Research on disturbance rejection motion control method of USV for UUV recovery

Abstract

The recovery of unmanned underwater vehicle (UUV) by unmanned surface vehicle (USV) has the characteristics of autonomy, safety, and efficiency. Taking the recovery of UUV by USV as the engineering background, this paper studies the guidance and anti-interference motion control of USV in the recovery process.Aiming at the problem of dynamic guidance when recovering UUV, the USV guidance strategy for UUV recovery is studied. Fuzzy guidance is introduced as the dynamic terminal guidance method, and a layered guidance strategy combining classical guidance and fuzzy guidance is proposed. On the basis of the theory of compact form dynamic linearization-based model-free adaptive control (CFDL-MFAC), the motion control of USV in the process of recovering UUV under the influence of model perturbation, external interference, and other uncertainties is studied. Theoretical analysis and experimental results show that there is a contradiction in the matching of dynamic change speed between the USV heading control subsystem and CFDL-MFAC. By introducing the difference item into the standard control criterion to weaken the integral effect in the heading control subsystem of USV, a difference-type compact format model-free adaptive control method (DCFDL-MFAC) is proposed, and the stability of DCFDL-MFAC method is proved theoretically. The effectiveness and practicability of the proposed method are verified by simulation tests and field tests of "Dolphin IB" small USV.

Event-Triggered Path Following Robust Control of Underactuated Unmanned Surface Vehicles with Unknown Model Nonlinearity and Disturbances

“…From the perspective of control subsystem, there are many approaches applied to the USV control field and that have achieved good control results. The approaches include proportional integration differentiation (PID) [15][16][17], trajectory linearization control (TLC) [18][19][20], sliding mode control (SMC) [21][22][23], backstepping [9,[24][25][26], and intelligent control [27][28][29][30][31].…”

“…Fuzzy logic control converts expert knowledge into fuzzy rules, which can effectively deal with the impact of model uncertainty and interference in the path following control of USVs [27]. In addition, NN can be used to approximate the uncertainty and external interference terms of the USV model, so as to improve the anti-interference ability and robustness of the controller [28,29]. In recent years, machine learning theory has developed rapidly, and reinforcement learning has been widely applied in the field of USV control [30,31].…”