Automated Model Generation and Observer Design for Interconnected Systems : A Port-Hamiltonian Approach

This work addresses the automated generation of physical-based models and model-based observers. We develop port-Hamiltonian methods, which for the first time allow a complete and consistent automation of these two processes for a large class of interconnected systems

Bond graph modelling of exergy in integrated energy systems

Ph. D. Thesis.Integrated municipal or district energy systems are one facet of the effort to support sustainable energy systems that work towards reducing anthropogenic climate change emissions. Current energy systems — including electricity, heat, and cooling — operate mostly independently, under the control of domain-distinct industries and regulatory bodies. Operating these separate systems in a cooperative or integrated manner promises improvements in efficiency, the ability of networks to absorb renewable energy sources and storage, emissions reductions and community-based benefits. The nature of district energy systems is that they cannot easily be modified or built upon without severe disruption to the communities they serve, so assessments of their behaviour and performance caused by potential changes must be modelled. This thesis investigates what methods can model integrated energy systems and develops a bond graph-based approach to constructing a fully-integrated system model. Although energy based methods for integrated energy system modelling exist, this thesis demonstrates that exergy can form the basis of integrated energy system models. Exergy being a measure of the usefulness of energy allows the equivalence of energy domains in a single model form, permitting development of a genuine, physically-founded integrated energy system model. An integrated model of a residential district supplied by heat and electrical networks, based on a real UK urban area, is demonstrated in OpenModelica using the developed modelling approach. The concept of an exergy storage device is introduced to provide a mechanism for mediating energy flows between the networks. The model is used to evaluate the performance of the test network, using trial cases to investigate how transferring exergy between energy domains through the mediating storage affects the overall system energy and exergy efficiencies. Operational regimes that transfer energy from the electrical to the thermal sub-system using the mediating storage are found to improve the exergy efficiency of the system.Newcastle University, Siemen

Knowledge-based design support and inductive learning

Designing and learning are closely related activities in that design as an ill-structure problem involves identifying the problem of the design as well as finding its solutions. A knowledge-based design support system should support learning by capturing and reusing design knowledge. This thesis addresses two fundamental problems in computational support to design activities: the development of an intelligent design support system architecture and the integration of inductive learning techniques in this architecture.This research is motivated by the belief that (1) the early stage of the design process can be modelled as an incremental learning process in which the structure of a design problem or the product data model of an artefact is developed using inductive learning techniques, and (2) the capability of a knowledge-based design support system can be enhanced by accumulating and storing reusable design product and process information.In order to incorporate inductive learning techniques into a knowledge-based design model and an integrated knowledge-based design support system architecture, the computational techniques for developing a knowledge-based design support system architecture and the role of inductive learning in Al-based design are investigated. This investigation gives a background to the development of an incremental learning model for design suitable for a class of design tasks whose structures are not well known initially.This incremental learning model for design is used as a basis to develop a knowledge-based design support system architecture that can be used as a kernel for knowledge-based design applications. This architecture integrates a number of computational techniques to support the representation and reasoning of design knowledge. In particular, it integrates a blackboard control system with an assumption-based truth maintenance system in an object-oriented environment to support the exploration of multiple design solutions by supporting the exploration and management of design contexts.As an integral part of this knowledge-based design support architecture, a design concept learning system utilising a number of unsupervised inductive learning techniques is developed. This design concept learning system combines concept formation techniques with design heuristics as background knowledge to build a design concept tree from raw data or past design examples. The design concept tree is used as a conceptual structure for the exploration of new designs.The effectiveness of this knowledge-based design support architecture and the design concept learning system is demonstrated through a realistic design domain, the design of small-molecule drugs one of the key tasks of which is to identify a pharmacophore description (the structure of a design problem) from known molecule examples.In this thesis, knowledge-based design and inductive learning techniques are first reviewed. Based on this review, an incremental learning model and an integrated architecture for intelligent design support are presented. The implementation of this architecture and a design concept learning system is then described. The application of the architecture and the design concept learning system in the domain of small-molecule drug design is then discussed. The evaluation of the architecture and the design concept learning system within and beyond this particular domain, and future research directions are finally discussed

Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility

According to the National Research Council, the use of embedded systems throughout society could well overtake previous milestones in the information revolution. Mechatronics is the synergistic combination of electronic, mechanical engineering, controls, software and systems engineering in the design of processes and products. Mechatronic systems put “intelligence” into physical systems. Embedded sensors/actuators/processors are integral parts of mechatronic systems. The implementation of mechatronic systems is consistently on the rise. However, manufacturers are working hard to reduce the implementation cost of these systems while trying avoid compromising product quality. One way of addressing these conflicting objectives is through new automatic control methods, virtual sensing/estimation, and new innovative hardware topologies