Adaptive steering of ships-A model reference approach

Authors:
J. Van Amerongen
Affiliations:
Control Laboratory, Electrical Engineering Department, Delft University of Technology, 2600 GA Delft, The Netherlands
Venue:
Automatica (Journal of IFAC)
Year:
1984

Citing 5
Cited 3

Adaptive Control: The Model Reference Approach

Adaptive Control: The Model Reference Approach
Model reference adaptive autopilots for ships

Automatica (Journal of IFAC)
A survey of model reference adaptive techniques-Theory and applications

Automatica (Journal of IFAC)
Paper: Adaptive autopilots for tankers

Automatica (Journal of IFAC)
Paper: A new ship's auto pilot design through a stochastic model

Automatica (Journal of IFAC)

Model reference adaptive robust fuzzy control for ship steering autopilot with uncertain nonlinear systems

Applied Soft Computing
Adaptive steering control for uncertain ship dynamics and stability analysis

Automatica (Journal of IFAC)
Adaptive dynamic surface control with Nussbaum gain for course-keeping of ships

Engineering Applications of Artificial Intelligence

Quantified Score

Hi-index	22.15

Visualization

Abstract

This paper describes the application of model reference adaptive control (MRAS) to automatic steering of ships. The main advantages in this case are the simplified controller adjustment which yields safer operation and the decreased fuel cost. After discussion of the mathematical models of process and disturbances, criteria for optimal steering are defined. Algorithms are given for direct adaptation of the controller gains, applicable after setpoint changes, as well as for identification and adaptive state estimation, to be used when the input is constant. Solutions for applying MRAS to a certain class of nonlinear systems are dealt with. Full-scale trials at sea and tests with a scale model in a towing tank are described. It is shown that the autopilot designed indeed has the desired properties. Fuel savings up to 5% in comparison to conventional PID control are demonstrated. These savings are mainly possible because of the adaptive state estimator.