Strategy and control systems:a framework for analysis

This research explores the links between the strategies adopted by companies and the mechanisms used to control the organisation. This is not seen as a one way process with the control system following from the strategy but rather as an interactive process between the control systems, the environment and the business strategy. The main proposition of the research, derived from a review of the relevant literature, is that the dimensions of Business Pro-Activity and Environmental Change provide a plausible explanation of the reasons why companies need to adopt different strategies in order to be successful in different markets. A model is proposed which links these dimensions with the business strategy, organisational structure, strategic planning system and management control systems. The model is used as a framework for analysing four companies in order to further our understanding of these interactions and the mechanisms which act to both promote and resist change. Whilst it is not suggested that the model in its present form is a perfect instrument it has, during the course of this research, proved to be an appropriate framework for analysing the various mechanisms used by four companies to formulate and implement their strategies. The research reveals that these should not be viewed independently but as a balanced system.

Cost effectiveness of production control systems

This thesis deals with the problems associated with the planning and control of production, with particular reference to a small aluminium die casting company. The main problem areas were identified as: (a) A need to be able to forecast the customers demands upon the company's facilities. (b) A need to produce a manufacturing programme in which the output of the foundry (or die casting section) was balanced with the available capacity in the machine shop. (c) The need to ensure that the resultant system enabled the company's operating budget to have a reasonable chance of being achieved. At the commencement of the research work the major customers were members of the automobile industry and had their own system of forecasting, from which they issued manufacturing schedules to their component suppliers, The errors in the forecast were analysed and the distributions noted. Using these distributions the customer's forecast was capable of being modified to enable his final demand to be met with a known degree of confidence. Before a manufacturing programme could be developed the actual manufacturing system had to be reviewed and it was found that as with many small companies there was a remarkable lack of formal control and written data. Relevant data with regards to the component and the manufacturing process had therefore to be collected and analysed. The foundry process was fixed but the secondary machining operations were analysed by a technique similar to Component Flow Analysis and as a result the machines were arranged in a series of flow lines. A system of manual production control was proposed and for comparison, a local computer bureau was approached and a system proposed incorporating the production of additional management information. These systems are compared and the relative merits discussed and a proposal made for implementation.

Modelling manufacturing planning and control systems:the application of object-oriented principles and discrete-event simulation

Manufacturing planning and control systems are fundamental to the successful operations of a manufacturing organisation. 10 order to improve their business performance, significant investment is made by companies into planning and control systems; however, not all companies realise the benefits sought Many companies continue to suffer from high levels of inventory, shortages, obsolete parts, poor resource utilisation and poor delivery performance. This thesis argues that the fit between the planning and control system and the manufacturing organisation is a crucial element of success. The design of appropriate control systems is, therefore, important. The different approaches to the design of manufacturing planning and control systems are investigated. It is concluded that there is no provision within these design methodologies to properly assess the impact of a proposed design on the manufacturing facility. Consequently, an understanding of how a new (or modified) planning and control system will perform in the context of the complete manufacturing system is unlikely to be gained until after the system has been implemented and is running. There are many modelling techniques available, however discrete-event simulation is unique in its ability to model the complex dynamics inherent in manufacturing systems, of which the planning and control system is an integral component. The existing application of simulation to manufacturing control system issues is limited: although operational issues are addressed, application to the more fundamental design of control systems is rarely, if at all, considered. The lack of a suitable simulation-based modelling tool does not help matters. The requirements of a simulation tool capable of modelling a host of different planning and control systems is presented. It is argued that only through the application of object-oriented principles can these extensive requirements be achieved. This thesis reports on the development of an extensible class library called WBS/Control, which is based on object-oriented principles and discrete-event simulation. The functionality, both current and future, offered by WBS/Control means that different planning and control systems can be modelled: not only the more standard implementations but also hybrid systems and new designs. The flexibility implicit in the development of WBS/Control supports its application to design and operational issues. WBS/Control wholly integrates with an existing manufacturing simulator to provide a more complete modelling environment.

Design of a multi-processor control systems

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Comparison of a preferred and actual method of selecting and implementing computerised control systems in a manufacturing environment

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Enhancing the performance of intelligent control systems in the face of higher levels of complexity and uncertainty

Modern advances in technology have led to more complex manufacturing processes whose success centres on the ability to control these processes with a very high level of accuracy. Plant complexity inevitably leads to poor models that exhibit a high degree of parametric or functional uncertainty. The situation becomes even more complex if the plant to be controlled is characterised by a multivalued function or even if it exhibits a number of modes of behaviour during its operation. Since an intelligent controller is expected to operate and guarantee the best performance where complexity and uncertainty coexist and interact, control engineers and theorists have recently developed new control techniques under the framework of intelligent control to enhance the performance of the controller for more complex and uncertain plants. These techniques are based on incorporating model uncertainty. The newly developed control algorithms for incorporating model uncertainty are proven to give more accurate control results under uncertain conditions. In this paper, we survey some approaches that appear to be promising for enhancing the performance of intelligent control systems in the face of higher levels of complexity and uncertainty.

Distributed control of computer communication systems

Flow control in Computer Communication systems is generally a multi-layered structure, consisting of several mechanisms operating independently at different levels. Evaluation of the performance of networks in which different flow control mechanisms act simultaneously is an important area of research, and is examined in depth in this thesis. This thesis presents the modelling of a finite resource computer communication network equipped with three levels of flow control, based on closed queueing network theory. The flow control mechanisms considered are: end-to-end control of virtual circuits, network access control of external messages at the entry nodes and the hop level control between nodes. The model is solved by a heuristic technique, based on an equivalent reduced network and the heuristic extensions to the mean value analysis algorithm. The method has significant computational advantages, and overcomes the limitations of the exact methods. It can be used to solve large network models with finite buffers and many virtual circuits. The model and its heuristic solution are validated by simulation. The interaction between the three levels of flow control are investigated. A queueing model is developed for the admission delay on virtual circuits with end-to-end control, in which messages arrive from independent Poisson sources. The selection of optimum window limit is considered. Several advanced network access schemes are postulated to improve the network performance as well as that of selected traffic streams, and numerical results are presented. A model for the dynamic control of input traffic is developed. Based on Markov decision theory, an optimal control policy is formulated. Numerical results are given and throughput-delay performance is shown to be better with dynamic control than with static control.

Improved robust control of nonlinear stochastic systems using uncertain models

We introduce a technique for quantifying and then exploiting uncertainty in nonlinear stochastic control systems. The approach is suboptimal though robust and relies upon the approximation of the forward and inverse plant models by neural networks, which also estimate the intrinsic uncertainty. Sampling from the resulting Gaussian distributions of the inversion based neurocontroller allows us to introduce a control law which is demonstrably more robust than traditional adaptive controllers.

Probability distribution modelling to improve stability in nonlinear MIMO control

We consider the direct adaptive inverse control of nonlinear multivariable systems with different delays between every input-output pair. In direct adaptive inverse control, the inverse mapping is learned from examples of input-output pairs. This makes the obtained controller sub optimal, since the network may have to learn the response of the plant over a larger operational range than necessary. Moreover, in certain applications, the control problem can be redundant, implying that the inverse problem is ill posed. In this paper we propose a new algorithm which allows estimating and exploiting uncertainty in nonlinear multivariable control systems. This approach allows us to model strongly non-Gaussian distribution of control signals as well as processes with hysteresis. The proposed algorithm circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider.

A mixture density network approach to modelling and exploiting uncertainty in nonlinear control problems

In nonlinear and stochastic control problems, learning an efficient feed-forward controller is not amenable to conventional neurocontrol methods. For these approaches, estimating and then incorporating uncertainty in the controller and feed-forward models can produce more robust control results. Here, we introduce a novel inversion-based neurocontroller for solving control problems involving uncertain nonlinear systems which could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. Based on importance sampling from these distributions a novel robust inverse control approach is obtained. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider. A nonlinear multi-variable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non-Gaussian distributions of control signal as well as processes with hysteresis. © 2004 Elsevier Ltd. All rights reserved.

Experimental comparison of dynamic tracking performance of iGPS and laser tracker

External metrology systems are increasingly being integrated with traditional industrial articulated robots, especially in the aerospace industries, to improve their absolute accuracy for precision operations such as drilling, machining and jigless assembly. While currently most of the metrology assisted robotics control systems are limited in their position update rate, such that the robot has to be stopped in order to receive a metrology coordinate update, some recent efforts are addressed toward controlling robots using real-time metrology data. The indoor GPS is one of the metrology systems that may be used to provide real-time 6DOF data to a robot controller. Even if there is a noteworthy literature dealing with the evaluation of iGPS performance, there is, however, a lack of literature on how well the iGPS performs under dynamic conditions. This paper presents an experimental evaluation of the dynamic measurement performance of the iGPS, tracking the trajectories of an industrial robot. The same experiment is also repeated using a laser tracker. Besides the experiment results presented, this paper also proposes a novel method for dynamic repeatability comparisons of tracking instruments. © 2011 Springer-Verlag London Limited.