Composite Observer-based Finite-time Command Filtered Adaptive Control for Full-state Constraints Nonlinear Systems

Abstract

In this paper, we look at a type of strict-feedback nonlinear systems that have neural adaptive finite-time command filtered control and full-state constraints. To begin with, indeterminate nonlinear functions are estimated using the NN. The system’s capacity to detect unmeasurable states and disturbances is done while using a composite observer, which significantly boosts the system’s anti-interference performance. Next, the issue of “complex explosion” that arose in the backstepping technique is resolved by adding a modified fractional-order filter. In order to address the full-state constraint issues, the BLF is also introduced. An adaptive finite-time tracking controller is created using Lyapunov’s theory analysis to make sure that all signals are confined to the finite time period. Lastly, a simulation study is conducted to evaluate the performance of the suggested control strategy.