of Mechatronics and Control Engineering, 66914University of Engineering and Technology, Lahore, Pakistan.

出版信息

Sci Prog. 2022 Jul-Sep;105(3):368504221122291. doi: 10.1177/00368504221122291.

Abstract

This paper formulates an enhanced Model-Reference-Adaptive-Controller (MRAC) that is augmented with a fuzzy-immune adaptive regulator to strengthen the disturbance-attenuation capability of closed-loop under-actuated systems. The proposed scheme employs the conventional state-space MRAC and augments it with a pre-configured fuzzy-immune mechanism that acts as a superior regulator to dynamically modulate the adaptation gains of the Lyapunov gain-adjustment law. The immunological computations increase the controller's adaptability to flexibly manipulate the damping control effort under exogenous disturbances. The efficacy of the proposed Immune-MRAC law is comparatively analyzed under practical disturbance conditions by conducting real-time hardware experiments on the QNET rotary pendulum. The experimental outcomes validate the faster transient-recovery behavior and stronger damping effort of the proposed control law against the exogenous disturbances while preserving the system's asymptotic stability and control energy efficiency.

摘要

本文提出了一种增强型模型参考自适应控制器(MRAC),该控制器增加了模糊免疫自适应调节器,以增强欠驱动系统闭环在存在外部干扰时的抗扰能力。所提出的方案采用了传统的状态空间 MRAC,并增加了一个预先配置的模糊免疫机制,作为一个优越的调节器,动态调节李雅普诺夫增益调整律的自适应增益。免疫计算增加了控制器的适应性,以便在外源干扰下灵活地操纵阻尼控制效果。通过在 QNET 旋转摆上进行实时硬件实验,对所提出的免疫-MRAC 律在实际干扰条件下的效果进行了比较分析。实验结果验证了所提出的控制律在外源干扰下具有更快的瞬态恢复行为和更强的阻尼效果,同时保持了系统的渐近稳定性和控制能量效率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5456/10450491/6e651f6f241d/10.1177_00368504221122291-fig1.jpg

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