Adaptive decentralized finite‐time tracking control for uncertain interconnected nonlinear systems with input quantization. (16th March 2021)

• Record Type: Journal Article
• Authors: Sun, Haibin; Hou, Linlin
• Is Part Of: International journal of robust and nonlinear control. Volume 31:Number 10(2021)
• Page Start: 4491
• Page End: 4510
• Publication Date: 2021-03-16
• Subjects: adaptive control -- decentralized control -- finite‐time control -- input quantization -- interconnected nonlinear system -- neural network; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.5487
• Languages: English
• ISSNs: 1049-8923

Abstract

Abstract: Compared with the previous references, very few results are available to study control scheme directly for interconnected nonlinear systems (INSs) with strongly interconnected terms described by unknown state‐dependent‐nonlinear functions and input quantization in the finite‐time control literature. In this article, an adaptive decentralized finite‐time tracking control (ADFTTC) algorithm is developed to address the system output tracking problem for a class of INSs with input quantization and strongly interconnected terms. To reduce the computation complexity, the neural network technique is employed to reconstruct the derivative of virtual control laws in each step. By employing adding a power integrator technique, an adaptive decentralized finite‐time tracking controller is constructed, which guarantees all signals in the closed‐loop systems converge to a neighborhood of the origin. Moreover, the developed controller avoids the "singularity" and "explosion of complexity" problems in the recursive design scheme. An example of an inverted pendulum confirms the ADFTTC scheme.