Decentralized adaptive quantized feedback tracking of a class of uncertain interconnected lower-triangular nonlinear systems

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

“…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. This issue is avoided entirely in the proposed approach by utilizing a nonrecursive design technique.…”

“…Specifically, adaptive back-stepping has emerged as one of the most popular design techniques for uncertain lower-triangular nonlinear systems. In the literature, large-scale interconnected strict-feedback systems were considered with unknown control directions in Zhang and Wang (2022) and Huang and Wang (2018), and with quantized control in Sun et al (2019), Choi and Yoo (2020) and Song et al (2023), whereas in Guo and Zhang (2021), the output is considered only available at the sampled instants. However, the back-stepping technique was, successfully, extended to deal with the general control problem of uncertain interconnected nonlinear systems in pure feedback form with prescribed performance in Zhou et al (2020), and with event-triggered control in Wang et al (2021), Choi and Yoo (2018), and Zhu and Han (2022).…”

“…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.…”

Quantized-State-Feedback-Based Neural Control for a Class of Switched Nonlinear Systems With Unknown Control Directions

“…To overcome this difficulty, an adaptive backstepping control method was presented to stabilize systems with matched unknown parameters and known nonlinear functions [43]. By employing the command-filtered backstepping technique [44], lowertriangular nonlinear systems with state quantization were considered to design adaptive trackers for dealing with various control problems such as unmatched parametric uncertainties [45], state time delays [46], decentralization [47], and switched nonlinearities [48]. Although these results [45]- [48] provided several successful solutions to the state quantization problems of uncertain lower-triangular nonlinear systems, there is scope for further improvement in the following aspects.…”