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Commercially, the Czochralski process is the most commonly applied method for production of monocrystalline silicon for semiconductor and solar cell applications. The process has traditionally been controlled using nested single-loop controllers. Due to the complex and nonlinear nature of the Cz process, this paper considers model-based control of the process with application of nonlinear model predictive controllers.

The SUBLOAD system for offshore loading of oil directly from a seafloor terminal by a flexible riser deployed from a dynamically positioned shuttle tanker is briefly described.

The process control system described in this paper, is a very flexible system equipped with a range of computer-aided configuration tools. These are tools for the configuration and reconfiguration of control loops, computing blocks, logic control modules, i/o connections or graphics, and presentation elements such as reports, trend curves, flowsheets, free graphical pictures etc., while the system is operational and in control. Documentation of system topology, system parametrization and the i/o configuration/connection etc., can be extracted from the system at any time. The system can also be configured for training and simulation applications as well as for process control. The paper describes the system hardware architecture, the distributed database and the configuration and operation tools.

This paper describes a computer-based, dynamic positioning system for floating vessels. The system is based on a detailed mathematical model of vessel motion in response to forces from thrusters, wind, waves and water current. The system uses a Kalman filter for optimal estimation of vessel motions and environmental forces from wind, waves and current. The control system is based on feedback from the motion variables where the oscillatory, wave-induced component is removed by the estimator. Feedback from the water current estimate provides the integral action of the system and feed forward from the wind force estimates are implemented. Simulation results and recordings from actual operation of the system indicate an excellent system performance.

A splitting algorithm for fast and slow modes of ocean hydrodynamics is presented. The purpose of the splitting is to reduce the large amount of computational work needed for simulating long real-time periods. The essential point of the splitting is that the external gravity wave terms are extracted from the fully three-dimensional equations of horizontal motion, allowing the reduced equations to be integrated with a larger time step than the original model. The fast external gravity waves are traced by a depth integrated system which is weakly coupled to the reduced three-dimensional momentum equations. The split model shows a radical decrease in computational time and the accuracy is of the same order as in the non-split case.

This paper addresses the blow-up problem associated with the parameter estimation part of an adaptive controller. A partial solution to the problem has been devised by the introduction of a variable forgetting factor. However, this does not eliminate the blow-up possibility. This is shown by simulation experiments on two different models.

This paper is concerned with analysis and design of an adaptive autopilot for ships. The design is based on a low and high frequency model of the vessel motion adequate to ship steering. The low frequency model describes the vessel response to rudder control and slowly varying environmental forces. The high frequency model represents the wave induced oscillatory part of the yaw motion. The models are used in a Kalman filter and the rudder control is computed from linear quadratic theory based on the low frequency part of the vector. This yields a very effective filtering of the wave component of the yaw motion. Proper operation of this filter/controller structure requires knowledge of the vessel model parameters and the dominating wave frequency. The vessel parameters are estimated on line by a recursive prediction error method. In order to reduce the computing requirements, the state estimator is operated using scheduled gains. This results in an easy and robust design. The convergence properties are investigated by using the method of Ljung. The performance is confirmed by simulation experiments.

This paper describes a concept for high level configuration and parametrization of process simulators. The simulator is an integral part of a distributed process control system. This system can be switched from a control mode to a simulation mode where i/o signals are generated by/supplied to a process simulator when the system is set in simulation mode. The system is supported by a set of configuration tools.