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.
In this article, a fuzzy active disturbance rejection controller (FADRC) is proposed for autonomous underwater vehicle manipulator system (AUVMS). First, the AUVMS is separated into nine subsystems. Then, for each subsystem, dynamic uncertainties, hydrodynamic forces, unknown disturbance, and nonlinear coupling effects are lumped into a total disturbance. Next, a linear extended state observer (LESO) and linear feedback control law are designed to estimate and compensate the total disturbance. The convergence and estimation error of the LESO are analyzed here to validate its performance. Then considering the control output in real industry are always limited, a saturated proportional-derivative (PD) controller is proposed, and close-loop stability of the controller can be ensured. Given the fact that there are many parameters to be scheduled in practical industrial applications, an FADRC is proposed to determine the parameters of the proposed LESO and saturated PD controller.The given fuzzy rules of the parameters' change of the saturated PD controller and bandwidths of LESO can be used in other FADRCs. In order to verify the effectiveness of the proposed method, two tasks are chosen to test the performance of trajectory tracking and the capability of rejecting and attenuating the total disturbance. Simulation shows that the proposed FADRC can achieve better performance and consume less energy than classic fuzzy logic controllers and linear active disturbance rejection controllers.
<abstract><p>When a malfunction occurs in a marine main engine system, the impact of the anomaly will propagate through the system, affecting the performance of all relevant components in the system. The phenomenon of fault propagation in the system caused by induced factors can interfere with fault localization, making the latter a difficult task to solve. This paper aims at showing how the "characteristic curves method" is able to properly locate malfunctions also when more malfunctions appear simultaneously. To this end, starting from the working principle of each component of a real marine diesel engine system, comprehensive and reasonable thermal performance parameters are chosen to describe their characteristic curves and include them in a one-dimensional thermodynamic model. In particular, the model of a low-speed two stroke MAN 6S50 MC-C8.1 diesel engine is built using the AVL Boost software and obtaining errors lower than 5% between simulated values and test bench data. The behavior of the engine is simulated considering eight multi-fault concomitant phenomena. On this basis, the fault diagnosis method proposed in this paper is verified. The results show that this diagnosis method can effectively isolate the fault propagation phenomenon in the system and quantify the additional irreversibility caused by the Induced factors. The fault diagnosis index proposed in this paper can quickly locate the abnormal components.</p></abstract>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.