MT‐filters‐based adaptive quantized dynamic surface control of stochastic nonstrict‐feedback nonlinear systems including input unmodeled dynamics. (18th August 2021)

• Record Type: Journal Article
• Authors: Zhang, Tianping; Chen, Penghao
• Other Names: Sename Olivier guestEditor.; Rotondo Damiano guestEditor.
• Is Part Of: International journal of robust and nonlinear control. Volume 31:Number 17(2021)
• Page Start: 8632
• Page End: 8657
• Publication Date: 2021-08-18
• Subjects: dynamic surface control -- input unmodeled dynamics -- MT‐filters -- quantization input -- stochastic nonstrict‐feedback nonlinear systems -- time‐varying output constraints; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.5730
• Languages: English
• ISSNs: 1049-8923

Abstract

Abstract: In this article, the issue of stochastic adaptive dynamic surface control (DSC) is discussed for stochastic nonstrict‐feedback constrained nonlinear systems with input quantization and input unmodeled dynamics. Linearly parametrized neural networks are used to estimate unknown continuous functions. With the help of MT‐filters, the unmeasurable system states are observed. An auxiliary signal constructed by the property of unmodeled dynamics is utilized to deal with the dynamic uncertain terms. Input‐quantized actuator is considered to possess quantization and unmodeled dynamics. Using the hyperbolic tangent function as invertible change, the output constrained stochastic nonstrict‐feedback system is transformed into a novel stochastic nonstrict‐feedback system without restrictions. Based on quantized DSC technology and the property of Gaussian function, adaptive neural control is developed for the transformed stochastic nonstrict‐feedback system. The output abides by stochastic constraints in probability. By the Lyapunov synthesis approach, all signals in the whole system are proved to be semi‐global uniform ultimate bounded in probability. The restriction condition is not triggered. The obtained theoretical findings are verified by two numerical examples.