Simulation-based optimal sensor scheduling with application to observer trajectory planning

The sensor scheduling problem can be formulated as a controlled hidden Markov model and this paper solves the problem when the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. The aim is to minimise the variance of the estimation error of the hidden state w.r.t. the action sequence. We present a novel simulation-based method that uses a stochastic gradient algorithm to find optimal actions. © 2007 Elsevier Ltd. All rights reserved.

Sequential decision making in general state space models

Many problems in control and signal processing can be formulated as sequential decision problems for general state space models. However, except for some simple models one cannot obtain analytical solutions and has to resort to approximation. In this thesis, we have investigated problems where Sequential Monte Carlo (SMC) methods can be combined with a gradient based search to provide solutions to online optimisation problems. We summarise the main contributions of the thesis as follows. Chapter 4 focuses on solving the sensor scheduling problem when cast as a controlled Hidden Markov Model. We consider the case in which the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. In sensor scheduling, our aim is to minimise the variance of the estimation error of the hidden state with respect to the action sequence. We present a novel SMC method that uses a stochastic gradient algorithm to find optimal actions. This is in contrast to existing works in the literature that only solve approximations to the original problem. In Chapter 5 we presented how an SMC can be used to solve a risk sensitive control problem. We adopt the use of the Feynman-Kac representation of a controlled Markov chain flow and exploit the properties of the logarithmic Lyapunov exponent, which lead to a policy gradient solution for the parameterised problem. The resulting SMC algorithm follows a similar structure with the Recursive Maximum Likelihood(RML) algorithm for online parameter estimation. In Chapters 6, 7 and 8, dynamic Graphical models were combined with with state space models for the purpose of online decentralised inference. We have concentrated more on the distributed parameter estimation problem using two Maximum Likelihood techniques, namely Recursive Maximum Likelihood (RML) and Expectation Maximization (EM). The resulting algorithms can be interpreted as an extension of the Belief Propagation (BP) algorithm to compute likelihood gradients. In order to design an SMC algorithm, in Chapter 8 uses a nonparametric approximations for Belief Propagation. The algorithms were successfully applied to solve the sensor localisation problem for sensor networks of small and medium size.

Probabilistic inference for fast learning in control

A novel framework is provided for very fast model-based reinforcement learning in continuous state and action spaces. It requires probabilistic models that explicitly characterize their levels of condence. Within the framework, exible, non-parametric models are used to describe the world based on previously collected experience. It demonstrates learning on the cart-pole problem in a setting where very limited prior knowledge about the task has been provided. Learning progressed rapidly, and a good policy found after only a small number of iterations.

Climate resilience of schools: a case study

Social and political concerns are frequently reflected in the design of school buildings, often in turn leading to the development of technical innovations. One example is a recurrent concern about the physical health of the nation, which has at several points over the last century prompted new design approaches to natural light and ventilation. The most critical concern of the current era is the global, rather than the indoor, environment. The resultant political focus on mitigating climate change has resulted in new regulations, and in turn considerable technical changes in building design and construction. The vanguard of this movement has again been in school buildings, set the highest targets for reducing operational carbon by the previous Government. The current austerity measures have moved the focus to the refurbishment and retrofit of existing buildings, in order to bring them up to the exacting new standards. Meanwhile there is little doubt that climate change is happening already, and that the impacts will be considerable. Climate scientists have increasing confidence in their predictions for the future; if today’s buildings are to be resilient to these changes, building designers will need to understand and design for the predicted climates in order to continue to provide comfortable and healthy spaces through the lifetimes of the buildings. This paper describes the decision processes, and the planned design measures, for adapting an existing school for future climates. The project is at St Faith’s School in Cambridge, and focuses on three separate buildings: a large Victorian block built as a substantial domestic dwelling in 1885, a smaller single storey 1970s block with a new extension, and an as-yet unbuilt single storey block designed to passivhaus principles and using environmentally friendly materials. The implications of climate change have been considered for the three particular issues of comfort, construction, and water, as set out in the report on Design for Future Climate: opportunities for adaptation in the built environment (Gething, 2010). The adaptation designs aim to ensure each of the three very different buildings remains fit for purpose throughout the 21st century, continuing to provide a healthy environment for the children. A forth issue, the reduction of carbon and the mitigation of other negative environmental impacts of the construction work, is also a fundamental aim for the school and the project team. Detailed modelling of both the operational and embodied energy and carbon of the design options is therefore being carried out, in order that the whole life carbon costs of the adaptation design options may be minimised. The project has been funded by the Technology Strategy Board as part of the Design for Future Climates programme; the interdisciplinary team includes the designers working on the current school building projects and the school bursar, supported by researchers from the University of Cambridge Centre for Sustainable Development. It is hoped that lessons from the design process, as well as the solutions themselves, will be transferable to other buildings in similar climatic regions.

The value handbook: getting the most from your buildings and spaces.

The Value Handbook is a practical guide, showing how public sector organisations can get the most from ther buildings and spaces in their area. It brings together essential evidence about the benefits of good design, and demonstrates how understanding the different types of value created by the built environment (exchange value, use value, image value,social value, environmental value, and cultural value)is the key to realising its full potential.

Thermal stratification produced by plumes and jets in enclosed spaces

The airflow and thermal stratification produced by a localised heat source located at floor level in a closed room is of considerable practical interest and is commonly referred to as a 'filling box'. In rooms with low aspect ratios H/R ≲ 1 (room height H to characteristic horizontal dimension R) the thermal plume spreads laterally on reaching the ceiling and a descending horizontal 'front' forms separating a stably stratified, warm upper region from cooler air below. The stratification is well predicted for H/R ≲ 1 by the original filling box model of Baines and Turner (J. Fluid. Mech. 37 (1968) 51). This model represents a somewhat idealised situation of a plume rising from a point source of buoyancy alone-in particular the momentum flux at the source is zero. In practical situations, real sources of heating and cooling in a ventilation system often include initial fluxes of both buoyancy and momentum, e.g. where a heating system vents warm air into a space. This paper describes laboratory experiments to determine the dependence of the 'front' formation and stratification on the source momentum and buoyancy fluxes of a single source, and on the location and relative strengths of two sources from which momentum and buoyancy fluxes were supplied separately. For a single source with a non-zero input of momentum, the rate of descent of the front is more rapid than for the case of zero source momentum flux and increases with increasing momentum input. Increasing the source momentum flux effectively increases the height of the enclosure, and leads to enhanced overturning motions and finally to complete mixing for highly momentum-driven flows. Stratified flows may be maintained by reducing the aspect ratio of the enclosure. At these low aspect ratios different long-time behaviour is observed depending on the nature of the heat input. A constant heat flux always produces a stratified interior at large times. On the other hand, a constant temperature supply ultimately produces a well-mixed space at the supply temperature. For separate sources of momentum and buoyancy, the developing stratification is shown to be strongly dependent on the separation of the sources and their relative strengths. Even at small separation distances the stratification initially exhibits horizontal inhomogeneity with localised regions of warm fluid (from the buoyancy source) and cool fluid. This inhomogeneity is less pronounced as the strength of one source is increased relative to the other. Regardless of the strengths of the sources, a constant buoyancy flux source dominates after sufficiently large times, although the strength of the momentum source determines whether the enclosure is initially well mixed (strong momentum source) or stably stratified (weak momentum source). © 2001 Elsevier Science Ltd. All rights reserved.