Virtual tracking control of underwater vehicles based on error injection and adaptive gain

Abstract

An improved virtual tracking control scheme is proposed based on error injection and adaptive gain for underwater vehicles in the presence of a large initial tracking error and external disturbances. To relieve the effect caused by a large initial tracking error, the developed control scheme is achieved based on two closed‐loop systems. Specifically, a virtual closed‐loop system is constructed based on an approximate dynamic model of an underwater vehicle, while an actual closed‐loop system is built with a real underwater vehicle. Firstly, in order to improve the tracking precision of the virtual tracking control scheme, an auxiliary variable produced by a first‐order filter is injected into a virtual tracking error in the virtual closed‐loop system. And then, the virtual trajectory provided by the virtual closed‐loop system is followed by the actual closed‐loop system. In the actual closed‐loop system, a modified sliding mode surface is designed to achieve the finite‐time stability, while the control gains can be on‐line adjusted based on the tracking performance. Finally, the effectiveness and feasibility of the proposed control scheme are demonstrated by case studies on an underwater vehicle subject to different external disturbances.

Adaptive Backstepping Sliding Mode Tracking Control For Autonomous Underwater Vehicles With Input Quantization

“…In the past decades, a host of remarkable control methods have been developed for trajectory tracking control of AUVs, such as backstepping control (BC), [7] adaptive control, [8][9][10] sliding mode control (SMC), [11][12][13] model predictive control (MPC), [14][15][16] fuzzy control, [17] neural networks control, [18] etc. Shen et al investigated the nonlinear model predictive control of AUVs, where a distributed implementation strategy was proposed to alleviate the computational burden by decomposing the original optimization problems into smaller size subproblems.…”

“…In practice, the dynamics model of the AUV is not exactly known, so Equation ( 2) can be written as (10) where ΔM, ΔC(𝝂) and ΔD(𝝂) are unknown dynamic uncertainties.…”

“…In the past decades, a host of remarkable control methods have been developed for trajectory tracking control of AUVs, such as backstepping control (BC), [ 7 ] adaptive control, [ 8–10 ] sliding mode control (SMC), [ 11–13 ] model predictive control (MPC), [ 14–16 ] fuzzy control, [ 17 ] neural networks control, [ 18 ] etc. Shen et al.…”