Active Disturbance Rejection Control for Uncertain Nonlinear Systems With Sporadic Measurements

This paper deals with the problem of active disturbance rejection control(ADRC)design for a class of uncertain nonlinear systems with sporadic measurements.A novel extended state observer(ESO)is designed in a cascade form consisting of a continuous time estimator,a continuous observation error predictor,and a reset compensator.The proposed ESO estimates not only the system state but also the total uncertainty,which may include the effects of the external perturbation,the parametric uncertainty,and the unknown nonlinear dynamics.Such a reset compensator,whose state is reset to zero whenever a new measurement arrives,is used to calibrate the predictor.Due to the cascade structure,the resulting error dynamics system is presented in a non-hybrid form,and accordingly,analyzed in a general sampled-data system framework.Based on the output of the ESO,a continuous ADRC law is then developed.The convergence of the resulting closed-loop system is proved under given conditions.Two numerical simulations demonstrate the effectiveness of the proposed control method.

Robustifying Dynamic Positioning of Crane Vessels for Heavy Lifting Operation

Construction crane vessels make use of dynamic positioning(DP)systems during the installation and removal of offshore structures to maintain the vessel’s position.Studies have reported cases of instability of DP systems during offshore operation caused by uncertainties,such as mooring forces.DP"robustification"for heavy lift operations,i.e.,handling such uncertainties systematically and with stability guarantees,is a long-standing challenge in DP design.A new DP method,composed by an observer and a controller,is proposed to address this challenge,with stability guarantees in the presence of uncertainties.We test the proposed method on an integrated cranevessel simulation environment,where the integration of several subsystems(winch dynamics,crane forces,thruster dynamics,fuel injection system etc.)allow a realistic validation under a wide set of uncertainties.