Summary of all THREE
submitted Cluster Proposals (including
Research Title, Partners, Industrial Collaborators, Project
Objectives & Summary)
1. FIRST (‘small-scale’)
Cluster Proposal:
1.1 Title: Modular
Learning and Co-ordination in Complex Systems
1.2 Project Partners:
Universities of Strathclyde & Stirling
1.3 Industrial Partners:
INCITE (Stirling), ACT Club (Strathclyde), Axeon
Ltd., National Instruments Corporation,
Emerson Process Management Ltd. (Total committed value: £60,356)
Total grant value requested: £556,110
1.4 Objectives:
The aim of
the project is to develop a new computational framework for coping with
significant complexity in dynamical systems.
This will involve developing new co-operative multi-agent based learning
control algorithms. Objectives are:
1. Introduction of a new theoretical
framework and algorithms for autonomous modular learning, hybrid
multiple-model, and switching control of complex systems.
2. Inclusion of novel event-driven
adaptation techniques for complex dynamical systems.
3. Production of a solution with
integrated monitoring and diagnostic techniques for complex systems based on a
learning multiple model and switching framework.
4. Development of new computational
intelligence based techniques for on-line controller design and
re-configuration.
5. Construction of algorithms for implementation
of robust adaptive intelligent controllers for non-linear complex systems and
production of software facilities to enable the algorithms to be assessed and
validated.
6. Testing and validation the robust
adaptive intelligent control strategy on two major industrial simulations and a
real robotic system.
1.5 Summary:
The aim is
to develop intelligent control numerical algorithms that have the capacity to
adjust and optimise complex systems behaviour when responses deteriorate, and
to enhance on line incremental system performance. The new approach will stem from employing new
techniques in computing science and non-linear mathematics integrating ideas of
robust intelligent control, multi-agent architectures, hybrid multiple-models,
switching techniques, data fusion, on-line condition monitoring and soft
computing tools.
The proposed computational methods for the control of very complex processes are ambitious and will include learning and intelligence to simplify the design process and provide enhanced overall performance. They will be adaptive so that the algorithms are: able to cope with unpredictable deterioration; autonomous, so no intervention of operators will be required to preserve reliability and performance; robust, so tolerating uncertainties and disturbances; and, modular, using multiple agents, contributing to the overall capabilities. The resulting novel computational framework will be evaluated on a diverse set of complex real-world applications in order to demonstrate its generic intelligent modelling and control capability, using a real-time robot application and two validated industrial simulations, working closely with companies throughout.
2. SECOND (‘medium-scale’)
Cluster Proposal:
2.1 Title: Towards
Multi-agent based Learning Modelling & Estimation in Complex Systems
2.2 Project Partners:
Universities of Strathclyde,
2.3 Industrial Partners:
INCITE (
Total grant value requested: £698,966
2.4 Objectives:
The aim of
the project is to develop a new modelling framework to cope with complexity in
dynamic systems. The research will develop
a new computationally-efficient framework based on multi-agent learning,
modelling and estimation framework for coping with this complexity. This will
involve new robust modelling techniques based on co-operative learning in a
multi–agent based framework including integrated condition monitoring,
estimation and diagnosis. The
methodology to be addressed will use learning of hybrid models, receding
horizon techniques, evolutionary and biologically-inspired computing, along
with game-type optimisation. Thus the
objectives are:
1. Introduction of a novel theoretical
framework and algorithms for distributed learning based modelling.
2. Development of new agent based
estimation for complex inter-connected dynamic systems.
3. Production of new distributed condition
monitoring and fault diagnosis algorithms.
4. Aggregation algorithms for the
implementation of diverse multiple-agents and the associated development of software facilities.
5. Assessment, testing and validation of
the proposed design framework on two major complex industrial (paper machine
& gas turbine) systems.
2.5 Summary:
This project is concerned with a new
multi-disciplinary approach to intelligent modelling, estimation of variables,
and of parameters in complex systems. Modern systems, for example a network of
embedded systems in automobiles, intelligent homes, intelligent manufacturing
and production systems and multi-robotics are complex in terms of both
behaviour and the large-scale nature of their connected and embedded structure. These systems are difficult to model, manage
and control using classical techniques.
Hence, an alternative methodology and as a result the learning and
identification issues are a vital challenge for further understanding.
The aim of this project is to
develop a new computational framework for modelling and estimation that can
cope with levels of complexity and effectively deal with changes in the system
produced by non-linearities, disturbances, failures
and faults. This will be achieved by
drawing inspiration from computing, behavioural, engineering and mathematical
sciences; integrating the ideas of estimation and fault detection (condition
monitoring), risk-sensitivity, multi-agent architectures, hybrid models and
soft computation.
Modelling and estimation must be
seen as part of a larger picture of managing and monitoring the performance of
complex systems. A multi-agent framework
will be an invaluable tool for the development of an efficient method of handling
complex systems. Such a novel framework
for modular learning has been proposed for further investigation as part of
another proposal submitted by the Strathclyde and
3. THIRD (‘large-scale’)
Cluster Proposal:
3.1 Title: Novel Computational Framework for Control of Complex
Engineering Systems
3.2 Project Partners:
Universities of
3.3 Industrial Partners:
INCITE (
Total grant value requested: £1,342,642
3.4 Objectives:
The project aim is to develop novel computational techniques for analysing,
modelling and controlling complex systems.
The research results will be a benchmark for the future development of
this particular subject area, thereby enhancing its applicability and
acceptance for the analysis and design of modern complex control systems. To achieve this aim the objectives are to:
1
Establish a novel computational framework for the
theory, analysis, design and operation of complex control systems. This will include the following developments:
1.1
Architectures for the design of systems with
learning, fault tolerance and self-repairing features.
1.2
Computational strategies for modelling complex
systems
1.3
New control strategies for complex embedded systems
1.4
Optimal decision-support systems for enhanced
autonomy and fault-tolerance
2
Demonstrate the generic nature of the framework by
assessing, testing, and validating on exemplar systems.
3
Establish a virtual
centre to run major international workshops and conferences on complex
control systems studies and provide a forum for stimulating further research in
the
3.5 Summary:
Recent advances in digital
and computing technology have resulted in a need for a better understanding and
development of complex engineering systems.
Advances, particularly in digital technology, are creating the potential
for higher levels of integrated, multi-loop distributed and networked control
in systems containing large numbers of dynamically interacting uncertain
components. So far these systems have
been engineered in an ad hoc or
piecemeal manner. However, the lack of a
systematic framework to model complex system has sometimes led to poor system
performance, as a consequence of the inability to properly understand the
system behaviour. The result is that we
are attempting to build systems where interactions among sub-systems and
components cannot be thoroughly planned, understood, anticipated, or guarded
against. This deficiency has resulted in
system malfunctions, failures and breakdown
or accidents, such as the
To cope with this complexity there is a critical
need to develop a formal computational
framework along with reliable computational paradigms to maintain quality
of service along with system safety and security. This project aims to develop
the required framework and develop a thorough understanding of the analysis and
design issues associated with the complexity of modern systems. Clearly, such a project requires multi-disciplinary
team effort and this research consortium plans to draw inspiration from the
emerging areas of science, such as “systems biology” and “bio-complexity”
including notions of hierarchical and distributed structures. This implies the synthesis of computing,
behavioural, engineering and mathematical sciences, synthesising control and
multiple-agent architectures for distributed decision-making. In order to
tackle these multi-disciplinary issues, this consortium comprises leading
experts and their teams from 8