Autonomous intelligent cruise control using a novel multiple-controller framework incorporating fuzzy-logic-based switching and tuning

This paper presents a novel intelligent multiple-controller framework incorporating a fuzzy-logic-based switching and tuning supervisor along with a generalised learning model (GLM) for an autonomous cruise control application. The proposed methodology combines the benefits of a conventional proportional-integral-derivative (PID) controller, and a PID structure-based (simultaneous) zero and pole placement controller. The switching decision between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using a fuzzy-logic-based supervisor, operating at the highest level of the system. The supervisor is also employed to adaptively tune the parameters of the multiple controllers in order to achieve the desired closed-loop system performance. The intelligent multiple-controller framework is applied to the autonomous cruise control problem in order to maintain a desired vehicle speed by controlling the throttle plate angle in an electronic throttle control (ETC) system. Sample simulation results using a validated nonlinear vehicle model are used to demonstrate the effectiveness of the multiple-controller with respect to adaptively tracking the desired vehicle speed changes and achieving the desired speed of response, whilst penalising excessive control action. Crown Copyright (C) 2008 Published by Elsevier B.V. All rights reserved.

Single trial BCI operation via Wackermann parameters

Accurate single trial P300 classification lends itself to fast and accurate control of Brain Computer Interfaces (BCIs). Highly accurate classification of single trial P300 ERPs is achieved by characterizing the EEG via corresponding stationary and time-varying Wackermann parameters. Subsets of maximally discriminating parameters are then selected using the Network Clustering feature selection algorithm and classified with Naive-Bayes and Linear Discriminant Analysis classifiers. Hence the method is assessed on two different data-sets from BCI competitions and is shown to produce accuracies of between approximately 70% and 85%. This is promising for the use of Wackermann parameters as features in the classification of single-trial ERP responses.

A hyperstable neural network for the modelling and control of nonlinear systems

A multivariable hyperstable robust adaptive decoupling control algorithm based on a neural network is presented for the control of nonlinear multivariable coupled systems with unknown parameters and structure. The Popov theorem is used in the design of the controller. The modelling errors, coupling action and other uncertainties of the system are identified on-line by a neural network. The identified results are taken as compensation signals such that the robust adaptive control of nonlinear systems is realised. Simulation results are given.

A mixture of experts network structure construction algorithm for modelling and control

This paper introduces a new fast, effective and practical model structure construction algorithm for a mixture of experts network system utilising only process data. The algorithm is based on a novel forward constrained regression procedure. Given a full set of the experts as potential model bases, the structure construction algorithm, formed on the forward constrained regression procedure, selects the most significant model base one by one so as to minimise the overall system approximation error at each iteration, while the gate parameters in the mixture of experts network system are accordingly adjusted so as to satisfy the convex constraints required in the derivation of the forward constrained regression procedure. The procedure continues until a proper system model is constructed that utilises some or all of the experts. A pruning algorithm of the consequent mixture of experts network system is also derived to generate an overall parsimonious construction algorithm. Numerical examples are provided to demonstrate the effectiveness of the new algorithms. The mixture of experts network framework can be applied to a wide variety of applications ranging from multiple model controller synthesis to multi-sensor data fusion.

Variable selection algorithm for the construction of MIMO operating point dependent neurofuzzy networks

An input variable selection procedure is introduced for the identification and construction of multi-input multi-output (MIMO) neurofuzzy operating point dependent models. The algorithm is an extension of a forward modified Gram-Schmidt orthogonal least squares procedure for a linear model structure which is modified to accommodate nonlinear system modeling by incorporating piecewise locally linear model fitting. The proposed input nodes selection procedure effectively tackles the problem of the curse of dimensionality associated with lattice-based modeling algorithms such as radial basis function neurofuzzy networks, enabling the resulting neurofuzzy operating point dependent model to be widely applied in control and estimation. Some numerical examples are given to demonstrate the effectiveness of the proposed construction algorithm.

Control strategy for Boolean input systems

The purpose of this paper is to design a control law for continuous systems with Boolean inputs allowing the output to track a desired trajectory. Such systems are controlled by items of commutation. This type of systems, with Boolean inputs, has found increasing use in the electric industry. Power supplies include such systems and a power converter represents one of theses systems. For instance, in power electronics the control variable is the switching OFF and ON of components such as thyristors or transistors. In this paper, a method is proposed for the designing of a control law in state space for such systems. This approach is implemented in simulation for the control of an electronic circuit.

Optimal control of a class of discrete–continuous non-linear systems — decomposition and hierarchical structure

A technique is derived for solving a non-linear optimal control problem by iterating on a sequence of simplified problems in linear quadratic form. The technique is designed to achieve the correct solution of the original non-linear optimal control problem in spite of these simplifications. A mixed approach with a discrete performance index and continuous state variable system description is used as the basis of the design, and it is shown how the global problem can be decomposed into local sub-system problems and a co-ordinator within a hierarchical framework. An analysis of the optimality and convergence properties of the algorithm is presented and the effectiveness of the technique is demonstrated using a simulation example with a non-separable performance index.

Correlations of control variables in variational data assimilation

Variational data assimilation systems for numerical weather prediction rely on a transformation of model variables to a set of control variables that are assumed to be uncorrelated. Most implementations of this transformation are based on the assumption that the balanced part of the flow can be represented by the vorticity. However, this assumption is likely to break down in dynamical regimes characterized by low Burger number. It has recently been proposed that a variable transformation based on potential vorticity should lead to control variables that are uncorrelated over a wider range of regimes. In this paper we test the assumption that a transform based on vorticity and one based on potential vorticity produce an uncorrelated set of control variables. Using a shallow-water model we calculate the correlations between the transformed variables in the different methods. We show that the control variables resulting from a vorticity-based transformation may retain large correlations in some dynamical regimes, whereas a potential vorticity based transformation successfully produces a set of uncorrelated control variables. Calculations of spatial correlations show that the benefit of the potential vorticity transformation is linked to its ability to capture more accurately the balanced component of the flow.

Speech motor control in fluent and dysfluent speech production of an individual with apraxia of speech and Broca’s aphasia

Apraxia of speech (AOS) is typically described as a motor-speech disorder with clinically well-defined symptoms, but without a clear understanding of the underlying problems in motor control. A number of studies have compared the speech of subjects with AOS to the fluent speech of controls, but only a few have included speech movement data and if so, this was primarily restricted to the study of single articulators. If AOS reflects a basic neuromotor dysfunction, this should somehow be evident in the production of both dysfluent and perceptually fluent speech. The current study compared motor control strategies for the production of perceptually fluent speech between a young woman with apraxia of speech (AOS) and Broca’s aphasia and a group of age-matched control speakers using concepts and tools from articulation-based theories. In addition, to examine the potential role of specific movement variables on gestural coordination, a second part of this study involved a comparison of fluent and dysfluent speech samples from the speaker with AOS. Movement data from the lips, jaw and tongue were acquired using the AG-100 EMMA system during the reiterated production of multisyllabic nonwords. The findings indicated that although in general kinematic parameters of fluent speech were similar in the subject with AOS and Broca’s aphasia to those of the age-matched controls, speech task-related differences were observed in upper lip movements and lip coordination. The comparison between fluent and dysfluent speech characteristics suggested that fluent speech was achieved through the use of specific motor control strategies, highlighting the potential association between the stability of coordinative patterns and movement range, as described in Coordination Dynamics theory.

The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization

In this paper a new system identification algorithm is introduced for Hammerstein systems based on observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a non-uniform rational B-spline (NURB) neural network. The proposed system identification algorithm for this NURB network based Hammerstein system consists of two successive stages. First the shaping parameters in NURB network are estimated using a particle swarm optimization (PSO) procedure. Then the remaining parameters are estimated by the method of the singular value decomposition (SVD). Numerical examples including a model based controller are utilized to demonstrate the efficacy of the proposed approach. The controller consists of computing the inverse of the nonlinear static function approximated by NURB network, followed by a linear pole assignment controller.

Transputer based real time robot control

A parallel processor architecture based on a communicating sequential processor chip, the transputer, is described. The architecture is easily linearly extensible to enable separate functions to be included in the controller. To demonstrate the power of the resulting controller some experimental results are presented comparing PID and full inverse dynamics on the first three joints of a Puma 560 robot. Also examined are some of the sample rate issues raised by the asynchronous updating of inertial parameters, and the need for full inverse dynamics at every sample interval is questioned.

Robust control system design for generalized state-space systems

Robustness in multi-variable control system design requires that the solution to the design problem be insensitive to perturbations in the system data. In this paper we discuss measures of robustness for generalized state-space, or descriptor, systems and describe algorithmic techniques for optimizing robustness for various applications.

Profiling dehydrin gene sequence and physiological parameters in drought tolerant and susceptible spring wheat cultivars.

Physiological and yield traits such as stomatal conductance (mmol m-2s-1), Leaf relative water content (RWC %) and grain yield per plant were studied in a separate experiment. Results revealed that five out of sixteen cultivars viz. Anmol, Moomal, Sarsabz, Bhitai and Pavan, appeared to be relatively more drought tolerant. Based on morphophysiological results, studies were continued to look at these cultivars for drought tolerance at molecular level. Initially, four well recognized primers for dehydrin genes (DHNs) responsible for drought induction in T. durum L., T. aestivum L. and O. sativa L. were used for profiling gene sequence of sixteen wheat cultivars. The primers amplified the DHN genes variably like Primer WDHN13 (T. aestivum L.) amplified the DHN gene in only seven cultivars whereas primer TdDHN15 (T. durum L.) amplified all the sixteen cultivars with even different DNA banding patterns some showing second weaker DNA bands. Third primer TdDHN16 (T. durum L.) has shown entirely different PCR amplification prototype, specially showing two strong DNA bands while fourth primer RAB16C (O. sativa L.) failed to amplify DHN gene in any of the cultivars. Examination of DNA sequences revealed several interesting features. First, it identified the two exon/one intron structure of this gene (complete sequences were not shown), a feature not previously described in the two database cDNA sequences available from T. aestivum L. (gi|21850). Secondly, the analysis identified several single nucleotide polymorphisms (SNPs), positions in gene sequence. Although complete gene sequence was not obtained for all the cultivars, yet there were a total of 38 variable positions in exonic (coding region) sequence, from a total gene length of 453 nucleotides. Matrix of SNP shows these 37 positions with individual sequence at positions given for each of the 14 cultivars (sequence of two cultivars was not obtained) included in this analysis. It demonstrated a considerable diversity for this gene with only three cultivars i.e. TJ-83, Marvi and TD-1 being similar to the consensus sequence. All other cultivars showed a unique combination of SNPs. In order to prove a functional link between these polymorphisms and drought tolerance in wheat, it would be necessary to conduct a more detailed study involving directed mutation of this gene and DHN gene expression.

Control without deception

Lying to participants offers an experimenter the enticing prospect of making “others' behaviour” a controlled variable, but is eschewed by experimental economists because it may pollute the pool of subjects. This paper proposes and implements a new experimental design, the Conditional Information Lottery, which offers all the benefits of deception without actually deceiving anyone. The design should be suitable for most economics experiments, and works by a modification of an already standard device, the Random Lottery incentive system. The deceptive scenarios of designs which use deceit are replaced with fictitious scenarios, each of which, from a subject's viewpoint, has a chance of being true. The design is implemented in a sequential play public good experiment prompted by Weimann's (1994) result, from a deceptive design, that subjects are more sensitive to freeriding than cooperation on the part of others. The experiment provides similar results to Weimann's, in that subjects are at least as cooperative when uninformed about others' behaviour as they are if reacting to high contributions. No deception is used and the data cohere well both internally and with other public goods experiments. In addition, simultaneous play is found to be more efficient than sequential play, and subjects contribute less at the end of a sequence than at the start. The results suggest pronounced elements of overconfidence, egoism and (biased) reciprocity in behaviour, which may explain decay in contributions in repeated play designs. The experiment shows there is a workable alternative to deception.

Sensitivity analysis of an ocean carbon cycle model in the North Atlantic: an investigation of parameters affecting the air-sea CO2 flux, primary production and export of detritus

The sensitivity of the biological parameters in a nutrient-phytoplankton-zooplankton-detritus (NPZD) model in the calculation of the air-sea CO2 flux, primary production and detrital export is analysed. We explore the effect on these outputs of variation in the values of the twenty parameters that control ocean ecosystem growth in a 1-D formulation of the UK Met Office HadOCC NPZD model used in GCMs. We use and compare the results from one-at-a-time and all-at-a-time perturbations performed at three sites in the EuroSITES European Ocean Observatory Network: the Central Irminger Sea (60° N 40° W), the Porcupine Abyssal Plain (49° N 16° W) and the European Station for Time series in the Ocean Canary Islands (29° N 15° W). Reasonable changes to the values of key parameters are shown to have a large effect on the calculation of the air-sea CO2 flux, primary production, and export of biological detritus to the deep ocean. Changes in the values of key parameters have a greater effect in more productive regions than in less productive areas. The most sensitive parameters are generally found to be those controlling well-established ocean ecosystem parameterisations widely used in many NPZD-type models. The air-sea CO2 flux is most influenced by variation in the parameters that control phytoplankton growth, detrital sinking and carbonate production by phytoplankton (the rain ratio). Primary production is most sensitive to the parameters that define the shape of the photosynthesis-irradiance curve. Export production is most sensitive to the parameters that control the rate of detrital sinking and the remineralisation of detritus.