Design, implementation and testing of an intelligent knowledge-based system for the supervisory control of a hot rolling mill

This paper describes the design, implementation and testing of an intelligent knowledge-based supervisory control (IKBSC) system for a hot rolling mill process. A novel architecture is used to integrate an expert system with an existing supervisory control system and a new optimization methodology for scheduling the soaking pits in which the material is heated prior to rolling. The resulting IKBSC system was applied to an aluminium hot rolling mill process to improve the shape quality of low-gauge plate and to optimise the use of the soaking pits to reduce energy consumption. The results from the trials demonstrate the advantages to be gained from the IKBSC system that integrates knowledge contained within data, plant and human resources with existing model-based systems. (c) 2005 Elsevier Ltd. All rights reserved.

Assessment of the food control system in the State of Kuwait

National food control systems are a key element in the protection of consumers from unsafe foods and from other fraudulent practices. International guidance is available and provides a framework for enhancing national systems. However, it is recognized that before reaching decisions on the necessary improvements to a national system, an analysis is required of the current state of key elements in the present system. This paper provides such an analysis for the State of Kuwait. The fragmented nature of the food control system is described. Four key elements of the Kuwaiti system are analyzed: the legal framework, the administrative structures, the enforcement activity and the provision of education and training. It is noted that the country has a dependence on imported foods and that the present national food control system is largely based on an historic approach to food sampling at the point of import and is unsustainable. The paper recommends a more coordinated approach to food safety control in Kuwait with a significant increase in the use of risk analysis methods to target enforcement.

Dynamic recurrent neural network for system identification and control

A dynamic recurrent neural network (DRNN) that can be viewed as a generalisation of the Hopfield neural network is proposed to identify and control a class of control affine systems. In this approach, the identified network is used in the context of the differential geometric control to synthesise a state feedback that cancels the nonlinear terms of the plant yielding a linear plant which can then be controlled using a standard PID controller.

A helmet mounted display system with active gaze control for visual telepresence

The objective of a Visual Telepresence System is to provide the operator with a high fidelity image from a remote stereo camera pair linked to a pan/tilt device such that the operator may reorient the camera position by use of head movement. Systems such as these which utilise virtual reality style helmet mounted displays have a number of limitations. The geometry of the camera positions and of the displays is generally fixed and is most suitable only for viewing elements of a scene at a particular distance. To address such limitations, a prototype system has been developed where the geometry of the displays and cameras is dynamically controlled by the eye movement of the operator. This paper explores why it is necessary to actively adjust the display system as well as the cameras and justifies the use of mechanical adjustment of the displays as an alternative to adjustment by electronic or image processing methods. The electronic and mechanical design is described including optical arrangements and control algorithms. The performance and accuracy of the system is assessed with respect to eye movement.

Minimising the latency induced by consistency control, within a large scale multi-user distributed virtual reality system

Where users are interacting in a distributed virtual environment, the actions of each user must be observed by peers with sufficient consistency and within a limited delay so as not to be detrimental to the interaction. The consistency control issue may be split into three parts: update control; consistent enactment and evolution of events; and causal consistency. The delay in the presentation of events, termed latency, is primarily dependent on the network propagation delay and the consistency control algorithms. The latency induced by the consistency control algorithm, in particular causal ordering, is proportional to the number of participants. This paper describes how the effect of network delays may be reduced and introduces a scalable solution that provides sufficient consistency control while minimising its effect on latency. The principles described have been developed at Reading over the past five years. Similar principles are now emerging in the simulation community through the HLA standard. This paper attempts to validate the suggested principles within the schema of distributed simulation and virtual environments and to compare and contrast with those described by the HLA definition documents.

Dynamic integrated system optimization and parameter estimation for discrete time optimal control of nonlinear systems

An algorithm for solving nonlinear discrete time optimal control problems with model-reality differences is presented. The technique uses Dynamic Integrated System Optimization and Parameter Estimation (DISOPE), which achieves the correct optimal solution in spite of deficiencies in the mathematical model employed in the optimization procedure. A version of the algorithm with a linear-quadratic model-based problem, implemented in the C+ + programming language, is developed and applied to illustrative simulation examples. An analysis of the optimality and convergence properties of the algorithm is also presented.

Model predictive control using dynamic integrated system optimisation and parameter estimation (DISOPE)

DISOPE is a technique for solving optimal control problems where there are differences in structure and parameter values between reality and the model employed in the computations. The model reality differences can also allow for deliberate simplification of model characteristics and performance indices in order to facilitate the solution of the optimal control problem. The technique was developed originally in continuous time and later extended to discrete time. The main property of the procedure is that by iterating on appropriately modified model based problems the correct optimal solution is achieved in spite of the model-reality differences. Algorithms have been developed in both continuous and discrete time for a general nonlinear optimal control problem with terminal weighting, bounded controls and terminal constraints. The aim of this paper is to show how the DISOPE technique can aid receding horizon optimal control computation in nonlinear model predictive control.