A self-tuning robust observer for marine surface vessels

The current work aims at developing a nonlinear, self-tuning, robust observer to estimate state variables pertinent to the control of under-actuated marine surface vessels. The state estimator combines the advantages of the variable structure systems theory with those of the self-tuning fuzzy logic algorithm. It does not require an exact knowledge of the system dynamics or the construction of a rule-based expert fuzzy inference system. However, the upper bounds of both modeling imprecision and external disturbances must be known. The convergence of the estimation process is guaranteed by forcing the tuning parameters to satisfy inequalities stemming from the sliding conditions. The observer has been implemented herein to provide accurate estimate of the state variables that are needed by an integrated guidance and control system for the autonomous operation of an under-actuated marine surface vessel. The observer along with the integrated guidance and control system have been tested on a six degree-of-freedom ship model that considers numerous uncertainties associated with wave excitation, nonlinear restoring forces, retardation forces, sea-current and wind resistive loads. The theoretical results prove that the proposed self-tuning observer can produce accurate estimates of the state variables in the presence of significant modeling imprecision and environmental disturbances. In addition, they illustrate the use of estimated state variables in the guidance and control system with minimal impact on the close-loop performance of the marine vessel.