Decentralized Adaptive Funnel Control of Uncertain Large-Scale Interconnected Nonlinear System

Control-error-based output-feedback adaptive decentralized neural network controller for interconnected uncertain strict-feedback nonlinear systems with input saturation

“…In contrast, the proposed controller can be executed using unmeasured states, making it particularly advantageous for practical systems. The number of adaptive units required in the proposed method is significantly reduced compared with previous methods Yoo, 2018, 2020;Guo and Zhang, 2021;Huang and Wang, 2018;Jiang et al, 2020;Niu et al, 2018;Shi et al, 2023;Song et al, 2023;Yang et al, 2023;Zhang et al, 2021;Zhang and Wang, 2022;Zhu and Han, 2022) as only one NN is used regardless of the order of the uncertain nonlinear subsystem. In this paper, we design an output-feedback direct adaptive NN controller that basis its adaptation on the control error to control MIMO strict-feedback nonlinear systems with input saturation, external disturbances, and unavailable state for measurement.…”

“…In this study, the interconnection terms are only assumed reasonably bounded without any additional restrictive conditions. In contrast to Yang et al (2023), Cui et al (2023), Zhang and Wang (2022), Zhang et al (2021), Guo and Zhang (2021), Yoo (2021), Zhou et al (2020), , Ma et al (2020), Niu et al (2018), Zhang et al (2020), , Yoo (2018, 2020), Huang and Wang (2018), Shi et al (2023), Han (2022), andSong et al (2023), where restrictive assumptions regarding interconnection terms were imposed. The proposed control approach is distinct from the control approaches in Zhang and Wang (2022), Guo and Zhang (2021), , Choi and Yoo (2020), Huang and Wang (2018), Bounemeur and Chemachema (2021), Bounemeur et al (2018), Zhu and Han (2022), and Abdelhamid et al (2017 which experience an explosion of complexity.…”

“…A common drawback in the aforementioned papers is the explosion of complexity inherent to the recursive design with repetitive derivatives of the virtual control laws in the back-stepping design. To solve this problem, dynamic surface control (DSC), introduced in Swaroop et al (2000), was applied to uncertain interconnected nonlinear systems in pure feedback form in Chen and Wang (2016) and Zhang et al (2021), and for strict-feedback form with prescribed performance in Yang et al (2023), Jiang et al (2020), Choi and Yoo (2018) and Shi et al (2023). Stochastic and switched nonstrict-feedback forms were considered in Niu et al (2018).…”

“…The most common drawback of all approaches presented in the literature is the restrictive assumptions imposed on the interconnection terms that are considered with peculiar mathematical form and bounded with a known or unknown nonlinear functions to ease stability analysis (Choi and Yoo, 2018, 2020; Cui et al, 2023; Guo and Zhang, 2021; Huang and Wang, 2018; Ma et al, 2020; Niu et al, 2018; Shi et al, 2023; Song et al, 2023; Sun et al, 2019; Yang et al, 2023; Yang and He, 2018; Yoo, 2021; Zhang et al, 2020, 2021; Zhang and Wang, 2022; Zhao et al, 2021; Zhou et al, 2020; Zhu and Han, 2022). Last and not least, the tracking errors were proved to belong to a compact region around the origin with unknown and uncontrolled bounds.…”

“…In this study, the interconnection terms are only assumed reasonably bounded without any additional restrictive conditions. In contrast to Yang et al (2023), Cui et al (2023), Zhang and Wang (2022), Zhang et al (2021), Guo and Zhang (2021), Yoo (2021), Zhou et al (2020), Sun et al (2019), Ma et al (2020), Niu et al (2018), Zhang et al (2020), Sun et al (2019), Choi and Yoo (2018, 2020), Huang and Wang (2018), Shi et al (2023), Zhu and Han (2022), and Song et al (2023), where restrictive assumptions regarding interconnection terms were imposed.…”