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The machining process is very important in many engineering applications. In high precision machining, surface finish is strongly correlated with vibrations and the dynamic interactions between the part and the cutting tool. Parameters affecting these vibrations and dynamic interactions, such as spindle speed, cut depth, feed rate, and the part's material properties can vary in real time, resulting in unexpected or undesirable effects on the surface finish of the machining product. The focus of this research is the development of an improved machining process through the use of active vibration damping. The tool holder employs a high-bandwidth piezoelectric actuator with an adaptive positive position feedback control algorithm for vibration and chatter suppression. In addition, instead of using external sensors, the proposed approach investigates the use of a collocated piezoelectric sensor for measuring the dynamic responses from machining processes. The performance of this method is evaluated by comparing the surface finishes obtained with active vibration control versus baseline uncontrolled cuts. Considerable improvement in surface finish (up to 50%) was observed for applications in modern day machining. [DOI: 10.1115/1.4001498]

Online condition monitoring and diagnosis systems play an important role in the modern manufacturing industry. This paper presents a novel method to diagnose the degradation processes of multiple failure modes using a modified hidden Markov model (MHMM) with variable state space. The proposed MHMM is combined with statistical process control to quickly detect the occurrence of an unknown fault. This method allows the state space of a hidden Markov model to be adjusted and updated with the identification of new states. Hence, the online degradation assessment and adaptive fault diagnosis can be simultaneously obtained. Experimental results in a turning process illustrate that the tool wear state can be successfully detected, and previously unknown tool wear processes can be identified at the early stages using the MHMM. [DOI: 10.1115/1.4001247] Keywords: hidden Markov model, online degradation assessment, adaptive fault detection

The main issue in a surveillance environment is the target tracking. The most important concern in this problem is the association of the various measurements with the existing target tracks. The fuzzy c-means data association (FCMDA) algorithm, based on the fuzzy c-means (FCM) algorithm, is an efficient solution for the problem of measurement to track association in a multisensor multitarget environment. It has a high accuracy in measurement to track association when targets are far from each other. However, its accuracy remains low when targets are close to one another. The FCMDA algorithm performance is usually lost in this environment, especially when measurement noise is high. In the FCMDA algorithm, the association between measurements and tracks is determined using an optimal membership function derived from the FCM algorithm for the fixed predicted state of targets. The prediction of the target state deviates from its correct value based on updating the tracker/filter with the wrong associated measurement. Consequently, the wrong association can take place using a deviated prediction of target state in the FCMDA algorithm. In this paper, to overcome this shortcoming of the FCMDA algorithm, the predicted state of every target in a surveillance environment is compensated for the effect of wrong associated measurement by an adaptive neurofuzzy inference system (ANFIS). An ANFIS has both the advantages of expert knowledge of a fuzzy inference system and the learning capability of neural networks. So a trained ANFIS is able to compensate the effect of a wrong associated measurement on the prediction of target state. Using the compensated prediction of target state in the FCMDA algorithm can always save the performance of the FCMDA algorithm and extend its domain of usage in practical applications. The simulation results demonstrate that considerable improvements in terms of accuracy and performance are achieved by using the compensated prediction of target state in the FCMDA algorithm. [DOI: 10.1115/1.4000663] Keywords: measurement to track association, fuzzy c-means data association (FCMDA) algorithm, adaptive neurofuzzy inference system (ANFIS)

In this paper, an adaptive controller is developed for the pressure tracking of the pressurized toolhead in order to maintain the constant contact stress for the polishing process. This is a new polishing control method, which combines the adaptive control theory and the constant stress theory of the contact model. By using an active pneumatic compliant toolhead, a recursive least-squares estimator is developed to estimate the pneumatic model and then a minimum-degree pole-placement method is applied to design a self-tuning controller. The simulation and experiment results of the proposed controller are presented and discussed. The main advantage of the constant contact stress control is high figuring accuracy. [DOI: 10.1115/1.4000959]

This brief paper presents a novel localization algorithm, named discriminant-adaptive neural network (DANN), which takes the received signal strength (RSS) from the access points (APs) as inputs to infer the client position in the wireless local area network (LAN) environment. We extract the useful information into discriminative components (DCs) for network learning. The nonlinear relationship between RSS and the position is then accurately constructed by incrementally inserting the DCs and recursively updating the weightings in the network until no further improvement is required. Our localization system is developed in a real-world wireless LAN WLAN environment, where the realistic RSS measurement is collected. We implement the traditional approaches on the same test bed, including weighted k-nearest neighbor (WKNN), maximum likelihood (ML), and multilayer perceptron (MLP), and compare the results. The experimental results indicate that the proposed algorithm is much higher in accuracy compared with other examined techniques. The improvement can be attributed to that only the useful information is efficiently extracted for positioning while the redundant information is regarded as noise and discarded. Finally, the analysis shows that our network intelligently accomplishes learning while the inserted DCs provide sufficient information. Index Terms--Adaptive, discriminant analysis, location fingerprinting, neural network, wireless local area network (WLAN).

A space-time adaptive decision feedback (DF) receiver using recurrent neural networks (RNNs) is proposed for joint equalization and interference suppression in direct-sequence code-division multiple-access (DS-CDMA) systems equipped with antenna arrays. The proposed receiver structure employs dynamically driven RNNs in the feedforward section for equalization and multiaccess interference (MAI) suppression and a finite impulse response (FIR) linear filter in the feedback section for performing interference cancellation. A data selective gradient algorithm, based upon the set-membership (SM) design framework, is proposed for the estimation of the coefficients of RNN structures and is applied to the estimation of the parameters of the proposed neural receiver structure. Simulation results show that the proposed techniques achieve significant performance gains over existing schemes. Index Terms--Adaptive receivers, direct-sequence code-division multiple-access (DS-CDMA), multiuser detection, neural networks, set-membership (SM) techniques, space-time processing.

Within the model reference adaptive control framework, a simple adaptive output-feedback control scheme is developed for a class of linear single-input single-output dynamic systems with multiple state delays which is robust with respect to multiple unknown time-varying plant delays and to an external disturbance with unknown bounds. A special form of the Lyapunov-Krasovskii functional with "virtual" adaptation gain is introduced to prove stability. Index Terms--Robust adaptive control, time-delay systems.

A spatial periodic adaptive control (SPAC) approach is developed to deal with rotary machine systems performing speed tracking tasks. Since the angular displacement is periodic when rotating by 2[pi] radians, most rotary machine systems present certain cyclic behaviors with a fixed periodicity which is either a fraction or multiple of 2[pi]. As a consequence, unknown system parameters and disturbances that characterize the system behaviors are also periodic in nature. By utilizing the spatial periodicity, the SPAC aims at improving the system performance. In the SPAC design, the dynamics of the rotary machine systems is first converted from the temporal to spatial coordinates in canonical form using the feedback linearization method. Then the new adaptive controller updates the parameters and the control signal periodically in a pointwise between two consecutive spatial cycles. Using a Lyapunov-Krasovskii functional, the convergence property of the SPAC can be analyzed for high order rotary systems and the periodic adaptation can be applied to rotary systems with pseudo-periodic parametric uncertainties. The effectiveness of the SPAC is verified through rigorous analysis and two numerical examples. Index Terms--Feedback linearization, Lyapunov-Krasovskii functional, periodic adaptation, rotary machine systems, spatial adaptive control.

Gigabit and multigigabit transceivers require very long adaptive filters for echo and near-end crosstalk (NEXT) cancellation. Implementation of these filters not only occupies large silicon area but also consumes significant power. These problems become even worse when the Tomlinson-Harashima precoding (THP) technique is used in applications such as 10-Gigabit Ethernet over Copper (10GBASE-T) as the input to the echo and NEXT cancellers is no longer a simple PAM-M signal. To reduce the complexity of these cancellers, in this paper, a novel method based on word-length reduction technique is proposed. The proposed design is derived by replacing the original input to the echo and NEXT cancellers with a finite-level signal, which is the sum of the input to the TH precoder and a finite-level compensation signal. Then, this modified input signal is recoded to have shorter word-length compared with the original input. Hence, the overall complexity can be reduced by using the proposed method. To further reduce the complexity of these cancellers, an improved design is proposed by exploiting the property of the compensation signal. Compared with the traditional design, the proposed echo and NEXT cancellers have exact input and do not suffer from the quantization problem, and thus they are more suitable for VLSI implementation. The proposed method can also be applied to design adaptive echo and NEXT cancellers with little modification. The performance evaluation is performed by simulations to verify the proposed design. It is shown that, by applying the proposed method to a 10GBASE-T ethernet system, the hardware complexity of echo and NEXT cancellers can be reduced by about 10.82% without performance loss, compared with the traditional design. Index Terms--Adaptive filter, echo and NEXT cancellers, low complexity, Tomlinson-Harashima precoding (THP), word-length reduction.

Current analog harmonic rejection mixers typically provide 30-40 dB of harmonic rejection, which is often not sufficient. We present a mixed analog-digital approach to harmonic rejection mixing that uses a digital interference canceler to reject the strongest interferer. Simulations indicate that, given a practical RF scenario, the digital canceler is able to improve the signal-to-interference ratio by 30-45 dB. Index Terms--Adaptive filter, CMOS, cognitive radio, gain and phase mismatch, harmonic rejection mixing, I/Q imbalance, interference canceling, LMS, software-defined radio, switching mixers.

A novel analog integrated circuit implementation of an adaptive fuzzy logic controller (AFLC), called variable universe fuzzy logic controller (VFLC), is presented which has not been reported before. The VFLC is a stable controller which has fewer on-line adapting parameters than the conventional AFLCs based on adapting fuzzy rules, thus it is more suitable for hardware implementation. The input and output universes of discourse of the VFLC are adaptively changed according to the input variables to improve the control effect. A novel peaky-triangle membership function is presented to realize the complex input universe variation. The absolute value of the integral of the input variables is used for the output universe variation, and then it is multiplied with the output of the conventional fuzzy logic controller to form the final output. The other parts are minimization circuits and a center of gravity defuzzification circuit that does not use a division circuit. An analog VFLC with 2 inputs, 1 output, and 9 rules is designed and fabricated using a 0.6-[micro]m CMOS standard technology. It can work either in a non-adaptive or an adaptive mode. The measurements show that it completes the VFLC functions. Index Terms--Adaptive control, analog circuits, fuzzy control.

An adaptive Fourier neural network (AFNN) control scheme is presented in this paper for the control of a class of uncertain nonlinear systems. Based on Fourier analysis and neural network (NN) theory, AFNN employs orthogonal complex Fourier exponentials as the activation functions. Due to the clear physical meaning of the neurons, the determination of the AFNN structure as well as the parameters of the activation functions becomes convenient. One salient feature of the proposed AFNN approach is that all the nonlinearities and uncertainties of the dynamical system are lumped together and compensated online by AFNN. It can, therefore, be applied to uncertain nonlinear systems without any a priori knowledge about the system dynamics. Derived from Lyapunov theory, a novel learning algorithm is proposed, which is essentially a frequency domain method and can guarantee asymptotic stability of the closed-loop system. The simulation results of a multiple-input-multiple-output (MIMO) nonlinear system and the experimental results of an X--Y positioning table are presented to show the effectiveness of the proposed AFNN controller. Index Terms--Adaptive Fourier neural network (AFNN), feedback control, frequency domain, orthogonal activation function, uncertain nonlinear system.

Many engineering systems are required to operate under changing operating conditions. A special class of systems called adaptive systems has been proposed in the literature to achieve high performance under changing environments. Adaptive systems acquire this powerful feature by allowing their design configurations to change with operating conditions. In the optimization of the adaptive systems, designers are often required to select (i) adaptive and (ii) nonadaptive (or fixed) design variables of the design configuration. Generally, the selection of these variables and the optimization of adaptive systems are performed sequentially, thus being a source of suboptimality. In this paper, we propose the Selection-Integrated Optimization (SIO) methodology, which integrates the two key processes: (1) the selection of the adaptive and fixed design variables and (2) the optimization of the adaptive system, thereby eliminating a significant source of suboptimality from adaptive system optimization problems. A major challenge to integrating these two key processes is the selection of appropriate fixed and adaptive design variables, which is discrete in nature. We propose the Variable-Segregating Mapping-Function (VSMF), which overcomes this challenge by progressively approximating the discreteness in the design variable selection process. This simple yet effective approach allows the SIO methodology to integrate the selection and optimization processes and helps avoid one significant source of suboptimality from the optimization procedure. The SIO methodology finds its applications in a variety of other engineering fields, such as product family optimization. However, in this paper, we limit the scope of our discussion to adaptive system optimization. The effectiveness of the SIO methodology is demonstrated by designing a new air-conditioning system called Active Building Envelope (ABE) system. [DOI: 10.1115/1.2965365]

The use of a neural network (NN) as a feedforward control element to compensate for nonlinear system uncertainties has been investigated for over a decade. Typical NN-based controllers yield uniformly ultimately bounded (UU-B) stability results due to residual functional reconstruction inaccuracies and an inability to compensate for some system disturbances. Several researchers have proposed discontinuous feedback controllers (e.g., variable structure or sliding mode controllers) to reject the residual errors and yield asymptotic results. The research in this paper describes how a recently developed continuous robust integral of the sign of the error (RISE) feedback term can be incorporated with a NN-based feedforward term to achieve semi-global asymptotic tracking. To achieve this result, the typical stability analysis for the RISE method is modified to enable the incorporation of the NN-based feedforward terms, and a projection algorithm is developed to guarantee bounded NN weight estimates. Index Terms--Adaptive control, asymptotic stability, Lyapunov methods, neural network, nonlinear systems, RISE feedback, robust control.

A novel robust adaptive controller for multi-input multi-output (MIMO) feedback lincarizable nonlinear systems possessing unknown nonlinearities, capable of guaranteeing a prescribed performance, is developed in this paper. By prescribed performance we mean that the tracking error should converge to an arbitrarily small residual set, with convergence rate no less than a prespecified value, exhibiting a maximum overshoot less than a sufficiently small prespecified constant. Visualizing the prescribed performance characteristics as tracking error constraints, the key idea is to transform the "constrained" system into an equivalent "unconstrained" one, via an appropriately defined output error transformation. It is shown that stabilization of the "unconstrained" system is sufficient to solve the stated problem. Besides guaranteeing a uniform ultimate boundedness property for the transformed output error and the uniform boundedness for all other signals in the closed loop, the proposed robust adaptive controller is smooth with easily selected parameter values and successfully bypasses the loss of controllability issue. Simulation results on a two-rink robot, clarify and verify the approach. Index Terms--Neural networks, prescribed performance, robust adaptive control.

This paper proposes a novel dynamic structure neural-fuzzy network (DSNFN) via a robust adaptive sliding-mode approach to address trajectory-tracking control of an n-link robot manipulator. In the DSNFN, a five-layer neural-fuzzy network (NFN) is used to model complex processes and compensate for structured and unstructured uncertainties. However, it is difficult to find a suitable-sized NFN to achieve the required approximation error. To deal with the mentioned problem, the number of rule nodes in the DSNFN can be either increased or decreased over time based on the tracking errors, and the adaptation laws in the sense of a projection algorithm are derived for tuning all parameters of the parameterized NFN. Using DSNFN, good tracking performance could be achieved in the system. Furthermore, the trained network avoids the problems of overfitting and underfitting. The global stability and the robustness of the overall control scheme are guaranteed, and the tracking errors converge to the required precision by the Lyapunov synthesis approach. Experiments performed on a two-link robot manipulator demonstrate the effectiveness of our scheme. Index Terms--Adaptive tuning algorithm, dynamic structure neural-fuzzy network (DSNFN), robot manipulator, stability and robustness.

To adapt the data rate in accordance with the quality of the link, the IEEE 802.11 standard proposes the variable rate shifting functionality. This intrinsic functionality of the 802.11 products progressively degrades the bit rate when a host detects unsuccessful frame transmissions. Furthermore, the basic CSMA/CA channel access method guarantees that the long-term channel access probability is equal for all hosts. When one host captures the channel for a long time because its bit rate is low, it penalizes other hosts that use the higher rate, inciting a performance overall degradation. We propose a self-adapting algorithm to improve resources shared among the active hosts in the hot spot, thus avoiding the performance degradation caused by one or more low-rate hosts. This algorithm aims at avoiding this performance degradation and the consequent waste of bandwidth. An extensive evaluation is performed depicting the improvement achieved. Keywords: Fair time sharing; 802.11; Self-adapting

Autonomic Computing is a concept that brings together many fields of computing with the purpose of creating computing systems that self-manage. In its early days it was criticised as being a "hype topic" or a rebadging of some Multi Agent Systems work. In this survey, we hope to show that this was not indeed 'hype' and that, though it draws on much work already carried out by the Computer Science and Control communities, its innovation is strong and lies in its robust application to the specific self-management of computing systems. To this end, we first provide an introduction to the motivation and concepts of autonomic computing and describe some research that has been seen as seminal in influencing a large proportion of early work. Taking the components of an established reference model in turn, we discuss the works that have provided significant contributions to that area. We then look at larger scaled systems that compose autonomic systems illustrating the hierarchical nature of their architectures. Autonomicity is not a well defined subject and as such different systems adhere to different degrees of Autonomicity, therefore we cross-slice the body of work in terms of these degrees. From this we list the key applications of autonomic computing and discuss the research work that is missing and what we believe the community should be considering. Categories and Subject Descriptors: A.1 [Introductory and Survey] General Terms: Design, Performance, Reliability. Additional Key Words and Phrases: Autonomic computing, self-adaptive, self-healing systems ACM Reference Format: Huebscher, M. C. and McCann, J. A. 2008. A survey of autonomic computing--degrees, models, and applications. ACM Comput. Surv., 40, 3, Article 7 (August 2008), 28 pages DOI = 10.1145/1380584.1380585 http://doi.acm.org/10.1145/1380584.1380585

This paper presents a novel approach for image-based visual servoing of a robot manipulator with an eye-in-hand camera when the camera parameters are not calibrated and the 3-D coordinates of the features are not known. Both point and line features are considered. This paper extends the concept of depth-independent interaction (or image Jacobian) matrix, developed in earlier work for visual servoing using point features and fixed cameras, to the problem using eye-in-hand cameras and point and line features. By using the depth-independent interaction matrix, it is possible to linearly parameterize, by the unknown camera parameters and the unknown coordinates of the features, the closed-loop dynamics of the system. A new algorithm is developed to estimate unknown parameters online by combining the Slotine-Li method with the idea of structure from motion in computer vision. By minimizing the errors between the real and estimated projections of the feature on multiple images captured during motion of the robot, this new adaptive algorithm can guarantee the convergence of the estimated parameters to the real values up to a scale. On the basis of the nonlinear robot dynamics, we proved asymptotic convergence of the image errors by the Lyapunov theory. Experiments have been conducted to demonstrate the performance of the proposed controller. Index Terms--Adaptive control, eye-in-hand, uncalibrated, visual servoing.

Most physical problems contain variations in scale; this holds true for the area of electromagnetic field computation. For this reason, research is ongoing to construct adaptive discretization techniques. The focus of this paper lies in the investigation of an adaptive refinement strategy based on the multigrid technique. Such adaptive discretizations are required, for instance, for the efficient calculation of 3-D space charge effects of bunches of charged particles. The refinement strategy under investigation here employs the [tau]-criterion. Index Terms--Adaptive mesh, particle beam dynamics, space charge.

A novel parallel formulation for meshless adaptive finite element analysis is developed and investigated. The method is based on an integrated interpretation of conventional meshless theory and the generalized irregular-cut formulation for triangles and tetrahedra. The new formulation provides a near orthogonal hierarchal relationship among local basis functions, and supports the virtually unrestricted introduction and refinement of localized modeling degrees of freedom in a discretization. The formulation is intended for h-p adaptive refinements, and is well suited to high-performance parallel and distributed computing implementations. Initial test implementations have been purpose-built to investigate the main issues and performance characteristics of adaptive finite element analysis applications in electromagnetics, for large-scale computations on the CLUMEQ Supercomputer Centre facilities at McGill University, Montreal, QC, Canada. Numerical results based on 1-D, 2-D, and 3-D benchmark analyses verify the correctness of the new formulation and illustrate the potential performance of the implementations. Index Terms--Adaptive systems, electromagnetic analysis, finite element methods (FEMs), parallel processing.

In this paper, we propose an adaptive scheme for the stabilization and synchronization of chaotic Lur'e systems with time-varying delay. Based on the invariant principle of functional differential equations, the strength of the feedback controller is enhanced adaptively to stabilize and synchronize chaotic Lur'e systems. The derivative-constraint that the time-varying delay is required to be differentiable and its derivation is less than one can be removed by using LaSalle-Razumikhin-type theorems. The time-varying delay is allowed to be bounded without any additional constraint or unbounded with derivative-constraint. This method is analytical, rigorous and simple to implement in practice. In addition, it is quite robust against the effect of parameters uncertainty and noise. Two examples are provided to show the effectiveness of the proposed scheme. The results of the paper demonstrate the fruitfulness of the modern feedback and adaptive control theory application to the stabilization and synchronization problems for delayed chaotic systems. Index Terms--Adaptive control, chaos control, chaotic systems, Lur'e systems, synchronization, time-varying delay.

Although the convergence behavior of gradient-based adaptive algorithms, such as steepest descent and leas mean square (LMS), has been extensively studied, the influence of the desired response on the transient convergence has generally received little attention. However, empirical results show that this signal can have a great impact on the learning curve. In this paper we analyze the influence of the desired response on the transient convergence by making a novel interpretation, from the viewpoint of the desired response, of previous convergence analyses of SD and LMS algorithms. We show that, without prior knowledge that can be used to wisely select the initial weight vector, initial convergence is fast whenever there is high similarity between input and desired response whereas, on the contrary, when there is low similarity between these two signals, convergence is slow from the beginning. Index Terms--Adaptive filters, adaptive signal processing, convergence, gradient methods, least-mean-square (LMS) methods.

In this paper, we propose a multiresolution approach for surface reconstruction from clouds of unorganized points representing an object surface in 3-D space. The proposed method uses a set of mesh operators and simple rules for selective mesh refinement, with a strategy based on Kohonen's self-organizing map (SOM). Basically, a self-adaptive scheme is used for iteratively moving vertices of an initial simple mesh in the direction of the set of points, ideally the object boundary. Successive refinement and motion of vertices are applied leading to a more detailed surface, in a multiresolution, iterative scheme. Reconstruction was experimented on with several point sets, including different shapes and sizes. Results show generated meshes very close to object final shapes. We include measures of performance and discuss robustness. Index Terms--Adaptive geometry meshes, multiresolution, point clouds, self-organizing maps (SOMs), surface reconstruction.

Adaptive imaging systems alter their data-acquisition configuration or protocol in response to the image information received. An adaptive pinhole single-photon emission computed tomography (SPECT) system might acquire an initial scout image to obtain preliminary information about the radiotracer distribution and then adjust the configuration or sizes of the pinholes, the magnifications, or the projection angles in order to improve performance. This paper briefly describes two small-animal SPECT systems that allow this flexibility and then presents a framework for evaluating adaptive systems in general, and adaptive SPECT systems in particular. The evaluation is in terms of the performance of linear observers on detection or estimation tasks. Expressions are derived for the ideal linear (Hotelling) observer and the ideal linear (Wiener) estimator with adaptive imaging. Detailed expressions for the performance figures of merit are given, and possible adaptation rules are discussed. Index Terms--Adaptive imaging, Hotelling observer, image quality, single-photon emission computed tomography (SPECT), Wiener estimator.

A classic tuned vibration absorber (TVA) is a device that, when attached to a structure, will greatly reduce the motion of the attachment at a specific excitation frequency. When a fixed frequency input is present, a TVA can be manufactured for the specific frequency input. When the input frequency changes during the course of operation, then an active adaptive TVA can be used where sensors, signal conditioning, and power are provided so that the tuned frequency can be varied over some range. A self-tuning vibration absorber (STVA) is a device that uses energy from the vibrating structure to produce some physical motion that changes the tuned frequency of the device. Through proper design, the tuned frequency will change in the appropriate direction and then stop changing when the tuned frequency matches the input frequency. This paper addresses the physics of one realization of a STVA and shows both analytical and experimental results. Keywords: self-tuning, vibration, absorber, TVA

An adaptive filtering algorithm is proposed in this paper to remove I/Q mismatch, dc offsets, flicker noise, and intersymbol interference (ISI) simultaneously in a direct-conversion receiver. I/Q mismatch is cancelled by a real valued adaptive mismatch canceller, and dc offsets are removed with one complex tap. In addition, flicker noise is modeled as a complex autoregressive (AR) random process so the system to be identified transforms to an ARX model. After estimating the coefficients in the model during the training period, the desired signal can be estimated using the decision feedback method. To accelerate the convergence of the algorithm and to reduce the estimation variance, an internal iterative algorithm is introduced. The convergence analysis of the proposed algorithm is also given, and the closed form of the minimum mean square error of the proposed algorithm is derived. Simulation results are provided to verify the superior performance of the proposed algorithm. Index Terms--ARX model, convergence, dc offsets, direct-conversion receivers, flicker noise, I/Q mismatch, minimum mean-square error (MMSE).

Multibit feedback, being one way of lowering [DELTA][SIGMA] modulators power consumption, has a major obstacle: the number of components in the internal analog-to-digital converter (ADC) and digital-to-analog converter (DAC). Nevertheless, the number of comparators in the ADC can be significantly reduced depending on the order of noise-shaping and the oversampling ratio. In this paper, we propose an auto-ranging algorithm with a mechanism to keep the structure stable that emulates more quantization levels than that allowed by the number of comparators. As the recourse to segmented DACs allows lowering the complexity of the mismatch shaping encoder, the auto-ranging ADC brings the benefits of multibit feedback without the usual increase in size and power consumption. The internal number of bits in [DELTA][SIGMA] modulators is no more restricted by the difficulty of building the flash ADC with a low voltage supply. Index Terms--Adaptive algorithm, auto-ranging, delta-sigma ([DELTA][SIGMA]), dynamic element matching (DEM), low voltage, multibit.

The performance of multiple-input-multiple-output systems with optimum combining (OC) is studied in a Rayleigh fading environment with arbitrary-power cochannel interference and thermal noise. Based on the joint eigenvalue distributions of quadratic functions of complex Gaussian matrices, a closed-form expression for the exact distribution of the output signal-to-interference-plus-noise ratio (SINR) is derived. A closed-form expression for the exact moment-generating function (MGF) of the output SINR of single-input-multiple-output (SIMO) systems is also derived. From the exact MGF, the moments of the output SINR and the symbol error rate of various M-ary modulation schemes are obtained. We verify the accuracy of our analytical results with numerical examples. The new analytical framework provides a simple and accurate way to assess the effects of equal-and unequal-power cochannel interferers and thermal noise on the performance of OC. Index Terms--Adaptive antennas, cochannel interference, fading channels, multiple-input-multiple-output (MIMO) systems.

Multicarrier code-division multiple access (CDMA) with time and frequency spreading has been recently considered as a candidate for fourth-generation (4G) wireless systems. This signaling scheme simultaneously utilizes code spreading in the time and frequency domains to simultaneously improve frequency diversity and minimize multiuser access interference. As a result, it is capable of outperforming multicarrier CDMA systems that employ 1-D spreading. In this paper, a novel adaptive subcarrier allocation algorithm is developed for multicarrier CDMA with time and frequency spreading to improve the overall bit error rate (BER) performance for all spreading configurations. This algorithm assigns users to subcarrier groups that provide favorable fading characteristics while simultaneously reducing the amount of interference caused to other users. The proposed algorithm is shown to provide a performance improvement, ranging from 1.5 dB with 2 x 16 (time x frequency) and spreading to 7 dB with 16 x 2 (time x frequency) spreading. The algorithm is also shown to maintain or improve the BER floor for each spreading configuration. It is concluded that at higher and lower levels of [E.sub.b]/[N.sub.o], a higher frequency- and time-domain spreading should be, respectively, employed to improve BER performance. Furthermore, the [E.sub.b]/[N.sub.o] threshold level to switch between time and frequency spreading for the analyzed system is found to be 2.5 dB. Index Terms--Adaptive subcarrier allocation, multicarrier code-division multiple access (CDMA), synchronous reverse links, time- and frequency-domain spreading.

This paper addresses the analysis and performance of several model reference adaptive system (MRAS) observers for sensorless vector control of doubly-fed induction machines. Small signal models allow the formal analysis of the observers for a given dynamic. The performance of each MRAS observer is analyzed, considering grid-connected and stand-alone operation. The MRAS observers are implemented in a 3.5 kW experimental prototype composed of a doubly-fed induction generator and a wind turbine emulator. Experimental results validate the predictions of the small signal models and demonstrate the performance of the sensorless methods during both steady state and variable speed wind energy generation. Index Terms--Adaptive observers, induction generators, power generation control.

Hysteresis hinders the effectiveness of smart materials in sensors and actuators. It is a challenging task to control the systems with hysteresis. This note discusses the adaptive control for discrete time linear dynamical systems preceded with hysteresis described by the Prandtl-Ishlinskii model. The time delay and the order of the linear dynamical system are assumed to be known. The contribution of the note is the fusion of the hysteresis model with adaptive control techniques without constructing the inverse hysteresis nonlinearity. Only the parameters (which are generated from the parameters of the linear system and the density function of the hysteresis) directly needed in the formulation of the controller are adaptively estimated online. The proposed control law ensures the global stability of the closed-loop system, and the output tracking error can be controlled to be as small as required by choosing the design parameters. Simulation results show the effectiveness of the proposed algorithm. Index Terms--Adaptive control, discrete time linear systems, hysteresis, Prandtl-Ishlinskii model.

This note addresses the problem of position control of robotic manipulators both nonredundant and redundant in the task space. A computationally simple class of task space regulators consisting of a transpose adaptive Jacobian controller plus an adaptive term estimating generalized gravity forces is proposed. The Lyapunov stability theory is used to derive the control scheme. The conditions on controller gains ensuring asymptotic stability are obtained herein in a form of simple inequalities including some information extracted from both robot kinematic and dynamic equations. The performance of the proposed control strategy is illustrated through computer simulations for a direct-drive arm of a SCARA type redundant manipulator with the three revolute kinematic pairs operating in a two-dimensional task space. Index Terms--Adaptive control, Lyapunow stability, robotic manipulators.

We present an adaptive controller that requires limited model information for stabilization, command following, and disturbance rejection for mult-input multi-output minimum-phase discrete-time systems. Specifically, the controller requires knowledge of the open-loop system's relative degree as well as a bound on the first nonzero Markov parameter. Notably, the controller does not require knowledge of the command or the disturbance spectrum as long as the command and disturbance signals are generated by a Lyapunov-stable linear system. Thus, the command and disturbance signals are combinations of discrete-time sinusoids and steps. In addition, the Markov-parameter-based adaptive controller uses feedback action only, and thus does not require a direct measurement of the command or disturbance signals. Using a logarithmic Lyapunov function, we prove global asymptotic convergence for command following and disturbance rejection as well as Lyapunov stability of the adaptive system when the open-loop system is asymptotically stable. Index Terms--Adaptive control, discrete time, Lyapunov

This paper proposes a combined approach of optimized charge simulation method (CSM) and hashing integrated adaptive genetic algorithm (HAGA) to the contour design of support insulators. In the combined approach, the HAGA determines the optimal displacement ratio and number of fictitious charges used in CSM, and then obtains the optimal contour design to minimize and uniform the tangential electric field along the dielectric boundary based on the optimized CSM. The proposed HAGA method considerably improves the execution efficiency by accessing the fitness values of the searched solutions saved a priori in a hashing table during the optimization process. To verify the effectiveness of the proposed method, three cases of different contours of support insulators have been studied. Results show that rather uniform and minimal tangential field distributions along the insulator surfaces can be effectively derived by the proposed approaches to achieve promising performance and higher probability free from flashover. Index Terms--insulator contour design, insulator flashover, adaptive genetic algorithm, hashing table, optimized charge simulation method.

Repetitive controllers have been shown to be effective for tracking periodic reference commands or for rejecting periodic disturbances. Typical repetitive controllers are synthesized in temporal domain where the periods of the reference or disturbance signals are assumed to be known and stationary. For periodic references and disturbances with varying periods, researchers usually resort to adaptive and robust control approaches. For rotational motion systems where the disturbances or reference signals are spatially periodic (i.e., periodic with respect to angular displacement), the temporal period of the disturbance and reference signals will be inversely proportional to the rotational speed and vary accordingly. Motivating by reducing halftone banding for laser printers, we propose a design framework for synthesizing spatially sampled repetitive controller by reformulating a linear time-invariant system subject to spatially periodic disturbances using angular displacement as the independent variable. The resulting nonlinear system can be represented as a quasi-linear parameter-varying (quasi-LPV) system with the angular velocity as one of the varying state-dependent parameters. An LPV self-gainscheduling controller that includes a spatially sampled repetitive control can be designed to take into consideration bounded model uncertainty and input nonlinearity, such as actuator saturation. Using the signal from an optical encoder pulse as a triggering interrupt, experimental results verified the effectiveness of the proposed approach in rejecting spatially periodic disturbances that cannot be compensated with fixed period temporal repetitive controllers. [DOI: 10.1115/1.2837306] Keywords: repetitive control, disturbance rejection, linear parameter-varying system, gain-scheduling control