Adaptive estimation and control for uncertain nonlinear systems and full actuation control

We study adaptive control for a family of nonlinear systems, involving unknown and uncertain parameters. The proposed control law estimates the system parameters adaptively and stabilizes the closed-loop system asymptotically for the initial state over any given bounded set of the state-space. Moreover, reconstruction filters are designed to obtain error residue signals and to enable the use of the least-squares algorithm for estimating the parameters, in order to achieve the convergence based on the persistent excitation condition and asymptotic linearization. The proposed methods are applicable to full actuation and under actuation control systems. Simulation studies are carried out for a pendulum system and for a third-order vehicle model, as well as control of vehicle platoons, validating the theoretical results presented in this paper.

Automated summary

This paper studies an adaptive control method for nonlinear systems with unknown and uncertain parameters. The proposed method estimates the system parameters adaptively, stabilizes the closed-loop system, and achieves asymptotic control over a given bounded set of the state-space. Reconstruction filters are designed to obtain error residue signals and enable parameter estimation using the least-squares algorithm. The proposed method is applicable to both full actuation and under actuation control systems, and its effectiveness is validated through simulation experiments.