An Iterative Learning Extended-State Observer-Based Fuzzy Fault-Tolerant Control Approach for AUVs

Abstract

In this article, an iterative learning algorithm based on extended state observer (ESO) is proposed to deal with the propeller failure of an underwater vehicle. In this control scheme, the nonlinear feedback mechanism of ESO is transplanted to iterative learning processes; that is, the nonlinear function of the current output residual is used to adjust the value of the virtual fault in the next iteration. Additionally, to ensure the safety of the control torque, a saturated proportional-derivative (PD) controller is proposed. Finally, to achieve online parameter self-tuning, a fuzzy logic controller is employed in this control scheme to fuzzify the parameters of a saturated PD controller and ESO. The obtained results show the favorable speed of tracking convergence and the high precision of fault estimation.

Fuzzy linear extended states observer‐based iteration learning fault‐tolerant control for autonomous underwater vehicle trajectory‐tracking system

“…For simplicity, in this work only the motion in the local level plane is considered to illustrate and test the proposed scheme, which can be seen in Figure 2, where O ‐ XY is the inertial coordinates, o ‐ xy is body‐fixed coordinates. The 3‐DOF dynamic and kinematic equation of motion of an AUV can be written in the following form [17]: $$$$\begin{equation} {{\bm \mu \dot{\bm v}}} + {{\bm C}}({{\bm v}}){{\bm v}} + {{\bm D}}({{\bm v}}){{\bm v}} + {{\bm g}}({{\bm \eta }}) = {{\bm \tau }} + {{{\bm \tau }}_{\rm{d}}}\end{equation}$$$$ $$$$\begin{equation} {{\dot{\bm \eta }}} = {{\bm R}}(\psi ){{\bm v}}\end{equation}$$$$where η = [ x , y , ψ ] T denotes lateral motion, longitudinal and yaw angle of AUV and v = [ u , v , r ] T is made up of velocities in the three dimension; M = diag ( M u , M v , M r ) is inertial matrix with added mass; D ( v ) = diag( X u , Y v , N r ) + diag( D u | u |, D v | v |, D r | r |) is the damping matrix; restoring force is given by g ( η ); τ denotes generalized control input; τ d is vector of unknown disturbance; C ( v ) v represents the Coriolis and centripetal forces, and the coefficient matrix can be written as $$$$\begin{equation} {{\bm C}}({{\bm v}}) = \left[ { \def\eqcellsep{&}\begin{array}{@{}*{3}{c}@{}} 0&\quad 0&\quad { - {M_v}v}\\[3pt] 0&\quad 0&\quad {{M_u}u}\\[3pt] {{M_v}v}&\quad { - {M_u}u}&\quad 0 \end{array} } \right] \end{equation}$$$$…”

“…To test the performance of the proposed control scheme, we choose the classic P‐type FTC control scheme and our previous work [17] to make comparison with the proposed control scheme. It is defined that the faults occur at t = 4.5 s. The iteration learning residual threshold is chosen as ε = 0.001.…”

Thruster Fault Diagnostics and Fault Tolerant Control for Autonomous Underwater Vehicle with Ocean Currents

“…In order to solve the problem of thruster fault tolerant control for AUV, a fault tolerant control method is proposed in the light of the sliding mode theory, the adaptive law is developed for the proposed controller to mitigate the chattering phenomenon [13]. In order to further improve the performance of the fault tolerant control, some intelligent methods are investigated [4,14,15]. An iterative learning algorithm is proposed to process the propeller failure for AUV based on an extended state observer, a fuzzy logic controller is introduced to deal with the fuzzification of the parameters of a saturated proportional-derivative controller and extended state observer [14].…”

“…In order to further improve the performance of the fault tolerant control, some intelligent methods are investigated [4,14,15]. An iterative learning algorithm is proposed to process the propeller failure for AUV based on an extended state observer, a fuzzy logic controller is introduced to deal with the fuzzification of the parameters of a saturated proportional-derivative controller and extended state observer [14]. Combined with the backstepping method, a single critic network based on adaptive dynamic programming is used to deal with the AUV fault tolerant control.…”

Model and Data-Driven Combination: A Fault Diagnosis and Localization Method for Unknown Fault Size of Quadrotor UAV Actuator Based on Extended State Observer and Deep Forest

“…ESO can track the motion state of the system quickly and accurately with a small number of parameters [ 16 ]. Li et al [ 17 ] has achieved good results by applying ESO to autonomous underwater vehicle actuator fault diagnosis. DF is an ensemble learning method based on decision tree proposed by Zhou et al [ 18 ].…”