Position control for an autonomous underwater vehicle under noisy conditions

Abstract

Position tracking of an autonomous underwater vehicle is not trivial as its accuracy is affected by process noise, measurement noise, and uncertain hydrodynamic parameters. Thus, in this article, we studied the position control of an autonomous underwater vehicle that operates under largely unbounded system uncertainties and large noise levels and proposed a method that reduces the interference and thereby enhances the overall autonomous underwater vehicle position estimation. Our technique extended the ensemble Kalman filter by combining it with a time-delay estimator to compensate against extended uncertainty of the system dynamics which cannot be solved by the traditional ensemble Kalman filter. Our synthetic simulations demonstrated the effectiveness of the proposed controller, highlighting its appealing position-control accuracy under simultaneous noise and uncertain hydrodynamics parameters. In addition, our simulation results showed that the proposed controller outperforms the conventional time-delay controller by a percentage range of approximately 30.8%–92.6% in terms of root-mean-square error and requires on average less than 88.2% calculation time than the conventional model predictive control.

Composite adaptive control of submarine and parameter identification by integral regressor excitation

“…In this section, we present the results obtained by simulating the closed-loop system including the submarine model equations ( 1) and ( 2), the control law equation (18), and the simplified composite adaptation law equation (54). The nonlinear hydrodynamic terms included in the model for simulation are Z n (w, _ u) = 1:2 3 10 À4 wjwj + 1:6 3 10 À4 _ uj _ uj and M n (w, _ u) = 2:4 3 10 À 4w jwj + 3:0 3 10 À4 _ uj _ uj.…”

“…We note that each element of the vector x = (u, _ u, w, y T , _ y T , ĥT c ) T 2 R 21 is bounded. The functions _ ĥc , u c , and ( _ w, € u),given in equations ( 51), (18), and (4) are explicit functions of the components of x. Differentiating each term of _ W in equation ( 53), we find that € W is also a function of the components of x. Therefore, € W is bounded.…”

“…Theorem 1. Consider the closed-loop system including the disturbance-free (d = 0) submarine model equations ( 1) and ( 2), the control law equation (18), and the composite adaptation law equation (51). Then, if B remains invertible during the adaptation process, the tracking error ỹ will converge to zero.…”

“…Specifically for AUVs, researchers have designed a variety of linear, nonlinear, and robust control systems. 1,2,12–16 The authors used time-delay estimation for the control of AUVs 17,18 in uncertain and noisy environments. A descriptor time-delay AUV model has been considered for the design of a robust H ∞ control system.…”

Observer-based Takagi–Sugeno fuzzy sampled-data dynamic positioning controller design for unmanned surface vehicle with external disturbances and actuator faults

“…Because it can realize environment perception, transportation, exploration of ocean resources, target detection and intricate tasks, USV plays an important role in both civil and military applications. Therefore, the study on USV has received a considerable attention, which includes trajectory tracking control, 1,2 steering control, 3 dynamic positioning control 4 and so on. Due to complex nonlinearity features of USV, many effective control strategies are proposed to achieve the above objectives, for instance, fuzzy control, 5,6 adaptive control 7 and neural network control.…”