Adaptive neural network fixed‐time control of p$$ p $$‐normal systems with asymmetric output constraints. Issue 3 (19th April 2023)

• Record Type: Journal Article
• Authors: Meng, Yanting; Liu, Yang; Wang, Lijie; Xue, Hong
• Is Part Of: Asian journal of control. Volume 25:Issue 3(2023)
• Page Start: 2249
• Page End: 2263
• Publication Date: 2023-04-19
• Subjects: asymmetric output constraints -- fixed‐time control -- H∞$$ {H}_{\infty } $$ control -- nonlinear p$$ p $$‐normal systems; Automatic control -- Periodicals; Control theory -- Periodicals; 629.805
• DOI: 10.1002/asjc.3082
• Languages: English
• ISSNs: 1561-8625

Abstract

Abstract: This paper studies the problem of fixed‐time H ∞ $$ {H}_{\infty } $$ control for a class of nonlinear p $$ p $$ ‐normal systems with asymmetric output constraints and external disturbances. Compared with the existing fixed‐time control schemes, the smooth switching functions are designed to avoid the singularity problem. Then, a time‐varying nonlinear transformation function (NTF) is introduced, blue and a unified tool to deal with the symmetric, asymmetric, and even unconstrained problems simultaneously. Moreover, by adding blue one power integration technology, the H ∞ $$ {H}_{\infty } $$ controller is designed, which can inhibit the influence of external disturbances on the system. According to Lyapunov stability analysis, the presented fixed‐time H ∞ $$ {H}_{\infty } $$ blue control method ensures that all signals are bounded in a fixed time. In the end, two simulation examples demonstrate the effectiveness of the proposed scheme.