L₁adaptive pitch control of an autonomous underwater vehicle

Abstract

Purpose – The purpose of this paper is to show the application of an L1 adaptive controller to control an autonomous underwater vehicle (AUV), considering realistic perturbations. Design/methodology/approach – In this paper, an L1 adaptive controller is proposed to control the pitch channel of an AUV, for the first time. Based on a six degree of freedom (6-DOF) nonlinear equations, an appropriate linear model considering real perturbations is derived for the pitch channel of AUV. Then L1 adaptive controller is applied on the model in the presence of the bounded disturbances and uncertainties. For this purpose, verified parameters of the REMUS AUV have been considered. Several simulations are performed in different operating conditions. Findings – The results confirm the quality of the proposed method in various situations. Furthermore, it is shown that the L1 adaptive controller is potential to contribute in practical applications of AUVs such as other autonomous systems. Originality/value – This is the first time that this method has been applied to an AUV.

Adaptive attitude controller design of autonomous underwater vehicle focus on decoupling

“…To increase the stability of numerical calculation and get a suitable reference model used in designing ℒ1 adaptive controller, hydrodynamic model should be simplified. In this paper, system identification is used instead of direct simplify [6] to acquire more design resources for the controller.…”

“…Set the steady velocity u to 1.5m/s. Let 0 = 1.5 and got the common simplified model as (6) in Yaw channel:…”

“…Then, ℒ1 adaptive controller is used to handle the coupling. Sarhadi [6] applied ℒ1 adaptive control on a simplified dynamic model of REMUS AUV. However, they lost a lot of nolinear characteristic in simplifying the model and didn't take the effect of coupling and the linearization error into consideration.…”

Pitch Channel Control of a REMUS AUV with Input Saturation and Coupling Disturbances

“…In contrast with MRAC sacrificing the performance of response speed during adaption, L1AC method accelerates the process of adaption and update reference model simultaneously. L1AC surpasses MRAC in control performance and parameters adjustment [21,26,27]. It is valuable to augment L1AC architecture using an AW compensator in autonomous vehicle control with input constraints, where Riccati equation is used to determine the gain vector for compensator's state feedback equation.…”