MT‐filters‐based adaptive quantized dynamic surface control of stochastic nonstrict‐feedback nonlinear systems including input unmodeled dynamics

Abstract

AbstractIn 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.