Adaptive fuzzy decentralized control for nonlinear large-scale systems based on high-gain observer

Abstract

An adaptive fuzzy decentralized backstepping output-feedback control approach is proposed for a class of nonlinear large-scale systems with completely unknown functions, the interconnections mismatched in control inputs, and without the measurements of the states. Fuzzy logic systems are employed to approximate the unknown nonlinear functions, and an adaptive high-gain observer is developed to estimate the unmeasured states. Using the designed high-gain observer, and combining the fuzzy adaptive control theory with backstepping approach, an adaptive fuzzy decentralized backstepping output-feedback control scheme is developed. It is proved that the proposed control approach can guarantee that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded (SUUB), and that the observer errors and the tracking errors converge to a small neighborhood of the origin by appropriate choice of the design parameters. Finally, a simulation example is provided to show the effectiveness of the proposed approach.Keywords nonlinear large-scale systems, fuzzy logic system, high-gain state observer, adaptive decentralized control, stability analysis CitationTong S C, Ren C E, Li Y M. Adaptive fuzzy decentralized control for nonlinear large-scale systems based on high-gain observer.

Robust estimation‐free decentralized prescribed performance control of nonaffine nonlinear large‐scale systems

“…Recently, large‐scale systems (also referred to as interconnected systems in some literature) have received considerable attention due to their universal descriptions for many practical systems such as power system, space robot system, spacecraft system, multi‐agent system and mechanical engineering system . Wherein, distributed control and decentralized control as 2 common effective approaches are widely used because they can alleviate the computational burden brought by a centralized control scheme for large‐scale systems.…”

“…Wherein, distributed control and decentralized control as 2 common effective approaches are widely used because they can alleviate the computational burden brought by a centralized control scheme for large‐scale systems. For the former, local relative exchange state information among subsystems is needed in the control protocol, whereas the latter uses only locally available state information of each subsystem without requirement of any state information of other subsystems . Generally, dencentralized control is more practical because it is ubiquitous that many isolated subsystems like large‐scale space robot system work without too much state information exchange to finish one typical task.…”

High-Gain Observer-Based Sliding-Mode Dynamic Surface Control for Particleboard Glue Mixing and Dosing System

“…To solve these problems, some scholars have proposed a high-gain observer (HGO) control strategy [23][24][25][26][27]. In Reference [23], an adaptive HGO was established to estimate the unmeasured states for the output-feedback control problem of a class of nonlinear large-scale system. In Reference [24], a decentralized control algorithm was developed based on HGO theory and fuzzy adaptive control algorithm for second-order uncertain nonlinear multiagent systems.…”

Neural network-based adaptive control for a class of chemical reactor systems with non-symmetric dead-zone

“…Specially, in system with time-varying input delays or fault [1][2][3][4], with the help of the Takagi-Sugeno (T-S) fuzzy model, a pseudo-predictor feedback approach method, or even the augmented sliding mode observer approach method, appropriate controllers are established, then the stability of the systems are proved [5][6][7][8][9][10][11]53]. In comparison with the intelligent control without adaptive, with the help of approximators, the adaptive control could deal with the uncertain of the systems better [12][13][14][15]. With the certain conditions, several approximation methods have been proven that, such as fuzzy systems, polynomials [16][17], splines [18] and NNs [19][20][21][22] have function approximation abilities and have been constantly used as function approximators.…”