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WOS: 000468732700018, PubMed ID: 31054483, Background and aims: One of the objectives of the ESC-EORP EUROASPIRE V survey is to determine how well European guidelines on the management of dyslipidaemias are implemented in coronary patients. Methods: Standardized methods were used by trained technicians to collect information on 7824 patients from 130 centers in 27 countries, from the medical records and at a visit at least 6 months after hospitalization for a coronary event. All lipid measurements were performed in one central laboratory. Patients were divided into three groups: on high-intensity LDL-C-lowering-drug therapy (LLT), on low or moderate-intensity LLT and on no LLT. Results: At the time of the visit, almost half of the patients were on a high-intensity LLT. Between hospital discharge and the visit, LLT had been reduced in intensity or interrupted in 20.8% of the patients and had been started or increased in intensity in 11.7%. In those who had interrupted LLT or had reduced the intensity, intolerance to LLT and the advice of their physician were reported as the reason why in 15.8 and 36.8% of the cases, respectively. LDL-C control was better in those on a high-intensity LLT compared to those on low or moderate intensity LLT. LDL-C control was better in men than women and in patients with self-reported diabetes. Conclusions: The results of the EUROASPIRE V survey show that most coronary patients have a less than optimal management of LDL-C. More professional strategies are needed, aiming at lifestyle changes and LLT adapted to the need of the individual patient., ESC - EORP; AmgenAmgen; Eli LillyEli Lilly; PfizerPfizer; SanofiSanofi-Aventis; Ferrer; Novo NordiskNovo Nordisk, The EUROASPIRE V survey was carried out under the auspices of the ESC - EORP. Since the start of EORP, the following companies have supported the programme: Amgen, Eli Lilly, Pfizer, Sanofi, Ferrer and Novo Nordisk. The sponsors of the EUROASPIRE surveys had no role in the design, data collection, data analysis, data interpretation, decision to publish, or writing the manuscript.

The present study compares aspects of the performance of new tungsten (W) nanoporous implantable electrodes to their smooth (non-porous) counterparts. In vivo studies in healthy rats indicated that after four months of implantation the change in the power spectral density function in the low frequency range (which is where the electroencephalogram data reside/EEG) was smaller for the nanoporous recording electrodes, indicating that the porosity allowed to maintain a more consistent reading of the EEG. Molecular dynamics simulations illustrated that during compression, protrusions form on the surface of smooth W microwires, which can produce severe stress concentrations in the brain tissue that damage cells and affect the electrical conductivity. This problem may become critical for thinner electrodes or under abnormal circumstances. Our simulations showed that inducing a porous structure, allowed to dispense such mechanical instabilities. These results indicate that inducing a porosity on the electrode surface increases, both, the in vivo electrical signal and mechanical stability. This is a first study that illustrates possible advantages with respect to signal degradation.

We have designed and implemented an automated, just-in-time stimulation, seizure control method using a seizure prediction method from nonlinear dynamics coupled with deep brain stimulation in the centromedial thalamic nuclei in epileptic rats. A comparison to periodic stimulation, with identical stimulation parameters, was also performed. The two schemes were compared in terms of their efficacy in control of seizures, as well as their effect on synchronization of brain dynamics. The automated just-in-time (JIT) stimulation showed reduction of seizure frequency and duration in 5 of the 6 rats, with significant reduction of seizure frequency (>50%) in 33% of the rats. This constituted a significant improvement over the efficacy of the periodic control scheme in the same animals. Actually, periodic stimulation showed an increase of seizure frequency in 50% of the rats, reduction of seizure frequency in 3 rats and significant reduction in 1 rat. Importantly, successful seizure control was highly correlated with desynchronization of brain dynamics. This study provides initial evidence for the use of closed-loop feedback control systems in epileptic seizures combining methods from seizure prediction and deep brain stimulation.

Epileptic seizures are manifestations of intermittent spatiotemporal transitions of the human brain from chaos to order. Measures of chaos, namely maximum Lyapunov exponents (STL max ), from dynamical analysis of the electroencephalograms (EEGs) at critical sites of the epileptic brain, progressively converge (diverge) before (after) epileptic seizures, a phenomenon that has been called dynamical synchronization (desynchronization). This dynamical synchronization/desynchronization has already constituted the basis for the design and development of systems for long-term (tens of minutes), on-line, prospective prediction of epileptic seizures. Also, the criterion for the changes in the time constants of the observed synchronization/desynchronization at seizure points has been used to show resetting of the epileptic brain in patients with temporal lobe epilepsy (TLE), a phenomenon that implicates a possible homeostatic role for the seizures themselves to restore normal brain activity. In this paper, we introduce a new criterion to measure this resetting that utilizes changes in the level of observed synchronization/desynchronization. We compare this criterion’s sensitivity of resetting with the old one based on the time constants of the observed synchronization/desynchronization. Next, we test the robustness of the resetting phenomena in terms of the utilized measures of EEG dynamics by a comparative study involving STL max , a measure of phase (φ max ) and a measure of energy (E) using both criteria (i.e. the level and time constants of the observed synchronization/desynchronization). The measures are estimated from intracranial electroencephalographic (iEEG) recordings with subdural and depth electrodes from two patients with focal temporal lobe epilepsy and a total of 43 seizures. Techniques from optimization theory, in particular quadratic bivalent programming, are applied to optimize the performance of the three measures in detecting preictal entrainment. It is shown that using either of the two resetting criteria, and for all three dynamical measures, dynamical resetting at seizures occurs with a significantly higher probability (α=0.05) than resetting at randomly selected non-seizure points in days of EEG recordings per patient. It is also shown that dynamical resetting at seizures using time constants of STL max synchronization/desynchronization occurs with a higher probability than using the other synchronization measures, whereas dynamical resetting at seizures using the level of synchronization/desynchronization criterion is detected with similar probability using any of the three measures of synchronization. These findings show the robustness of seizure resetting with respect to measures of EEG dynamics and criteria of resetting utilized, and the critical role it might play in further elucidation of ictogenesis, as well as in the development of novel treatments for epilepsy.

In an effort to understand basic functional mechanisms that can produce epileptic seizures, we introduce some key features in a model of coupled neural populations that enable the generation of seizure-like events and similar dynamics with the ones observed during the route of the epileptic brain towards real seizures. In this model, modified from David and Friston’s neural mass model, an internal feedback mechanism is incorporated to maintain synchronous behavior within normal levels despite elevated coupling. Normal internal feedback quickly regulates an abnormally high coupling between the neural populations, whereas pathological internal feedback can lead to hypersynchronization and the appearance of seizure-like high amplitude oscillations. Feedback decoupling is introduced as a robust seizure control strategy. An external feedback decoupling controller is introduced to maintain normal synchronous behavior. The results from the analysis in this model have an interesting physical interpretation and specific implications for the treatment of epileptic seizures. The proposed model and control scheme are consistent with a variety of recent observations in the human and animal epileptic brain, and with theories from nonlinear systems, adaptive systems, optimization, and neurophysiology.

Gradient-type adaptive channel equalizers can suffer from burst error problems under sustained fading and in the absence of sufficient excitation. To overcome this problem, we propose an algorithm called the gradient-with-projection-on-bounding-ellipsoid (GPrOBE). The main idea in the proposed GPrOBE algorithm is that the equalizer parameters are computed by projecting the gradient estimate onto a set that is updated using a priori information on the instantaneous error magnitude. Simulation results show an improvement in the performance of the GPrOBE algorithm over other gradient-based algorithms in terms of reduced error bursts. Normalized error energy (NEE) performance analysis highlights that the GPrOBE algorithm yields solutions that are close to minimum mean-square-error (MMSE), while providing instantaneous performance guarantees during periods of insufficient excitation. Bit error rate (BER) experiments reveal that the GPrOBE algorithm is more robust in channels with sustained fading.

We have studied coupled neural populations in an effort to understand basic mechanisms that maintain their normal synchronization level despite changes in the inter-population coupling levels. Towards this goal, we have incorporated coupling and internal feedback structures in a neuron-level population model from the literature. We study two forms of internal feedback — regulation of excitation, and compensation of excessive excitation with inhibition. We show that normal feedback actions quickly regulate/compensate an abnormally high coupling between the neural populations, whereas a pathology in these feedback actions can lead to abnormal synchronization and "seizure"-like high amplitude oscillations. We then develop an external control paradigm, termed feedback decoupling, as a robust synchronization control strategy. The external feedback decoupling controller acts to achieve the operational objective of maintaining normal-level synchronous behavior irrespective of the pathology in the internal feedback mechanisms. Results from such an analysis have an interesting physical interpretation and specific implications for the treatment of diseases such as epilepsy. The proposed remedy is consistent with a variety of recent observations in the human and animal epileptic brain, and with theories from nonlinear systems, adaptive systems, optimization, and neurophysiology.

We discuss the key features of theoretical models of networks of coupled chaotic oscillators that produce seizure-like events and bear striking similarities to dynamics of epileptic seizures. Our long-term objective is to understand the basic functional mechanisms that can produce seizures and may ultimately lead to strategies for seizure suppression and control. We show that, from a dynamical systems point of view, a plausible cause of seizures is a pathological feedback in the brain circuitry. This suggests new seizure control approaches, as well as systematic methods to tune existing ones. While the suggested models and control approaches are far from being considered optimal for epileptic seizures, they have interesting physical interpretation and implications for treatments of epilepsy. They also have close ties with a variety of recent practical observations in the human and animal epileptic brain, and with theories from adaptive systems, optimization and chaos.

To understand basic functional mechanisms that cause epileptic seizures, the paper discusses some key features of theoretical brain functioning models. The hypothesis is put forward that a plausible reason for seizures is pathological feedback in brain circuitry. The analysis of such circuitry has an interesting physical interpretation and may be used to cure epilepsy.

We present a novel signal processing methodology to determine the direction and the strength of coupling between coupled nonlinear systems. The methodology is based on multivariate local linear prediction in the reconstructed state spaces of the observed variables from each multivariable nonlinear system. Application of the method is illustrated with systems of coupled Rossler and Lorenz oscillators in various coupling configurations. The obtained results are compared with ones produced by the use of the directed transfer function, a model-based method in the time domain. Through a surrogate analysis, it is shown that the proposed method is more reliable than the directed transfer function in identifying the direction and strength of the involved interactions.

The main motivation for this work was the need for a systematic design of high-performance controllers for paper machines. An integrated identification and controller design approach, based on loop-shaping principles, is presented. Starting with time-domain input-output data, a coprime factorization of the plant is identified using linear least-squares techniques. The residual error from the identification is then used to obtain estimates of the coprime factor uncertainty that are translated to sensitivity and complementary sensitivity bounds for the closed-loop system. These bounds serve as a guide for the selection of a target loop and the controller is designed using H-infinity tools. The application of this approach is demonstrated on Honeywell's high-fidelity simulator. The simulations demonstrate the suitability of the approach and illustrate that the technique can be used to reliably provide high performance for both servo and regulatory control.

Control strategies for sources in microgrids are pervasive in the literature. Droop control has received a great deal of attention as it requires no communications with other sources in the network. Droop control is typically synthesized by considering an entire network, implemented as a pair of constant gain controllers, and may suffer from poor performance and limited operating range. Since networks of sources can become quite large and are subject to change, a simplified approach to modeling and robust control are needed. In this paper H∞ and gain scheduled H∞ controllers are synthesized using Glover McFarlane loopshaping. It is shown that the H∞ approach produces superior robustness and performance compared to the traditional approach to droop control using constant gains. The H∞ and constant gain controllers are also compared using hardware.

Examines the performance of adaptive algorithms such as gradient, dead-zone, and bounding ellipsoid in the presence of bounded disturbances. The authors begin with a simple bursting example and continue by describing the algorithms and their properties, including analytical results on their asymptotic performance. Finally, a new algorithm is proposed, providing both MSE and maximum steady-state error performance guarantees.

A gap between theory and practice in signals and systems courses is often reported at many universities as a key problem in recruiting signals and systems students. On the other hand, instructors often cite a lack of fundamental understanding in mathematics as an issue in this course. Students seem to be discontent with some of the abstraction of the signals and systems courses. In this work-in-progress paper, we describe a new pathways concept we introduced to address these problems by introducing in-depth discussions, several applications and hands-on exercises.

Epilepsy is characterized by intermittent (aperiodical, sporadic), paroxysmal (abrupt onset and offset), hypersynchronous (simultaneous activation of a considerable neural mass) electrical activity, which may remain localized or spread and severely disrupt the brain's normal operation. Epileptic seizures and interictal spikes are typical manifestations of such activity, and are reminiscent of crises in nonlinear systems. It is only recently that prediction and control of epileptic seizures have been the subject of intensive interdisciplinary research. Within this context, we investigate epilepsy from the point of view of pathology of the dynamics of the electrical activity of the brain. Three critical aspects of the dynamics of epileptic seizures are discussed in this article: a) seizures are preceded by a long-term (on the order of minutes to hours) entrainment (synchronization of dynamics) of brain sites by the epileptogenic focus/zone; b) seizures typically reset the aforementioned dynamical entrainment and lead to the disentrainment (desynchronization of dynamics) of the focus from the rest of the brain; c) seizures may occur due to a failure of global or local internal feedback loops in the brain, normally responsible for keeping the spatial correlations/interactions within strict limits (time-wise and space-wise), and thus being able to support the brain's fast multi-processing and multi-task function. These aspects, taken together, constitute the basis for a dynamical interpretation of ictogenesis that may lead to the detection of seizure susceptibility periods, long-term prediction of seizures, epileptogenic focus localization, and potentially to the development of innovative treatments for epilepsy through timely interventions (e.g., electrical stimulation and/or in situ drug delivery) to control (ideally suppress) seizure occurrence. Preliminary evidence from dynamical analysis of EEG from patients with focal epilepsy and patients in status epilepticus, as well as from rodent and simulation models of epilepsy, supports the feasibility of these goals.

A recently proposed measure, namely Transfer Entropy (TE), is used to estimate the direction of information flow between coupled linear and nonlinear systems. In this study, we suggest improvements in the selection of parameters for the estimation of TE that significantly enhance its accuracy and robustness in identifying the direction of information flow and quantifying the level of interaction between observed data series from coupled systems. We demonstrate the potential usefulness of the improved method through simulation examples with coupled nonlinear chaotic systems. The statistical significance of the results is shown through the use of surrogate data. The improved TE method is then used for the study of information flow in the epileptic human brain. We illustrate the application of TE to electroencephalographic (EEG) signals for the study of localization of the epileptogenic focus and the dynamics of its interaction with other brain sites in two patients with Temporal Lobe Epilepsy (TLE).

Epileptic seizures are manifestations of intermittent spatiotemporal transitions of the human brain from chaos to order. In this paper, a comparative study involving a measure of chaos, in particular the short-term Lyapunov exponent (STLmax), a measure of phase (ϕmax) and a measure of energy (E) is carried out to detect the dynamical spatial synchronization changes that precede temporal lobe epileptic seizures. The measures are estimated from intracranial electroencephalographic (EEG) recordings with sub-dural and in-depth electrodes from two patients with focal temporal lobe epilepsy and a total of 43 seizures. Techniques from optimization theory, in particular quadratic bivalent programming, are applied to optimize the performance of the three measures in detecting preictal synchronization. It is shown that spatial synchronization, as measured by the convergence of STLmax, ϕmax and E of critical sites selected by optimization versus randomly selected sites leads to long-term seizure predictability. Finally, it is shown that the seizure predictability period using STlmax is longer than that of the phase or energy synchronization measures. This points out the advantages of using synchronization of the STlmax measure in conjunction with optimization for long-term prediction of epileptic seizures.

This paper presents a robust-control-oriented system identification method aiming to minimize the normalized coprime factor uncertainty of the performance-weighted system. The nominal model and the normalized coprime factor uncertainty bound are estimated employing a filter bank approach, which approximately calculates the chordal distance between the identified model and the true system in the frequency range of interest. An iterative LMI optimization is formulated to identify the model coefficients. The optimal stability margin is also calculated and compared to the identified nominal uncertainty bound to decide if re-identification is necessary.

This paper addresses issues arising in the online adaptation of PID controller parameters. Employing a frequency loop shaping approach to define the control objective, it is possible to adapt the PID parameters directly with standard least squares algorithms. In addition to this, the use of filter-banks in the regressor leads to the approximate minimization of the H-infinity norm of the error operator. The practical implication of this observation is that PIDs can be reliably tuned, even in cases of large mismatch between the target and the feasible loop shapes. Numerical examples are included, illustrating the properties and the implementation of the algorithm.

In this paper, we utilize a measure of brain dynamics, namely the short-term largest Lyapunov exponent (STLmax) to evaluate the efficacy of treatment in epileptic animals and humans with known antiepileptic drugs (AED) like diazepam and phenobarbital during status epilepticus (SE). This measure is estimated from analysis of electroencephalographic (EEG) recordings at multiple brain locations in both an SE patient and a cobalt/homocysteine thiolactone SE-induced animal. Techniques from optimization theory and statistics are applied to select optimal sets of brain sites, whose dynamics are then measured over time to study their entrainment/disentrainment. Results from such analysis indicate that the observed abnormal spatio-temporal dynamical entrainment in SE is reversed by AED administration (resetting of brain dynamics). These results may provide a potential use of nonlinear dynamical measures in the evaluation of the efficacy of AEDs and the development of new treatment strategies in epilepsy.

We consider a cortical neural population model in an effort to understand the basic mechanisms that regulate and maintain "normal" neural firing-rate despite changes in the cortical input level. Towards this goal, we have postulated the existence of internal feedback structures that functionally model firing-rate homeostasis via the balance of excitation and inhibition. In particular, normal internal feedback action maintains normal firing-rate by regulating the strength of excitatory input from pyramidal neurons in other cortical populations, and the strength of inhibitory input from interneurons. We observe that a pathology in the internal feedback that regulates the inhibitory input can lead to seizure-like high amplitude oscillations. These arise from two mechanisms, namely, excessive specific recurrent excitation between cortical populations, and excessive non-specific excitatory input. Further, we develop an external closed-loop control technique where the controller acts to achieve the operational objective of maintaining normal firing-rate. In addition to that, the external controller is also successful in avoiding the occurrence of "seizures". The results of this analysis are consistent with recent experimental observations in the epileptic brain, and have an interesting physical interpretation and implications for the treatment of disorders such as epilepsy.

In an effort to understand basic functional mechanisms that can produce epileptic seizures, and strategies for seizure suppression and control, we discuss some key features of theoretical models of networks of coupled chaotic oscillators that produce seizure-like events and bear striking similarities to dynamics of epileptic seizures. We show that a plausible cause of seizures is a pathological feedback in the brain circuitry. These results have interesting physical interpretation and implications for treatment of epilepsy. They also have close ties with a variety of recent practical observations in the human and animal epileptic brain, and with theories from adaptive systems, optimization, and chaos.

We propose a computationally attractive transform-domain gradient-with-projection-on-bounding-ellipsoid (G-PrOBE) algorithm. The main idea in the proposed frequency-domain G-PrOBE algorithm is that the system parameters are computed by projecting the gradient estimate onto a set that is updated using a priori information from the instantaneous error magnitude. Experimental results demonstrate that the transform-domain G-PrOBE algorithm is more robust towards burst errors and tends to exhibit smaller parameter variance relative to gradient-type algorithms especially under low signal excitation.

This paper presents a control-theoretic approach to the design of digital to analog converters and digital amplifiers leading to improved performance in audio and multimedia applications. The design involves oversampling and noise compression blocks as a pulse width modulation class-D amplifier, introduces an output-filter estimator block and applies a different modulation scheme. The theoretical model results in a family of digital circuits verified by software simulation and validated by a FPGA implementation with best performance 147 dB signal to noise ratio.

In this paper, we discuss the implementation of various controller performance monitoring criteria on a multivariable benchmark example. These criteria can be based on either time-domain or frequency-domain concepts and have different requirements for information content and algorithm complexity. The simulation results from the benchmark example are analyzed to derive conclusions on the consistency of each criterion to identify poorly tuned controllers. Typically, all methods generate consistent results with suitably designed tests and when the only source of performance deterioration is the controller tuning. However, in the presence of arbitrary external disturbances (e.g., large amplitude, deterministic) and varying levels of excitation, not all techniques yield consistent results. In this respect, criteria based on controller unfalsification concepts appear to be the more promising to yield reliable performance monitoring for operation in industrial environments.

The authors consider an application of adaptive techniques to control the space station in orbit with a moving payload. The nonadaptive counterpart of this problem has been considered by B. Wie et al. (1984). Such a controller, designed to tolerate constant parametric uncertainty, may exhibit unsatisfactory behavior when the variation of the system parameters is not sufficiently slow. Motivated by this practical problem, the theory and application of adaptive techniques in up-dating the parameters of the controller designed by Wie et al. is discussed. The main theoretical problem arising in this application is associated with the adaptive identification and control of multivariable linear time-varying systems. Other theoretical problems arise from the decentralized nature of the controller of Wie et al., and a hybrid implementation of the adaptive control scheme to reduce the required computational effort. Such problems have been studied individually in the literature, and are integrated in the present study to provide a complete theoretical justification of the adaptive approach to the application at hand. >

The author considers the adaptive control problem of linear, time-varying single-input, single-output plants, with emphasis on adaptive laws employing the so-called sigma -modification. He derives a sufficient condition which guarantees signal boundedness for adaptive laws using the sigma -modification and normalization. Effectively, this condition relates the speed and the range of the plant parameter variations with the value of sigma and provides at least qualitative guidelines for the selection of the design parameters in the adaptive law. >

J-DSP is a Java-based object-oriented programming environment that was developed at Arizona State University for use in the undergraduate DSP class [A. Sapnias et al., June 2000]. In this paper, we describe innovative software extensions on J-DSP to accommodate on-line laboratories for speech processing, image processing, and communications systems. Significant modifications in the object-oriented GUI of J-DSP that enable simulation of feedback systems are also presented. The speech processing functions enable on-line simulations of speech coding algorithms and include PCM and ADPCM quantization as well as more elaborate algorithms such as the LPC and the CELP. Image processing functionalities include development of 2-D signal processing capabilities including 2-D-FFT, 2-D-filter design, and 2-D graphics and picture processing. Communications functionality covers several aspects of analog and digital modulation and demodulation. On-line laboratory exercises have been developed in the aforementioned areas and posted on a web site (http://jdsp.asu.edu). This site also includes on-line evaluation forms for the exercises. Statistical and qualitative evaluations that assess the learning experiences of the students that use J-DSP are presented.

The application of optimal control theory to the process of low pressure chemical vapor deposition on patterned surfaces can substantially decrease the processing time for a given step coverage, compared with the programmed rate chemical vapor deposition (PRCVD) process. The control model is developed from the simultaneous 1D Knudsen diffusion and chemical reaction description. For such a model, the optimal control problem is formulated as to find a temperature trajectory yielding the minimum processing time and its solution is computed numerically via a modified variation of extremals method. For the thermally activated deposition of silicon dioxide from tetraethylorthosilicate (TEOS) and for a ninety-six percent step coverage, the optimal control-generated temperature trajectory results in time-savings of approximately 28 percent, when compared to the PRCVD approach.

In this paper, a reduced order, physically-motivated empirical model is proposed and validated via simulation for the single wafer tungsten low pressure chemical vapor deposition (LPCVD) processing step. The so-called multiple response surface method is adopted to describe the spatial deposition nonuniformity across a wafer surface. Based on this modeling methodology, a simple adaptive optimization control strategy is developed by which the average deposition thickness at the wafer surface is controlled to a desired level while its variation of the state across the wafer surface is minimized. Simulation results demonstrate the effectiveness of the control strategy and its potential capability of rejecting disturbances during the process. In this study, a simulation platform (CFDSWR) is used to represent the single wafer tungsten LPCVD process. The control strategy introduced here is quite general and applicable to other processing steps as well.

In this paper, we describe the gradient-with-projection-on-optimal-bounding-ellipsoid (G-Probe) algorithm, for use in channel equalization. The G-Probe is compared to the NLMS-based equalizer in the case where the transmitted signal is attenuated by strong and periodic fading, and the channel undergoes structured interference at the same time. In such scenarios small perturbations in the channel may cause the NLMS parameters to drift, leading to large instantaneous errors referred to as burst phenomena. The G-Probe provides both MSE and instantaneous steady-state error performance guarantees resulting in reduced error bursts. This paper provides simulation results in the performance of G-Probe for a linear ISI channel with AWGN and its performance in a channel with periodic fading. Finally, the paper provides comparison results of G-Probe against three adaptive algorithms; the OBE, NLMS, and RLS.

This paper presents various on-line computer laboratory exercises and related assessment results for Java-Digital Signal-Processing-Controls (J-DSP-C). J-DSP- C is an object oriented simulation environment that is intended for control systems simulations. All J-DSP-C component functions appear as graphical blocks that can be connected to form a variety of educational controls simulations. Dynamical systems with various interconnection topologies can be developed and simulated while results can be examined graphically. J-DSP-C has been designed to be easily accessible through the internet with any computer system equipped with a Java -enabled browser.

An integrated identification and controller design approach, based on loop-shaping principles, is presented. Starting with time-domain input-output data, a coprime factorization of the plant is identified using linear least-squares techniques. The residual error from the identification is then used to obtain estimates of the coprime factor uncertainty that are translated to sensitivity and complementary sensitivity bounds for the closed-loop system. These bounds serve as a guide for the selection of a target loop and the controller is designed using H/sub /spl infin// tools. An application on a paper machine simulator is also presented, illustrating the main ideas of the approach.

This paper compares three different uncertainty estimation techniques in terms of predicting a priori performance bounds on the closed-loop system. A major benefit of computing the performance bounds is they expedite the controller implementation process, which is a critical component in the acceptance of the control system by the process operators. A key ingredient in computing these bounds is uncertainty estimation. The three methods evaluated are the coprime factor uncertainty method, Zhu's asymptotic method (1989), and the frequency-domain method of Bayard and Yam (1994). We compare the performance bounds obtained by applying these uncertainty estimation procedures on a paper machine control case study.

The performance of adaptive FIR algorithms in the presence of bounded disturbances, in particular, algorithms based on gradient mean squared error (MSE) estimators, gradient estimators with dead-zone thresholds, and set membership estimators, is examined. A simple example of the bursting phenomenon is given, and the algorithms and their properties are described. Analytical results on the performance of these estimators are also given. An algorithm that provides both MSE and instantaneous maximum amplitude error (in the limsup sense) performance guarantees is proposed. >

In this paper we address the adaptive and nonadaptive model reference control problem for a class of linear time-varying plants, namely the index-invariant ones. We show that, under appropriate controllability and observability conditions, this class of plants admits a fractional description in terms of polynomial differential operators and, as such, allows for a polynomial equation-based controller design. We also show that, for a control objective of the model reference type, the controller can be designed by solving a set of algebraic equations. Further, in the case where the plant parameters are only partially known, we employ a gradient-based adaptive law with projection and normalization to update the controller parameters and establish the stability and tracking properties of adaptive closed-loop plant. >

We present the application of optimal control theory to the process of low pressure chemical vapor deposition (LPCVD) on patterned surfaces. An optimally controlled CVD protocol is developed by employing an approximate feature scale model, based on the simultaneous one-dimensional Knudsen diffusion and chemical reaction description of LPCVD, for the specific problem of maximizing throughput for a specified step coverage. The corresponding control conditions for the reactor are obtained by an adaptive solution of the inverse problem for the reactor scale model. Rigorous process simulations demonstrate that the computed temperature and partial pressure trajectories provide good step coverage.

A frequency loop-shaping technique is presented for tuning proportional-integral-derivative controllers. In this approach, the controller parameters are tuned so as to minimize the difference between the actual and a target loop transfer function, in the sense of the /spl Hscr//sub /spl infin// norm. The simple structure of these controllers allows the formulation of the loop-shaping objective as a convex optimization problem that can be solved numerically in a reliable way. Several examples are presented to illustrate the application of the proposed tuning technique and compare it with existing ones.

We introduce a novel modeling technique, in an effort to develop a physically-motivated empirical model of the deposition rate and spatial deposition nonuniformity for a single-wafer tungsten silicide low pressure chemical vapor deposition (LPCVD) processing step. In general, such a description is difficult to obtain due to the complexity of the mapping between the spatial nonuniformity and manipulated variables. Combining the so-called multiple response surface method with a "feedback-like" model structure, relating reactant partial pressures with manipulated variables, we develop a reasonably accurate description of the deposition rate and spatial deposition nonuniformity across wafer surface. This model offers good fitting accuracy with fewer adjustable parameters compared with the more traditional polynomial-structure models. Further, based on this modeling methodology, we derive a run-to-run adaptive control/optimisation strategy aiming to achieve prescribed values of the average deposition rate and silicon to tungsten ratio at the wafer surface, while minimizing the variation of the deposition rate across the wafer surface.

In this paper, Optimal Control theory is applied to develop an alternative process protocol in single wafer reactor LPCVD on patterned wafer in an effort to minimize the processing time, for given final step coverage. To achieve this, the operating conditions are changed during the deposition in a prescribed manner. A simplified control model is developed from the simultaneous one-dimensional Knudsen diffusion and chemical reaction description. The optimal control problem is formulated to find a temperature trajectory yielding the minimum processing time and its solution is computed numerically via a modified variation of extremals method. To demonstrate the concept of optimal control CVD (OCCVD), we consider the thermally activated deposition of silicon dioxide (SiO/sub 2/) from tetraethylorthosilicate (TEOS). Using the simplified control model, the estimated process time to achieve a 96% step coverage at 98% closure with the constant rate CVD (CRCVD) strategy is 729 seconds. Under the same conditions, the optimal control CVD (OCCVD) process time is 278 seconds. Compared to CRCVD, the process time saved with OCCVD is 62%.

This paper presents some simple criteria to monitor the performance of multivariable controllers. Their derivation is based on the small gain theorem using estimates of the uncertainty in the process model. Large values of such estimates indicate the need for controller retiming. Encouraging preliminary results have been obtained from applications to simulated data. Although adequate excitation is necessary to allow conclusive statements, the excitation requirement is quite modest compared to system identification.

An integrated modeling and robust multivariable control approach is presented for paper machines. An application of this approach is demonstrated on a high-fidelity simulator. Modeling relies on input-output data, collected from an identification experiment at the desired operating condition. An estimate of the process model along with the uncertainty bounds that describe the confidence limits of the model, consistent with the robust control theory, is obtained. The results are used to design an multivariable controller based on loop-shaping principles and guided by the estimated uncertainty bounds. The simulations demonstrate the suitability of the approach and illustrate that the technique can be used to provide high bandwidth performance for both servo and regulatory control.

A control-oriented identification and uncertainty estimation approach from input-output data is presented, for use in the design of control systems for paper machines. The application of this approach is demonstrated on a high fidelity simulator. An estimate of the process model along with the uncertainty bounds that describe the confidence limits of the model, consistent with the robust control theory, is obtained. These results can then be used to design a multivariable controller based on loop-shaping principles and guided by the estimated uncertainty bounds. The simulations demonstrate the suitability of the approach and illustrate that the technique can be used to provide high bandwidth performance for both servo and regulatory control.

A framework for integrated identification and robust PID control for multivariable processes is presented. The method relies on robust loop shaping bounds computed directly from identification data using the concept of the structured singular value (/spl mu/). These performance bounds are then used to define the nominal loop shape required to tune PID controllers.

The purpose of this note is to discuss certain aspects of the identification and control problems of single-input single-output (SISO), linear time-varying (LTV) plants. An input-output (I/O) framework is adopted whereby the LTV plant is described in terms of differential operators. Such a description provides a factorization of the I/O operator of the LTV plant which is convenient for parametric identification and design of 'pole-placement'-type controllers.

In this note we address the problem of improving the performance of a class of Model Reference Adaptive Controllers (MRAC) under insufficient excitation and in the presence of perturbations, e.g., non-parametric uncertainty and bounded disturbances. In such an environment, the global boundedness of a MRAC closed-loop (BIBO stability) has been established, without any excitation requirements, for both bounded disturbances and `small' non-parametric uncertainty (e.g., see [1, 2] and references therein). In addition to this form of robust stability, the adaptive controller also guarantees robust tracking performance in a root-mean-square (RMS) sense. That is, the RMS value of the normalized tracking error is of order of the size of the non-parametric uncertainty and/or the RMS value of the external disturbances. Unfortunately, however, this weak performance measure is rarely reliable in assessing the effectiveness of the adaptive controller. Indeed, in the absense of sufficient excitation, even arbitrarily small perturbations may cause the adjustable parameters to drift away from their desired or actual values. Such a parameter drift can then produce large instantaneous tracking errors due to either a change in the excitation signal or the (local) destabilization of the closed loop system. This type of undesirable behavior, often referred to as burst phenomena, has been the subject of several studies (e.g., see [3, 4, 5, 6]). A typical remedy of the bursting problem has been recognized in the form of absolute or relative dead-zones [7, 8].

In this note we consider the adaptive control problem of linear, time varying, single-input singl-output plants with parameter discontinuities ("jump" parameter variations). In our formulation, the plant parameters are assumed to be uniformly bounded and piecewise continuous functions of time. Inside the intervals of continuity, the plant is assumed to satisfy some general conditions on observability, controllability and zero dynamics and the parameters are assumed to be smooth (or at least Lipschitz) but their speed of variation is not necessarily small. Under these, quite general, conditions it is established that a control law of the model reference type [5] which, in the case of know parameters, guarantees exponential and therefore internal stability of the closed loop, provided that in any time interval the average number of parameter discontinuities is small enough. In the case of partially unknown plant parameters, this control law is combined with an estimator. The resulting adaptive controller guarantees boundedness of the closed loop for bounded reference signals provided that: 1. in any time interval, the average number of parameter discontinuities is small enough and 2. inside the intervals of continuity, the functional dependence on time (or structure) of the fast time varying parameters is known up to a slowly varying component. The currently available results on the adaptive control problem of time-varying plants have been derived under stronger assumptions, typically requiring that the plant parameters are smooth and slowly time-varying [1,2,3] or piecewise continuous and slowly varying in the mean [4].