State and Disturbance Estimation of Autonomous Surface Vehicles based on Nonlinear Cascade Extended State Observers

shijian Jiao, lu liu, Yongqi Yu, Anqing Wang, Dan Wang, Zhouhua Peng

Abstract: This paper is concerned with the state and disturbance estimation of autonomous surface vehicles (ASVs) in the presence of measurement noises. The velocity state information is unmeasured and the lumped disturbances consisting of internal model uncertainties and external environmental disturbance are unknown. Specifically, in the presence of measurement noises, a nonlinear cascade extended state observer (CESO) based on position-orientation measurement is presented to estimate unmeasured velocity and unknown lumped disturbances. The stability of the overall cascade system is validated through input-to-state stability theory and cascade theory. Simulation results are shown to confirm the effectiveness of the proposed observer scheme.

Submission Number: 253