Synthesis of searchless adaptive system based on lyapunov function using state observer

Abstract

Searchless adaptive control system was studied and synthesized based on a Lyapunov function by a transient object under noisiness of measured values using state observer. The system adapts to the control object, regardless of whether the control object has static or dynamic characteristics. Additionally, the noise effect on the adaptive system behavior in the long run was investigated. The designed adaptive inference correction controller allows to adjust the control object inference and eliminates the static error of inference. State observer, which is based on the reference model restores the control object state vector. Even with significant object root deviations and random variables, adaptive system adapts the control action values and sets the control object inference to the desired value with the desired transition process time. The model was developed in the MATLAB/Simulink software package. Based on simulation, study of efficiency of the proposed model with different changes in the control object parameters was carried out.