Ship Steering Control System Research Articles

Reliability Distribution of a Ship Steering Control System Based on Scoring Method

In order to improve the reliability of the ship's steering control system, the structure and function of a new ship steering control system are taken as objects, the basic reliability model of the system is established, the working characteristics of the system are analyzed and the scoring distribution method for the ship's steering control system is proposed, and the reliability index of the unit in the system is distributed. The reliability index of each unit is obtained, which provides the reliability basis for further optimizing the design of the steering control system.

General fuzzified CMAC-based model reference adaptive control for ship steering using float-encoding genetic algorithm

The application of CMAC control techniques for improving the performance of ship steering is discussed. Aiming at a conventional Cerebellar Model Articulation Controller (CMAC), combining CMAC-addressing schemes with a fuzzy logic idea, a general fuzzified CMAC (GFCMAC) is proposed, in which the fuzzy membership functions are utilized as the receptive field functions. Incorporating GFCMAC into a control system, a GFCMAC-based model reference adaptive controller is described, whose required teacher signals are explicitly constructed by a reference model. In order to improve the control performance, using a float-encoding genetic algorithm (FGA) to optimize parameters, an FGA-GFCMAC controller is discussed. Applying the FGA-GFCMAC controller to a ship steering control system, the simulation results show that the ship’s course can be properly controlled when changing wind and waves exist.

Automatic steering of ships using neural networks

Ship steering control system design presents challenges because the dynamic properties of the vessel itself vary significantly. The use of an artificial neural network as a controller which incorporates the properties of a series of conventional controllers designed for different operating conditions could provide an alternative to adaptive control or gain scheduling in this application. Local model network methods could also provide a basis for efficient modelling of the vessel over a range of operating conditions. The paper describes an investigation of radial basis function networks for ship steering control and of local model networks for representation of ship dynamics. Performance is demonstrated by a series of simulation studies. Copyright © 1999 John Wiley & Sons, Ltd.

Autopilot Design by the Method of Simultaneous Stabilization and Parameter Optimization

Abstract The paper presents a synthesis of a nonlinear ship steering control system through application of the simultaneous stabilization and parametric optimization methods. The ship steering control system is closed through the gyrocompass and autopilot. The loop consists of an autopilot, the steering gear subsystem, and a ship. For linearization of the nonlinear steering gear subsystem the procedure of quasi - linearization is used. The steering gear linearized model structure and initial parameters are determined by means of frequency characteristics of the steering gear subsystem, while the time response is utilized for the fine adjustment of parameters. The dynamics of the ship, treated as a linear part of the system, varies depending on conditions (trim, depth etc.) to which the ship is subjected, and can be described by a set of transfer functions. The linearized steering gear model together with a set of the ship linear models produce the nonlinear ship steering control system model with variable parameters. The obtained model consists of a set of transfer functions describing the complete system dynamics in open loop. After obtaining such a model of the system, an autopilot design is taking place by applying the simultaneous stabilization procedure. The obtained regulator stabilizes the system under given operating conditions, respectively, it stabilizes the set of transfer functions. The procedure of parametric optimization of the nonlinear system with variable parameters is used to improve the system dynamics, whereas through the simultaneous stabilization procedure only the system stability conditions are checked. The procedure is verified, and the obtained results confirm the advantage of the autopilot obtained in such a manner.