An application of the individual channel analysis and design approach to control of a two-input two-output coupled-tanks system

Frequency-domain methods have provided an established approach to the analysis and design of single-loop feedback control systems in many application areas for many years. Individual Channel Analysis and Design (ICAD) is a more recent development that allows neo-classical frequency-domain analysis and design methods to be applied to multi-input multi-output control problems. This paper provides a case study illustrating the use of the ICAD methodology for an application involving liquid-level control for a system based on two coupled tanks. The complete nonlinear dynamic model of the plant is presented for a case involving two input flows of liquid and two output variables, which are the depths of liquid in the two tanks. Linear continuous proportional plus integral controllers are designed on the basis of linearised plant models to meet a given set of performance specifications for this two-input two-output multivariable control system and a computer simulation of the nonlinear model and the controllers is then used to demonstrate that the overall closed-loop performance meets the given requirements. The resulting system has been implemented in hardware and the paper includes experimental results which demonstrate good agreement with simulation predictions. The performance is satisfactory in terms of steady-state behaviour, transient responses, interaction between the controlled variables, disturbance rejection and robustness to changes within the plant. Further simulation results, some of which involve investigations that could not be carried out in a readily repeatable fashion by experimental testing, give support to the conclusion that this neo-classical ICAD framework can provide additional insight within the analysis and design processes for multi-input multi-output feedback control systems

Modelling human control behaviour with a Markov-chain switched bank of control laws

A probabilistic model of human control behaviour is described. It assumes that human behaviour can be represented by switching among a number of relatively simple behaviours. The model structure is closely related to the Hidden Markov Models (HMMs) commonly used for speech recognition. An HMM with context-dependent transition functions switching between linear control laws is identified from experimental data. The applicability of the approach is demonstrated in a pitch control task for a simplified helicopter model

Stochastic Frontier Models With Correlated Error Components

In the productivity modelling literature, the disturbances U (representing technical inefficiency) and V (representing noise) of the composite error W=V-U of the stochastic frontier model are assumed to be independent random variables. By employing the copula approach to statistical modelling, the joint behaviour of U and V can be parameterised thereby allowing the data the opportunity to determine the adequacy of the independence assumption. In this context, three examples of the copula approach are given: the first is algebraic (the Logistic-Exponential stochastic frontier model with margins bound by the Fairlie-Gumbel-Morgenstern copula) and the second and third are empirically oriented, using data sets well-known in productivity analysis. Analysed are a cross-section of cost data sampled from the US electrical power industry, and an unbalanced panel of data sampled from the US airline industryStochastic Frontier model; Copula; Copula approach; Sklar's theorem; Families of copulas; Spearman's rho.

Combining case based reasoning with neural networks

This paper presents a neural network based technique for mapping problem situations to problem solutions for Case-Based Reasoning (CBR) applications. Both neural networks and CBR are instance-based learning techniques, although neural nets work with numerical data and CBR systems work with symbolic data. This paper discusses how the application scope of both paradigms could be enhanced by the use of hybrid concepts. To make the use of neural networks possible, the problem's situation and solution features are transformed into continuous features, using techniques similar to CBR's definition of similarity metrics. Radial Basis Function (RBF) neural nets are used to create a multivariable, continuous input-output mapping. As the mapping is continuous, this technique also provides generalisation between cases, replacing the domain specific solution adaptation techniques required by conventional CBR. This continuous representation also allows, as in fuzzy logic, an associated membership measure to be output with each symbolic feature, aiding the prioritisation of various possible solutions. A further advantage is that, as the RBF neurons are only active in a limited area of the input space, the solution can be accompanied by local estimates of accuracy, based on the sufficiency of the cases present in that area as well as the results measured during testing. We describe how the application of this technique could be of benefit to the real world problem of sales advisory systems, among others

Granular synthesis for display of time-varying probability densities

We present a method for displaying time-varying probabilistic information to users using an asynchronous granular synthesis technique. We extend the basic synthesis technique to include distribution over waveform source, spatial position, pitch and time inside waveforms. To enhance the synthesis in interactive contexts, we "quicken" the display by integrating predictions of user behaviour into the sonification. This includes summing the derivatives of the distribution during exploration of static densities, and using Monte-Carlo sampling to predict future user states in nonlinear dynamic systems. These techniques can be used to improve user performance in continuous control systems and in the interactive exploration of high dimensional spaces. This technique provides feedback from users potential goals, and their progress toward achieving them; modulating the feedback with quickening can help shape the users actions toward achieving these goals. We have applied these techniques to a simple nonlinear control problem as well as to the sonification of on-line probabilistic gesture recognition. We are applying these displays to mobile, gestural interfaces, where visual display is often impractical. The granular synthesis approach is theoretically elegant and easily applied in contexts where dynamic probabilistic displays are required

Nomadic input on mobile devices: the influence of touch input technique and walking speed on performance and offset modeling

In everyday life people use their mobile phones on-the-go with different walking speeds and with different touch input techniques. Unfortunately, much of the published research in mobile interaction does not quantify the influence of these variables. In this paper, we analyze the influence of walking speed, gait pattern and input techniques on commonly used performance parameters like error rate, accuracy and tapping speed, and we compare the results to the static condition. We examine the influence of these factors on the machine learned offset model used to correct user input and we make design recommendations. The results show that all performance parameters degraded when the subject started to move, for all input techniques. Index finger pointing techniques demonstrated overall better performance compared to thumb-pointing techniques. The influence of gait phase on tap event likelihood and accuracy was demonstrated for all input techniques and all walking speeds. Finally, it was shown that the offset model built on static data did not perform as well as models inferred from dynamic data, which indicates the speed-specific nature of the models. Also, models identified using specific input techniques did not perform well when tested in other conditions, demonstrating the limited validity of offset models to a particular input technique. The model was therefore calibrated using data recorded with the appropriate input technique, at 75% of preferred walking speed, which is the speed to which users spontaneously slow down when they use a mobile device and which presents a tradeoff between accuracy and usability. This led to an increase in accuracy compared to models built on static data. The error rate was reduced between 0.05% and 5.3% for landscape-based methods and between 5.3% and 11.9% for portrait-based methods

Information-theoretic measures of music listening behaviour

We present an information-theoretic approach to the mea- surement of users’ music listening behaviour and selection of music features. Existing ethnographic studies of mu- sic use have guided the design of music retrieval systems however are typically qualitative and exploratory in nature. We introduce the SPUD dataset, comprising 10, 000 hand- made playlists, with user and audio stream metadata. With this, we illustrate the use of entropy for analysing music listening behaviour, e.g. identifying when a user changed music retrieval system. We then develop an approach to identifying music features that reflect users’ criteria for playlist curation, rejecting features that are independent of user behaviour. The dataset and the code used to produce it are made available. The techniques described support a quantitative yet user-centred approach to the evaluation of music features and retrieval systems, without assuming objective ground truth labels

Combining case based reasoning with neural networks

This paper presents a neural network based technique for mapping problem situations to problem solutions for Case-Based Reasoning (CBR) applications. Both neural networks and CBR are instance-based learning techniques, although neural nets work with numerical data and CBR systems work with symbolic data. This paper discusses how the application scope of both paradigms could be enhanced by the use of hybrid concepts. To make the use of neural networks possible, the problem's situation and solution features are transformed into continuous features, using techniques similar to CBR's definition of similarity metrics. Radial Basis Function (RBF) neural nets are used to create a multivariable, continuous input-output mapping. As the mapping is continuous, this technique also provides generalisation between cases, replacing the domain specific solution adaptation techniques required by conventional CBR. This continuous representation also allows, as in fuzzy logic, an associated membership measure to be output with each symbolic feature, aiding the prioritisation of various possible solutions. A further advantage is that, as the RBF neurons are only active in a limited area of the input space, the solution can be accompanied by local estimates of accuracy, based on the sufficiency of the cases present in that area as well as the results measured during testing. We describe how the application of this technique could be of benefit to the real world problem of sales advisory systems, among others

Nonlinear adaptive control using non-parametric Gaussian Process prior models

Nonparametric Gaussian Process prior models, taken from Bayesian statistics methodology are used to implement a nonlinear adaptive control law. The expected value of a quadratic cost function is minimised, without ignoring the variance of the model predictions. This leads to implicit regularisation of the control signal (caution), and excitation of the system. The controller has dual features, since it is both tracking a reference signal and learning a model of the system from observed responses. The general method and its main features are illustrated on a simulation example

Impact of smartphone notification display choice in a typing task

External displays have the potential to make smartphone notifications less obtrusive when a user has committed their attention to a primary task. We compare six notification displays, and evaluate the impact that negotiating smartphone interruptions has on a typing task when the number of notifications to ignore and act on are equal. A lab experiment with 30 participants is conducted, and initial results show that desktop pop-ups are preferred significantly more, where they require the fewest actions to read. Managing notifications via the notification bar is least preferred, despite requiring fewer actions to respond. This work is a well-controlled pre-cursor to the application of notification displays in social scenarios. The results motivate the use of external displays to manage attention around smartphone interruptions