Output feedback tracking of ships

Abstract-In this brief, we consider output feedback tracking of ships with position and orientation measurements only. Ship dynamics are highly nonlinear, and for tracking control, as opposed to dynamic positioning, these nonlinearities have to be taken into account in the control design. We propose an observer-controller scheme which takes into account the complete ship dynamics, including Coriolis and centripetal forces and nonlinear damping, and results in a semi-globally uniformly stable closed-loop system. Furthermore, a gain tuning procedure for the observer-controller scheme is developed. Experimental results are presented where the observer-controller scheme is implemented onboard a Froude scaled 1:70 model supply ship. The experimentally obtained results are compared with simulation results under ideal conditions and both support the theoretical results on semi-global exponential stability of the closed-loop system

Comments on ``Nonlinear Output Feedback Control of Dynamically Positioned Ships Using Vectorial observer Backstepping''

The decomposition of nonlinear output feedback control into an observer and a state feedback control is an open problem. A solution for dynamic positioning of ships has been proposed in the above paper (1) and by Grovlen and Fossen, where an observer-based backstepping method is used. This note points out that the observer design in the above-mentioned papers does not cover unstable ship dynamics and suggests a remedy for an extended class of ships. The proof for the nonlinear observer used in the design in the above-mentioned papers only applies to ships with stable sway yaw dynamics. In the above-mentioned papers an example concerning thruster assisted mooring of a tanker is given, which does not fulfill the needed stability properties, so an extension to this case is highly motivated. We propose a method to extend the results, under a detectability condition. This condition implies stable surge dynamics, which is a natural assumption for ships

Review of dynamic positioning control in maritime microgrid systems

For many offshore activities, including offshore oil and gas exploration and offshore wind farm construction, it is essential to keep the position and heading of the vessel stable. The dynamic positioning system is a progressive technology, which is extensively used in shipping and other maritime structures. To maintain the vessels or platforms from displacement, its thrusters are used automatically to control and stabilize the position and heading of vessels in sea state disturbances. The theory of dynamic positioning has been studied and developed in terms of control techniques to achieve greater accuracy and reduce ship movement caused by environmental disturbance for more than 30 years. This paper reviews the control strategies and architecture of the DPS in marine vessels. In addition, it suggests possible control principles and makes a comparison between the advantages and disadvantages of existing literature. Some details for future research on DP control challenges are discussed in this paper

Comments on "Nonlinear Output Feedback Control of Dynamically Positioned Ships Using Vectorial Observer Backstepping"

The decomposition of nonlinear output feedback control into an observer and a state feedback control is an open problem. A solution for dynamic positioning of ships has been proposed in [1] and [2], where an observer-based backstepping method is used. This note points out that the observer design in [1] and [2] does not cover unstable ship dynamics and suggests a remedy for an extended class of ships. The proof for the nonlinear observer used in the design in [1] and [2] only applies to ships with stable sway-yaw dynamics. In [1] and [2] an example concerning thruster assisted mooring of a tanker is given, which does not fulfill the needed stability properties, so an extension to this case is highly motivated. We propose a method to extend the results, under a detectability condition. This condition implies stable surge dynamics, which is a natural assumption for ships. I. Introduction We are using the problem formulation and notation from [1] motivated by the application to ship mode..

Ship steering control using feedforward neural networks

One significant problem in the design of ship steering control systems is that the dynamics of the vessel change with operating conditions such as the forward speed of the vessel, the depth of the water and loading conditions etc. Approaches considered in the past to overcome these difficulties include the use of self adaptive control systems which adjust the control characteristics on a continuous basis to suit the current operating conditions. Artificial neural networks have been receiving considerable attention in recent years and have been considered for a variety of applications where the characteristics of the controlled system change significantly with operating conditions or with time. Such networks have a configuration which remains fixed once the training phase is complete. The resulting controlled systems thus have more predictable characteristics than those which are found in many forms of traditional self-adaptive control systems. In particular, stability bounds can be investigated through simulation studies as with any other form of controller having fixed characteristics. Feedforward neural networks have enjoyed many successful applications in the field of systems and control. These networks include two major categories: multilayer perceptrons and radial basis function networks. In this thesis, we explore the applicability of both of these artificial neural network architectures for automatic steering of ships in a course changing mode of operation. The approach that has been adopted involves the training of a single artificial neural network to represent a series of conventional controllers for different operating conditions. The resulting network thus captures, in a nonlinear fashion, the essential characteristics of all of the conventional controllers. Most of the artificial neural network controllers developed in this thesis are trained with the data generated through simulation studies. However, experience is also gained of developing a neuro controller on the basis of real data gathered from an actual scale model of a supply ship. Another important aspect of this work is the applicability of local model networks for modelling the dynamics of a ship. Local model networks can be regarded as a generalized form of radial basis function networks and have already proved their worth in a number of applications involving the modelling of systems in which the dynamic characteristics can vary significantly with the system operating conditions. The work presented in this thesis indicates that these networks are highly suitable for modelling the dynamics of a ship