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Dissociative experiences, traditionally studied in relation to trauma and PTSD, may be important phenomena across many different psychological conditions, including as a contributory causal factor for psychotic experiences. In this study, the aim was to explore, using network approaches, how dissociative experiences taking the form of a Felt Sense of Anomaly (FSA) relate to both common mental health conditions and psychotic experiences. 6941 individuals from the general population completed online assessments of FSA-dissociation, post-traumatic stress symptoms (PTSS), anxiety, depression, insomnia, worry, distress tolerance, hallucinations, grandiosity, paranoia, and cognitive disorganization. An undirected partial correlation network analysis was used to explore the network structure, then Bayesian inference with Directed Acyclic Graphs (DAGs) was used to identify potential directions of relationships between dissociation and mental health symptoms. Dissociation was found to be highly connected in both network models. Both networks found direct relationships between dissociation and hallucinations, grandiosity, paranoia, cognitive disorganization, anxiety, depression, and PTSS. In the DAGs analysis, the direction of influence between dissociation and hallucinations, PTSS, anxiety and depression was unclear, however it was found to be probable that dissociation influences paranoia (97.66% of sampled DAGs found the direction dissociation to paranoia, versus 2.34% finding the reverse direction), cognitive disorganization (99.74% vs. 0.26%), and grandiosity (93.49% vs. 6.51%). Further, dissociation was found to be a probable influence of insomnia and distress tolerance via indirect pathways. In summary, dissociation is connected to many mental health disorders, and may influence a number of presentations, particularly psychotic experiences. The importance of dissociation in mental health may therefore currently be under-recognised., Highlights • Dissociation is highly connected to other mental health presentations. • Dissociation has a strong relationship with hallucinations. • The direction of effect between dissociation and anxiety, depression and trauma symptoms is unclear. • Dissociation likely influences paranoia, grandiosity and cognitive disorganization.

Demand side management (DSM) aims to efficiently manage power flow by engaging energy customers, through offering incentives via price signals to alter their consumption patterns or directly controlling their loads. However, the integration of renewable energy generators and batteries in residential premises requires new approaches for DSM as they offer more flexibility. Moreover, as there are often a large number of residential energy customers within a distribution network, it is quite challenging to accommodate all of them in the DSM. In this paper, we propose an Aggregated Method (AM) that allows the treatment of distribution grid as a composition of several microgrids, which helps to aggregate underlying energy customers’ power and energy constraints and operating preferences. In addition, we provide methods for distributing the aggregated energy demand decisions among the participating energy customers. In contrast to the alternative centralized method, our approach requires less computational time to obtain decisions and hence scales well with increasing network size. Moreover, our results indicate that when using our method, energy customers receive more benefits through satisfying their energy requirements and operating conditions. Our overall analyses showed that the proposed framework can be easily adopted by the electricity market operators to create scalable DSM programs.

This paper studies the complexity of solving the class G of all N -player non-cooperative games with continuous action spaces that admit at least one Nash equilibrium (NE). We consider a distributed Nash seeking setting where agents communicate with a set of system nodes (SNs), over noisy communication channels, to obtain the required information for updating their actions. The complexity of solving games in the class G is defined as the minimum number of iterations required to find a NE of any game in G with e accuracy. Using information-theoretic inequalities, we derive a lower bound on the complexity of solving the game class G that depends on the Kolmogorov 2e-capacity of the constraint set and the total capacity of the communication channels. We also derive a lower bound on the complexity of solving games in G which depends on the volume and surface area of the constraint set.

An optimal control problem for continuous time systems described by a special class of multi-valued mappings and quasi-concave utility functions is considered. The objective is defined as an analogue of the terminal functional defined over an infinite time horizon. An upper bound of this functional over all solutions to the system is established. The turnpike property is proved which states that all optimal solutions converge to some unique optimal stationary point.

In this paper improved stress intensity geometry factors are determined for four key geometric configurations. They were developed using a p-version finite element method program. Two-dimensional uniaxially loaded plates are investigated, with either: an edge crack, a crack approaching a hole, or a crack propagating from a hole after ligament failure. The three-dimensional problem of a through crack in an integral stiffener approaching a junction, under uniaxial tension, is also considered. The resulting normalised stress intensity factor data are provided as compact equations or presented in tabular form.

River basins are key components of water supply grids. River basin operators must handle a complex set of objectives including runoff storage, flood control, supply for consumptive use, hydroelectric power generation, silting management, and maintenance of river basin ecology. At present, operators rely on a combination of simulation and optimization tools to help them make operational decisions. The complexity associated with this approach makes it suitable for long term planning but not daily or hourly operation. The consequence is that between longerterm optimized operation points, river basins are largely operated in open loop. This leads to operational inefficiencies most notably wasted water and poor ecological outcomes. This paper proposes a systematic approach using optimal control based on simple low order models for the real-time operation of entire river basin networks. © 2011 IFAC.

Many manufacturing environments are only semi-automated and still require operator input for key aspects of operations. Decision Support Systems (DSS) are a useful means of improving the performance of human-in-the-loop control aspects of operations. Passive DSS systems can empower operators to make optimal decisions while still allowing them to retain control of the plant. However in practice DSS systems are not always utilised to their full potential and ‘operator discretion’ can result in sub-optimal plant performance. An on-line real-time DSS has been implemented to help operators minimise redundant cleaning cycles in a brewery. Autonomous monitoring functionality has been developed to provide visibility and transparency of the actual use and performance of the DSS.

A new generation of light sources is now coming on line to address more fundamental scientific challenges in the realms of the ultra-small and ultra-fast, as well as in dynamics. At Jefferson Lab we have developed an Energy Recovered Linac [1,2] based light source that provides tunable sub-ps and synchronized pulses of IR and THz light not available elsewhere. In SRN 13.4 (2000), we reported on an earlier incarnation of this light source [2], and in the past seven years it has undergone a major upgrade [3].

A new THz/IR/UV photon source at Jefferson Lab is the first of a new generation of light sources based on an Energy-Recovered, (superconducting) Linac (ERL). The machine has a 160 MeV electron beam and an average current of 10 mA in 75 MHz repetition rate hundred femtosecond bunches. These electron bunches pass through a magnetic chicane and therefore emit synchrotron radiation. For wavelengths longer than the electron bunch the electrons radiate coherently a broadband THz ∼ half cycle pulse whose average brightness is >5 orders of magnitude higher than synchrotron IR sources. Previous measurements showed 20 W of average power extracted [Carr, et al., Nature 420 (2002) 153]. The new facility offers simultaneous synchrotron light from the visible through the FIR along with broadband THz production of 100 fs pulses with >200 W of average power. The FELs also provide record-breaking laser power [Neil, et al., Phys. Rev. Lett. 84 (2000) 662]: up to 10 kW of average power in the IR from 1 to 14 μm in 400 fs pulses at up to 74.85 MHz repetition rates and soon will produce similar pulses of 300–1000 nm light at up to 3 kW of average power from the UV FEL. These ultrashort pulses are ideal for maximizing the interaction with material surfaces. The optical beams are Gaussian with nearly perfect beam quality. See www.jlab.org/FEL for details of the operating characteristics; a wide variety of pulse train configurations are feasible from 10 ms long at high repetition rates to continuous operation. The THz and IR system has been commissioned. The UV system is to follow in 2005. The light is transported to user laboratories for basic and applied research. Additional lasers synchronized to the FEL are also available. Past activities have included production of carbon nanotubes, studies of vibrational relaxation of interstitial hydrogen in silicon, pulsed laser deposition and ablation, nitriding of metals, and energy flow in proteins. This paper will present the status of the system and discuss some of the discoveries we have made concerning the physics performance, design optimization, and operational limitations of such a first generation high power ERL light source.

This paper considers the optimal control of linear systems where measurement data is transmitted from the plant output to the controller over a noiseless communication channel with limited instantaneous data rate. The cost is defined to be the average, over a random initial state, of the usual infinite horizon quadratic regulation criterion, and the number of bits transported by the channel during each sampling interval is bounded. Several fundamental properties of the optimal cost functional are derived for initial state densities that satisfy a mild moment condition. Using these properties, precise expressions for the optimal cost and policy are obtained assuming a uniformly distributed initial state. These expressions agree with the classical optimal LQR results in the high data rate limit and with recent minimum rate results in the low rate regime. Extensions to the case of non-uniform densities and vector-valued states are discussed.

This paper addresses the problem of adaptively controlling a plant with unknown parameters using communication-limited feedback. Assuming known dynamics, expressions have recently been obtained for the minimum average feedback data rate required for asymptotic stabilisability. The main purpose of this work is to demonstrate that this minimum rate does not increase if the plant parameters are unknown, and the key elements of a stabilizing, minimum-rate policy are explicitly discussed. By regarding the uncertain plant as a higher-dimensional, nonlinear plant with unknown initial condition, it is shown that this result agrees with the recent concept of local topological feedback entropy. Extensions to the case of uncertain nonlinear plants are discussed.

In this work, we numerically investigate a state estimation problem for the chaotic Chua system involving finite communication channel capacity constraints. Evaluation of the asymptotic estimation error supports the conjecture that the minimum transmission rate should exceed maximum local entropy of the chaotic system over its attractor. It is shown that the first order time-varying quantizer with memory provides reasonable approximation for the minimum transmission rate.

We analyze performance of adaptive chaotic synchronisation system under information constraints in assumption that some system parameters are unknown and only system output is measured. Such a problem was studied previously in absence of information constraints based on adaptive observer scheme, allowing for its usage for message transmission systems. We provide analytical bounds for the closed-loop system performance (asymptotic error) and conduct a numerical case study for a typical chaotic system, namely the Chua circuit, in presence of information constraints. It is shown that the time-varying quantiser with one-step memory provides reasonable approximation for the minimum transmission rate for adaptive state estimation.

A survey of the emerging field termed “control of chaos” is given. Several major branches of research are discussed in detail: feedforward or “nonfeedback control” (based on periodic excitation of the system); “OGY method” (based on linearization of the Poincare map), “Pyragas method” (based on a time-delay feedback), traditional control engineering methods including linear, nonlinear and adaptive control, neural networks and fuzzy control. Some unsolved problems concerning the justification of chaos control methods are presented. Other directions of active research such as chaotic mixing, chaotization, etc. are outlined. Applications in various fields of engineering are discussed.

In this paper, it is shown that the problem of communication-limited stabilization is related to the concept of topological entropy, introduced by Adler et. al. as a measure of the information rate of a continuous map on a compact topological space. Using open covers, the notion of topological feedback entropy (TFE) is defined and proposed as a measure of the inherent rate at which a map on a noncompact topological space with inputs generates stability information. It is then proven that a topological dynamical plant can be stabilized into a compact set if and only if the data rate in the feedback loop exceeds the TFE of the plant on the set.

Target tracking using delayed, out-of-sequence measurements is a problem of growing importance due to an increased reliance on networked sensors interconnected via complex communication network architectures. In such systems, it is often the case that measurements are received out-of-time-order at the fusion center. This paper presents a Bayesian solution to this problem and provides approximate, implementable algorithms for both cluttered and non-cluttered scenarios involving single and multiple time-delayed measurements. Such an approach leads to a solution involving the joint probability density of current and past target states. In contrast, existing solutions in the literature modify the sensor measurement equation to account for the time delay and explicitly deal with the resulting correlations that arise in the process noise and current target state. In the Bayesian solution proposed in this paper, such cross correlations are treated implicitly. Under linear Gaussian assumptions, the Bayesian solution reduces to an augmented state Kalman filter (AS-KF) for scenarios devoid of clutter and an augmented state probabilistic data association filter (AS-PDA) for scenarios involving clutter. Computationally efficient versions of AS-KF and AS-PDA are considered in this paper. Simulations are presented to evaluate the performance of these solutions.

A critical notion in the field of communication-limited control is the smallest data rate above which there exists a stabilising coding and control law for a given plant. This quantity measures the lowest rate at which information can circulate in a stable feedback loop and provides a practical guideline for the allocation of communication resources. In this paper, the exponential stabilisability of finite-dimensional LTI plants with limited feedback data rates is investigated. By placing a probability density on the initial state and casting the objective in terms of state moments, the problem is shown to be similar to one in asymptotic quantisation. Quantisation theory is then applied to obtain the infimum stabilising data rate over all causal coding and control laws, under mild requirements on the initial state density.

The left-inverse system with minimal order of a discrete-time nonlinear system is studied in a linear algebraic framework. The general structure of the left inverse is described. Two algorithms are given for constructing left-inverse systems having minimal order.

A critical notion in the field of communication-limited control is the lowest data rate above which a given system is stabilisable. In this paper the objective is to derive this rate for a multidimensional LTI system when exponential stability with a given decay is desired. By assuming an initial state probability density, a new quantiser distortion lower bound and an asymptotic quantisation result are able to be applied to derive the infimum stabilising data rate, under very mild requirements on the initial density. Furthermore, a stabilising scheme is explicitly constructed.

Jefferson Laboratory's Free Electron Laser (FEL) incorporates a cesiated gallium arsenide (GaAs) DC photocathode gun as its electron source. By using a set of scanning mirrors, the surface of the GaAs wafer is illuminated with a 543.5nm helium–neon laser. Measuring the current flow across the biased photocathode generates a quantum efficiency (QE) map of the 1-in. diameter wafer surface. The resulting QE map provides a very detailed picture of the efficiency of the wafer surface. By generating a QE map in a matter of minutes, the photocathode scanner has proven to be an exceptional tool in quickly determining sensitivity and availability of the photocathode for operation.

This paper considers the sensor scheduling problem which consists of estimating the state of an uncertain process based on measurements obtained by switching a given set of noisy sensors. The noise and uncertainty models considered in this paper are assumed to be unknown deterministic functions which satisfy an energy type constraint known as an integral quadratic constraint. The problem of optimal robust sensor scheduling is formulated and solution to this problem is given in terms of the existence of suitable solutions to a Riccati differential equation of the game type and a dynamic programming equation. Furthermore, a real time implementable method for sensor scheduling is also presented.

This paper investigates the stabilizability of a linear, discrete-time plant with a real-valued output when the controller, which may be nonlinear, receives observation data at a known rate. It is first shown that, under a finite horizon cost equal to the m th output moment, the problem reduces to quantizing the initial output. Asymptotic quantization theory is then applied to directly obtain the limiting coding and control scheme as the horizon approaches infinity. This is proven to minimize a particular infinite horizon cost, the value of which is derived. A necessary and sufficient condition then follows for there to exist a coding and control scheme with the specified data rate that takes the m th output moment to zero asymptotically with time. If the open-loop plant is finite-dimensional and time-invariant, this condition simplifies to an inequality involving the data rate and the unstable plant pole with greatest magnitude. Analagous results automatically hold for the related problem of state estimation with a finite data rate.

In this paper we present two new results on Bode-type integrals. Firstly, we obtain, for a given scalar or multivariable continuous-time plant, the infimum of the Bode sensitivity integral which can be obtained with any stabilizing controller. The result involves the unstable plant poles and, perhaps surprisingly, a subset of the plant nonminimum phase zeros. Secondly, we obtain an apparently new expression for the Bode integral for the complementary sensitivity for a stable discrete-time scalar system.

The driver for Jefferson Lab's kW-level infrared free-electron laser (FEL) is a superconducting, recirculating accelerator that recovers 75% of the electron-beam power and converts it to radio frequency power. As reported in FEL'98, the accelerator operated ''straight-ahead'' to deliver 38 MeV, 1.1 mA cw current for lasing at wavelengths in the vicinity of 5 microns. The waste beam was sent directly to a dump, bypassing the recirculation loop. Stable operation at up to 311 W cw was achieved in this mode. The machine has now recirculated cw average current up to 4.6 mA and has lased cw with energy recovery up to 1,720 W output at 3.1 microns. This is the first FEL to ever operate in the ''same-cell'' energy recovery mode. Energy recovery offers several advantages (reduced RF power and dramatically reduced radio-nuclide production at the dump) and several challenges will be described. The authors have observed heating effects in the mirrors which will be described. They will also report on the additional performance measurements of the FEL that have been performed and connect those measurements to standard models.

We consider tracking algorithms for manoeuvring targets when the observations include extra information on the current operating mode of the target obtained from an image sensor. The target is modelled as a Markov jump linear system and the image-based observations form a discrete-time point process. We derive the optimal (minimum mean square error) filtered estimate which intrinsically fuses the image-based and primary observations. This optimal filter is computationally prohibitive but provides the basis for a clear understanding of various suboptimal approaches. We propose the image-enhanced IMM filter as a practical alternative which retains many desirable properties of the optimal filter and outperforms existing image-enhanced tracking algorithms over a broad range of operating scenarios.

This paper considers the problem of estimating the state of a linear system via a digital link with a finite bit-rate R. Classical estimation theory does not apply here since the estimator only observes the transmitted sequence of finite-valued symbols. A new coder-estimator for linear systems with process and measurement noise is given. Provided that a certain inequality linking the bit-rate to the dynamical parameters is satisfied, and under very mild assumptions on the noise distributions, the expected absolute estimation error remains of the same order as in the classical situation with infinite bit-rate. In particular, if the classical estimation error approaches zero, then the finite bit-rate error goes to zero at exactly the same rate

As reported previously [1], Jefferson Lab is building a free-electron laser capable of generating a continuous wave kilowatt laser beam. The driver-accelerator consists of a superconducting, energy-recovery accelerator. The initial stage of the program was to produce over 100 W of average power with no recirculation. In order to provide maximum gain the initial wavelength was chosen to be 5 mu-m and the initial beam energy was chosen to be 38.5 MeV. On June 17, 1998, the laser produced 155 Watts cw power at the laser output with a 98% reflective output coupler. On July 28th, 311 Watts cw power was obtained using a 90% reflective output coupler. A summary of the commissioning activities to date as well as some novel lasing results will be summarized in this paper. Present work is concentrated on optimizing lasing at 5 mu-m, obtaining lasing at 3 mu-m, and commissioning the recirculation transport in preparation for kilowatt lasing this fall.

There are many practical control problems where the control action is determined by switching among a given set of control laws. This paper presents necessary and sufficient conditions to test for quadratic stabilizability and for robust stabilizability with a quadratic storage function for switched controller systems. Algorithms which can be used to construct appropriately stabilizing control laws are also presented.

The purpose of this paper is to consider the problem of adaptive control of a continuous-time single-input single-output nonlinear stochastic system which is sampled at regular time-intervals, subject to certain input (control) constraints. The (unknown) system is not required to be stable in the absence of control. Consequently, a strict saturation input constraint cannot be imposed if the closed-loop system is to remain stable. Therefore, a type of “long-term average control effort᾿ input constraint is imposed. A control law is presented and conditions on the sampling rate are found, such that the Markov chain consisting of the state and control at the sampling instants is ergodic, and the input constraint is satisfied.

Uncertain nonlinear systems affine in the input are considered. It is shown that under some matching conditions these systems can be made practically stable by state feedback. We present an adaptive algorithm which renders the origin practically stable to any desired accuracy. The presented algorithms are simple and computationally cheap.

We study the problem of minimum-variance control of linear time-varying discrete-time systems. We consider the standard time-varying controlled autoregressive moving-average model and develop a time-varying controller that guarantees both closed-loop stability and minimum-variance output control for a large class of linear time-varying systems. The controller is based on our previous results on minimum-variance prediction for linear time-varying systems. We also show that the standard minimum-variance controller cannot in general guarantee closed-loop stability when applied to linear time-varying systems. However, a simple modification can make the closed loop stable.

The paper presents a new approach to robust control synthesis problems for hybrid dynamical systems. The hybrid system under consideration is a composite of a continuous plant and a discrete event controller. State and output feedback problems are considered. The main results are given in terms of the existence of suitable solutions to a dynamic programming equation and a Riccati differential equation of the H∞ filtering type. These results show a connection between the theories of hybrid dynamical systems and robust and nonlinear control.

We consider the problem of estimating the state of a discrete-time, linear stochastic system whose observation process consists of a finite set of known, linear measurement models. The correspondence of the measurements with the models is unknown. Assuming all the measurements relate to the state, we derive a recursive algorithm, the multiple simultaneous measurement filter (MSMF), which provides a fixed-complexity, sub-optimal solution to this problem. The MSMF is a generalization of the discrete-time Kalman filter and reduces to the latter when a single measurement model applies. Simulations are provided that demonstrate the superior performance of the MSMF over the probabilistic data association filter for tracking a single target in an environment where up to three measurements of the state are received simultaneously.

The paper presents a new approach to output feedback robust control synthesis problems for hybrid dynamical systems. The hybrid system under consideration is a composite of a continuous plant and a discrete event controller. An output feedback H ∞ control problem on an infinite time interval is considered. The main results are given in terms of the existense of suitable solutions to a dynamic programming equation and a Riccati algebraic equation of the H ∞ filtering type. These results show a connection between the theories of hybrid dynamical systems and robust and nonlinear control.

This paper contains results on stability robustness for continuous-time SISO systems comprising a plant with H ∞ norm bounded uncertainty in the numerator and a feedback controller with integral action. An upper bound on the H ∞ norm of the minimal destabilising perturbation is obtained. Also the class of nominal plants for which this robustness bound can be achieved with a linear time invariant controller is distinguished. Membership of this class is equivalent to the positive semidefiniteness of a Pick matrix, whose dimension is the number of unstable plant poles. A design example is also included.

This paper considers the problem of forming and maintaining tracks when measurements have both uncertain origin and are limited by finite resolution. A new version of the probabilistic data association algorithm is derived which takes account of the effects of sensor resolution. The Integrated Probabilistic Data Association-Finite Resolution (IPDA-FR) algorithm is presented for two situations, first when an accurate stationary clutter map is available and second when no a priori clutter information is available. Both cases result in a set of recursive formulae for data association and probability of track existence, thus enabling automatic track initiation and track maintenance. Simulation results comparing the performance of IPDA-FR with the IPDA and standard PDA algorithm are presented.

In this paper we study the problem of minimum variance prediction for linear time-varying systems. We consider the standard time-varying autoregression moving average (ARMA) model and develop a predictor which guarantees minimum variance prediction for a large class of linear time-varying systems. The predictor is developed based on a pseudocommutation technique for dealing with noncommutativity of linear time-varying operators in a transfer operator framework. We also show connections between this input-output predictor and the Kalman predictor via an example.

A new approach to high-resolution radar imaging is presented. The starting is the notion that there exists an image of the target which needs to be identified. This image consists of a finite array of pixel elements, each with a magnitude and phase representing the complex reflectivity of target scatterers. The radar observation process is then modelled as a sequence of matrix operations applied to the image. We first treat this problem in a general way, then impose practical constraints to finally deal with the problem of a rotating target. One solution for recovering the image from the measurements in this case is obtained by inverting the matrix operations, although the rotation rate of the target is required. A second solution based on maximum likelihood techniques is derived and the connection with the pseudoinverse is shown. When the rotation rate of the target is unknown, the maximum likelihood solution can still be found through minimization of a non-convex matrix trace cost function. It is also observed that for unknown target rotation rate the maximum likelihood solution gives the same image estimate as does the conventional Fourier transform solution. Some results of applying this identification approach to simulated and experimental data are presented. Various extensions are proposed, including a recursive solution.

This paper presents an iterative adaptive pole assignment control algorithm which iteratively solves the Diophantine equation to reduce the on line Computation load of the adaptive control law. A set of conditions are provided under which the closed loop system is stable. The system properties are illustrated by a simulation example.

This paper combines the discrete periodic controller with adaptive pole assignment control system to present a model reference adaptive control system An adaptive periodic gain is developed which assigns the system zeros to achieve adaptive model matching. This scheme overcomes the difficulty of unstable pole- zero cancellations in standard model reference adaptive control systems. and thus can be applied to a non-minimum phase plant.

Present adaptive eoniiollei implementations have limitations in then use due to requirements of sufficient excitation. This results in complex solutions that do not lend well to antenna tracking systems. The Australia Telescope, a huge array of radio antennae, called for a close examination of servo controller and structural related design due to the strict tracking specifications. We discuss these issues and piesent an adaptive controller based on derivative feed forward control and the MIT-Rule that has performed well when applied to the tracking antenna problem.

This paper briefly describes a control technique which has been found usefulon a large class of linear and non-linear systems . The B-MAC controller has been extensively evaluated on many experimental and simulation examples. Some of these results are presented in this paper including those for an induction motor. The controller has been theoretically analysed in some detail and a brief outline of these results is provided.

Algorithms described in the literature for adaptation of equalizers usually consider minimization of a mean square cost. The mean square cost considered is usually comprised of two components; one component is the mean square error which arises because of inexact equilization of the channel response to the desired response. The other component can be identified as the mean square value of the noise at the output of the equalizer which is generated by channel noise. The paper describes algorithms which enable the two components to be independently weighted and the weighted mean square error minimized by the adaptive algorithms. Motivation for considering the independent weight is discussed in relation to the use of a compromised Viterbi algorithm receiver for the recovery of digital data transmitted over a noisy dispersive channel. However, other applications also exist.

Many of the current adaptive control techniques do not directly address the performance criteria required by precision servo tracking systems. Many rely on sufficient excitation of the process to converge to stable, useful, solutions that may still not satisfy the primary operating objectives of the process. Solutions requiring excitation do not lend well to antenna tracking systems. The Australia Telescope, a large array of radio antennae, called for a close examination of servo controller and structural related design due to the strict tracking specifications. We discuss these issues and present an adaptive controller based on derivative feed-forward control using MIT-Rule adaptation that has performed well when applied to the tracking antenna problem. Based on our experience with the AT application we emphasize the need to approach adaptive control studies from an application viewpoint from which future directions in adaptive control studies may be borne out.

This paper presents the application of new interval polynomial results to die design of pole assignment controllers. The new theory allows controllers to make use of freedom in the placement of closed-loop poles to achieve increased robustness against plant parameter uncertainties.

This paper considers the problem of direct self-tuning controller design when the plant input is subject to a hard saturation constraint. fie show that for a stable plant or a class of type-1 plants (not necessarily non-minimum phase) the system is BIBO stable and satisfies a control tracking property. The stability result is also shown to hold when the plant is under-modelled and/or when bounded disturbances exist. Simulation results are presented showing system performance

The considerable potential benefits of compatibility checking of aircraft dynamic flight data has, in the past, not been fully realized when applied to real data. It is suggested in this paper that this is partly due to the presence of errors in the real data which are not usually accounted for in computer simulation studies. When factors such as instrument offset from centre of gravity, measurement time delays, sinusoidal disturbances and non-linearities are accounted for, good results can be achieved with moderate quality instrumentation. The effect of these factors on the Identified instrument errors are studied in this paper using the extended Kalman Filter approach and the Maximum Likelihood method. The relative merits of each approach are also studied.

The pule placement design principle pruvides a good way of determining a closed loop system with desired properties. The method has been used in adaptive contexts for many years. A drawback of the method is, however, that the polynomial formulation mav lead to robustness problems because of the sensitivity with respect to the coefficients of the polynomials. Further, unless all process zeros are cancelled a set of linear equations has to be solved at each step in time. In this paper an attempt is made to avoid these two problems. The sensitivity of the polynomial formulation is avoided by parameterizing the model in the poles and zeros, which are estimated on-line. With the plant expressed in this form it is possible to parameterize the controller such that the solution of the Diophantine equation corresponds to the solution of a triangular system of equations. The controller parameters can thus be computed recursively without extensive computations

This paper presents theoretical results concerned with boundedness and control-tracking properties of amplitude constrained direct adaptive controllers. These properties require a finite gain stability condition for the non-adaptive control system using the true parameters and a property of the saturation non-linearity. The adaptive control also accounts for some disturbances and unmodelled dynamics to obtain certain degree of robustness. Examples are given showing system performance.

In this paper we consider several theoretical aspects of signal design when the signal-matched filter output is required to possess certain optimality properties. The problem is formulated as a constrained minimax problem in function space. Necessary conditions for optimality are obtained, and a useful numerical algorithm is derived. Several uncertainty-principle-type results are also established.