Adaptive Fuzzy Backstepping and Backstepping Sliding Mode Controllers Based on ICD Observer: A Comparative Study

IF 1 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Uncertainty Fuzziness and Knowledge-Based Systems Pub Date : 2024-04-19 DOI:10.1142/s0218488524500065
Safa Choueikh, Marwen Kermani, Faouzi M’Sahli
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Abstract

This paper develops a Fuzzy Adaptive Backstepping Control (FABC) and a Fuzzy Adaptive Backstepping Sliding Mode Control (FABSMC) for Single-Input Single-Output (SISO) nonlinear-systems with unmeasured states. The proposed adaptive schemes are fully compared. Thus, the Fuzzy Type-2 (FT2) concept and the High-Order Integral-Chain Differentiator (HOICD) are used as two universal approximators. Indeed, the first one is employed to approximate the nonlinear system model’s and the second one to estimate the unknown states. Special attention is paid for the used approximators robustness under unmodeled dynamics, parameter variations and process noise.

It should be noted that the asymptotic stability of both the fuzzy adaptive controls and the observer convergence for each scheme have been proved. In addition, the employed schemes have been simulated on a two-tank coupled nonlinear system. Thus, from simulation results, we can prove that the proposed methods guarantee that all signals for closed loop systems are both regular and bounded. Specifically, it can be shown that the performances of the proposed Fuzzy Interval Type-2 (FIT2) schemes are significantly improved compared with Fuzzy Type-1 (FT1) schemes in presence of external disturbances.

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基于 ICD 观察器的自适应模糊反向和反向滑模控制器:比较研究
本文针对具有未测量状态的单输入单输出(SISO)非线性系统,开发了模糊自适应后退控制(FABC)和模糊自适应后退滑模控制(FABSMC)。对提出的自适应方案进行了充分比较。因此,模糊 2 型(FT2)概念和高阶积分链微分器(HOICD)被用作两个通用近似器。事实上,前者用于近似非线性系统模型,后者用于估计未知状态。值得注意的是,每个方案的模糊自适应控制和观测器收敛的渐近稳定性都已得到证明。此外,还在双油箱耦合非线性系统上模拟了所采用的方案。因此,从仿真结果来看,我们可以证明所提出的方法能保证闭环系统的所有信号都是正则和有界的。具体而言,可以证明与模糊 1 型(FT1)方案相比,所提出的模糊 2 型(FIT2)方案在外部干扰下的性能有了显著提高。
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来源期刊
CiteScore
2.70
自引率
0.00%
发文量
48
审稿时长
13.5 months
期刊介绍: The International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems is a forum for research on various methodologies for the management of imprecise, vague, uncertain or incomplete information. The aim of the journal is to promote theoretical or methodological works dealing with all kinds of methods to represent and manipulate imperfectly described pieces of knowledge, excluding results on pure mathematics or simple applications of existing theoretical results. It is published bimonthly, with worldwide distribution to researchers, engineers, decision-makers, and educators.
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