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This study investigates the properties of motor pools in the human motor control system. The simulations carried out as part of this study used two biologically inspired neuronal models to simulate networks with properties similar to those observed in the human motor system (Burke, 1991). The Synchronous neuronal model developed as part of this study explicitly models the input/output spike train and frequency relationship of each neuron. The motor pool simulations were carried out using the INSIGHT TOO simulation software developed as part of this study. INSIGHT TOO is a flexible neural design tool that allows the visual interactive design of network connectivity and has the power of a node specification language similar to that of BASIC that allows multi-layer, multi-model networks to be simulated. The simulations have shown that the motor pools are capable of reproducing commonly observed physiological properties during normal voluntary reaching movements. As a result of these findings a theoretical model of ballistic voluntary motor action was proposed called the Recruitment Model. The Recruitment model utilises the "recruitment" principle known to exist in motor pools and applies this distributed processing methodology to the higher levels of motor action to explain how complex structures similar to the human skeletal system might be controlled. A simple version of the Recruitment Model is simulated showing an animation of a running "stick man". This simulation demonstrates some of the principles necessary to solve problems relating to synergy formation.

Optimisation is an adaptiver process and it is widely applied in science and engineering from scheduling a manufacturing process to control of a spacecraft. Genetic algorithms are search and optimisation methods based on the mechanics of natural evolution. they provide promising results in non-linear and complex search problems. They have been proven to be parallel and global in nature. Genetic algorithms run slowly on sequential machines which is their mmmajor drawback. Most of the applications of genetic algorithm in engineering are in the area of design and schedule optimisation, where usually enough time is avialable to simulate the algorithm. The computer architecture is a main bottleneck since the sequential computation does not reflect the true spatial structure of the algorithm. There are a couple of parallel models and implementations available which improve the performance of these algorithms. The aim of this research is to develop a new model and/or to improve existing parallel models for real-time application of these methods in system identification and intelligent control. The desired features of this new model are: it should be independent of the optimisation problem, so it could be able to cope with a black box problem, it could be used in real-time applications, where the exact model of the system is unknown, and it should be implementable within the current technological framework. An extensive study of the current literasture on genetic algorithms has been carried out. A detailed review of the underlying theory of genetic algorithms has also been presented. A parallel model of genetic algorithm has been proposed and implemented on a transputer based system using ANSI C toolset for transputers. It has been tested for different strategies on the traditional suite of optimisation problems, ie DeJong's function and Deceptive functions. The results are compared using the performance measures proposed by DeJong. The performance and efficiency measures of the algorithm have been defined and worked out for the simulation results. The research has advanced the understanding of genetic algorithms as stochastic processes. A Markov chain based mathematical model has been developed. An informal study of convergence properties of the algorithm are presented from different points of view, ie time series, real analysis, Markov chains and metric topology. Gradient like information has been integrated into the genetic search in order to improve the performance and efficiency of the algorithm. A novel directional search method has been developed tested on the same set of problems and comapred using the same performance and efficiency measures as those reported in the recent publications. Unlike neural networks and fuzzy systems, genetic algorithms do not provide any general logic for system modelliung. Therefore system identification is achieved by using the fuzzy network for general logic and a genetic algorithm for parameter estimation giving as a result an evolving fuzzy network. This novel method has been applied to modelling of chaotic time series and it has been used to control a highly non-linear system, ie inverse pendulum. It is expected that with the advance of re-configurable electronics, evolutionary chips will be realised in the near future. They will play an important role in the development of genetic algorithms based control systems

The last decade has witnessed rapid developments in computer technology, which in return, has found widespread applications in manufacturing systems, communication networks, robots etc. Such systems are called Discrete Event Systems (DESs), in which properties such as non-determinism, conflict and parallelism are exhibited. As DESs become more complex, the need for an effective design tool and its implementation becomes more important. Supervisory control theory, based on finite state machines (FSM) and formal languages, is a well established framework for the study of DESs. In supervisory control, given a model of the system and the desired system behaviour specifications, the objective is to find a supervisor (controller) such that the controlled behaviour of the system does not contradict the specifications given and does not unnecessarily constrain the behaviour of the system. In general, the classes of specifications that have been considered within the supervisory control fall into two categories: the forbidden state problem, in which the control specifications are expressed as forbidden conditions that must be avoided, and the desired string problem, in which the control specifications are expressed as sequence of activities that must be provided. In supervisory control, there are some problems when using FSMs as an underlying modelling tool. Firstly, the number of states grows exponentially as the system becomes bigger. Secondly, FMSs lack from graphical visivalisation. To overcome these problems Petri nets have been considered as an alternative modelling tool for the analysis, design and implementation of such DESs, because of their easily understood graphical representation in addition to their well formed mathematical formalism. The thesis investigates the use of Petri nets in supervisory control. Both the forbidden state problem and the desired string problem are solved. In other words, this work presents systematic approaches to the synthesis of Petri-nets-based supervisors (controllers) for both the forbidden state problem and the desired string problem and introduces the details of supervisory design procedures. The supervisors obtained are the form of a net structure as oppose to supervisors given as a feedback fiinction. This means that a controlled model of the system can be constructed and analysed using the techniques regarding to Petri net models. In particular the thesis considers discrete manufacturing systems. The results obtained can be applied to high level control of manufacturing systems, where the role of the supervisor is to coordinate the control of machines, robots, etc. and to low-level control of manufacturing systems, where the role of the supervisor is to arrange low-level interactions between the control devices, such as motors, actuators, etc. An approach to the conversion from the supervisors to ladder logic diagrams (LLDs) for implementation on a programmable logic controller (PLC) is proposed. A discrete manufacturing system example is then considered. The aim of this is to illustrate the applicability, strengths and drawbacks of the design techniques proposed.

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