Representations of uncertainty in sensorimotor control.

Uncertainty is ubiquitous in our sensorimotor interactions, arising from factors such as sensory and motor noise and ambiguity about the environment. Setting it apart from previous theories, a quintessential property of the Bayesian framework for making inference about the state of world so as to select actions, is the requirement to represent the uncertainty associated with inferences in the form of probability distributions. In the context of sensorimotor control and learning, the Bayesian framework suggests that to respond optimally to environmental stimuli the central nervous system needs to construct estimates of the sensorimotor transformations, in the form of internal models, as well as represent the structure of the uncertainty in the inputs, outputs and in the transformations themselves. Here we review Bayesian inference and learning models that have been successful in demonstrating the sensitivity of the sensorimotor system to different forms of uncertainty as well as recent studies aimed at characterizing the representation of the uncertainty at different computational levels.

Optimal control predicts human performance on objects with internal degrees of freedom.

On a daily basis, humans interact with a vast range of objects and tools. A class of tasks, which can pose a serious challenge to our motor skills, are those that involve manipulating objects with internal degrees of freedom, such as when folding laundry or using a lasso. Here, we use the framework of optimal feedback control to make predictions of how humans should interact with such objects. We confirm the predictions experimentally in a two-dimensional object manipulation task, in which subjects learned to control six different objects with complex dynamics. We show that the non-intuitive behavior observed when controlling objects with internal degrees of freedom can be accounted for by a simple cost function representing a trade-off between effort and accuracy. In addition to using a simple linear, point-mass optimal control model, we also used an optimal control model, which considers the non-linear dynamics of the human arm. We find that the more realistic optimal control model captures aspects of the data that cannot be accounted for by the linear model or other previous theories of motor control. The results suggest that our everyday interactions with objects can be understood by optimality principles and advocate the use of more realistic optimal control models for the study of human motor neuroscience.

Computational approaches to motor control.

This review will focus on four areas of motor control which have recently been enriched both by neural network and control system models: motor planning, motor prediction, state estimation and motor learning. We will review the computational foundations of each of these concepts and present specific models which have been tested by psychophysical experiments. We will cover the topics of optimal control for motor planning, forward models for motor prediction, observer models of state estimation arid modular decomposition in motor learning. The aim of this review is to demonstrate how computational approaches, as well as proposing specific models, provide a theoretical framework to formalize the issues in motor control.

A framework for analysing business performance, firm innovation and related contextual factors: Perceptions of managers and policy makers in two European regions

The aim of this paper is to propose a novel reference framework that can be used to study how different kinds of innovation can result in better business performance and how external factors can influence both the firm's capacity to innovate and innovation itself. The value of the framework is demonstrated as it is applied in an exploratory study of the perceptions of public policy makers and managers from two European regions - the Veneto Region in Italy and the East of England in the UK. Amongst other things, the data gathered suggest that managers are generally less convinced than public policy makers, that the innovativeness of a firm is affected by factors over which policy makers have some control. This finding poses the question "what, if any, role can public policy makers play in enhancing a company's competitiveness by enabling it to become more innovative?".

Hybrid model predictive control applied to switching control of burner load for a compact marine boi

This paper discusses the application of hybrid model predictive control to control switching between different burner modes in a novel compact marine boiler design. A further purpose of the present work is to point out problems with finite horizon model predictive control applied to systems for which the optimal solution is a limit cycle. Regarding the marine boiler control the aim is to find an optimal control strategy which minimizes a trade-off between deviations in boiler pressure and water level from their respective setpoints while limiting burner switches.The approach taken is based on the Mixed Logic Dynamical framework. The whole boiler systems is modelled in this framework and a model predictive controller is designed. However to facilitate on-line implementation only a small part of the search tree in the mixed integer optimization is evaluated to find out whether a switch should occur or not. The strategy is verified on a simulation model of the compact marine boiler for control of low/high burner load switches. It is shown that even though performance is adequate for some disturbance levels it becomes deteriorated when the optimal solution is a limit cycle. Copyright © 2007 International Federation of Automatic Control All Rights Reserved.

Air traffic control with an expected value criterion

in this contribution we discuss a stochastic framework for air traffic conflict resolution. The conflict resolution task is posed as the problem of optimizing an expected value criterion. Optimization is carried out by Monte Carlo Markov Chain (MCMC) simulation. A numerical example illustrates the proposed strategy. Copyright © 2005 IFAC.

Monte Carlo optimization strategies for air-traffic control

The safety of the flights, and in particular conflict resolution for separation assurance, is one of the main tasks of Air Traffic Control. Conflict resolution requires decision making in the face of the considerable levels of uncertainty inherent in the motion of aircraft. We present a Monte Carlo framework for conflict resolution which allows one to take into account such levels of uncertainty through the use of a stochastic simulator. A simulation example inspired by current air traffic control practice illustrates the proposed conflict resolution strategy. Copyright © 2005 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

External corporate venture capital investment: Towards a framework for capturing and measuring strategic value

This paper presents the initial results of on-going research in the field of external Corporate Venture Capital (CVC) investments, i.e. equity investments of large corporations in entrepreneurial ventures which originated outside the corporation. The research is motivated by the fact that external CVC plays an increasingly important role within the strategy of corporations. Driven by a general trend towards a more open approach to innovation, companies see particular value in external corporate venturing as a tool to gain, for example, access to complementary technologies and a general window on technology developments. The review of literature in the field of external corporate venturing clearly reveals that theoretical gaps exist in understanding mechanisms for capturing value and measurements of this value. To help close these gaps, the research addresses the underlying question "How do corporations and start-ups capture and measure strategic value through external CVC investments" by using embedded, multiple case studies. Following an initial set of case studies, steps towards the development of a framework for capturing and measuring strategic value from CVC investments are outlined within this paper and the resulting preliminary framework is presented. The paper closes with an outlook on ongoing and future research steps. © 2009 PICMET.

Optimal control with stochastic PDE constraints and uncertain controls

The optimal control of problems that are constrained by partial differential equations with uncertainties and with uncertain controls is addressed. The Lagrangian that defines the problem is postulated in terms of stochastic functions, with the control function possibly decomposed into an unknown deterministic component and a known zero-mean stochastic component. The extra freedom provided by the stochastic dimension in defining cost functionals is explored, demonstrating the scope for controlling statistical aspects of the system response. One-shot stochastic finite element methods are used to find approximate solutions to control problems. It is shown that applying the stochastic collocation finite element method to the formulated problem leads to a coupling between stochastic collocation points when a deterministic optimal control is considered or when moments are included in the cost functional, thereby forgoing the primary advantage of the collocation method over the stochastic Galerkin method for the considered problem. The application of the presented methods is demonstrated through a number of numerical examples. The presented framework is sufficiently general to also consider a class of inverse problems, and numerical examples of this type are also presented. © 2011 Elsevier B.V.

Nearest neighbour decoding with pilot-assisted channel estimation for fading multiple-access channels

This paper studies a noncoherent multiple-input multiple-output (MIMO) fading multiple-access channel (MAC). The rate region that is achievable with nearest neighbour decoding and pilot-assisted channel estimation is analysed and the corresponding pre-log region, defined as the limiting ratio of the rate region to the logarithm of the signal-to-noise ratio (SNR) as the SNR tends to infinity, is determined. © 2011 IEEE.

A framework for distributed intelligent automation systems developments

This paper proposes a pragmatic framework that has been developed for classifying and analyzing developments in distributed automation and information systems - especially those that have been labeled intelligent systems for different reasons. The framework dissects the different stages in the standard feedback process and assesses distribution in terms of the level of granularity of the organization that is being considered. The framework has been found to be useful in comparing and assessing different distributed industrial control paradigms and also for examining common features of different development projects - especially those that might be sourced from different sectors or domains. © 2012 IFAC.

Towards a fast electro-thermo-mechanical IGBT/Diode simulation framework for design of reliable power electronic converters

This paper describes a methodology that enables fast and reasonably accurate prediction of the reliability of power electronic modules featuring IGBTs and p-i-n diodes, by taking into account thermo-mechanical failure mechanisms of the devices and their associated packaging. In brief, the proposed simulation framework performs two main tasks which are tightly linked together: (i) the generation of the power devices' transient thermal response for realistic long load cycles and (ii) the prediction of the power modules' lifetime based on the obtained temperature profiles. In doing so the first task employs compact, physics-based device models, power losses lookup tables and polynomials and combined material-failure and thermal modelling, while the second task uses advanced reliability tests for failure mode and time-to-failure estimation. The proposed technique is intended to be utilised as a design/optimisation tool for reliable power electronic converters, since it allows easy and fast investigation of the effects that changes in circuit topology or devices' characteristics and packaging have on the reliability of the employed power electronic modules. © 2012 IEEE.