Showing papers on "Robustness (computer science) published in 1991"

Robustness in the presence of mixed parametric uncertainty and unmodeled dynamics

Abstract: Continuing the development of the structured singular value approach to robust control design, the authors investigate the problem of computing mu in the case of mixed real parametric and complex uncertainty. The problem is shown to be equivalent to a smooth constrained finite-dimensional optimization problem. In view of the fact that the functional to be maximized may have several local extrema, an upper bound on mu whose computation is numerically tractable is established; this leads to a sufficient condition of robust stability and performance. A historical perspective on the development of the mu theory is included. >

Robust speed control of DC servomotors using modern two degrees-of-freedom controller design

Abstract: The authors propose a robust speed control system for DC servomotors based on the parametrization of two-degree-of-freedom controllers. The servosystems can dramatically improve the characteristics of the closed loop systems, i.e. the disturbance torque suppression performance and the robustness to system parameter variations, without changing the command input response. The excellent control performances obtained during laboratory experiments by using a microprocessor-based controller are shown. >

Acoustical and environmental robustness in automatic speech recognition

Abstract: This dissertation describes a number of algorithms developed to increase the robustness of automatic speech recognition systems with respect to changes in the environment. These algorithms attempt to improve the recognition accuracy of speech recognition systems when they are trained and tested in different acoustical environments, and when a desk-top microphone (rather than a close-talking microphone) is used for speech input. Without such processing, mismatches between training and testing conditions produce an unacceptable degradation in recognition accuracy. Two kinds of environmental variability are introduced by the use of desk-top microphones and different training and testing conditions: additive noise and spectral tilt introduced by linear filtering. An important attribute of the novel compensation algorithms described in this thesis is that they provide joint rather than independent compensation for these two types of degradation. Acoustical compensation is applied in our algorithms as an additive correction in the cepstral domain. This allows a higher degree of integration within SPHINX, the Carnegie Mellon speech recognition system, that uses the cepstrum as its feature vector. Therefore, these algorithms can be implemented very efficiently. Processing in many of these algorithms is based on instantaneous signal-to-noise ratio (SNR), as the appropriate compensation represents a form of noise suppression at low SNRs and spectral equalization at high SNRs. The compensation vectors for additive noise and spectral transformations are estimated by minimizing the differences between speech feature vectors obtained from a "standard" training corpus of speech and feature vectors that represent the current acoustical environment. In our work this is accomplished by minimizing the distortion of vector-quantized cepstra that are produced by the feature extraction module in SPHINX. In this dissertation we describe several algorithms including the SNR-Dependent Cepstral Normalization, (SDCN) and the Codeword-Dependent Cepstral Normalization (CDCN). With CDCN, the accuracy of SPHINX when trained on speech recorded with a close-talking microphone and tested on speech recorded with a desk-top microphone is essentially the same obtained when the system is trained and tested on speech from the desk-top microphone. An algorithm for frequency normalization has also been proposed in which the parameter of the bilinear transformation that is used by the signal-processing stage to produce frequency warping is adjusted for each new speaker and acoustical environment. The optimum value of this parameter is again chosen to minimize the vector-quantization distortion between the standard environment and the current one. In preliminary studies, use of this frequency normalization produced a moderate additional decrease in the observed error rate.

Stability of the pseudo-inverse method for reconfigurable control systems

Abstract: One of the key reconfigurable control methods, the pseudo-inverse method (PIM), is analysed and new insight is obtained which provides the theoretical basis for this practical approach. The main shortcoming of this method, the lack of stability guarantees, is pointed out and a new approach is proposed in which recent results on the stability robustness of linear systems are used to provide stability constraints for the solutions of the PIM. When the original PIM solution results in an unstable closed-loop system, the control redesign problem is treated as a constraint minimization problem. For single-input systems, a closed-form solution is presented; for multi-input systems, a near-optimal solution is found which maintains the stability of the closed-loop system.

Optimal trade-off filters for noise robustness, sharpness of the correlation peak, and Horner efficiency.

Abstract: Filters that have optimal trade-offs among the criteria of noise robustness, sharpness of the correlation peak, and Horner efficiency are presented, and an explicit mathematical expression is provided. Owing to their optimality, these filters provide a figure of merit and then permit a rigorous characterization of filter performances for optical pattern recognition.

Performance robustness of discrete-time systems with structured uncertainty

Abstract: Given an interconnection of a nominal discrete-time plant and a stabilizing controller together with structured, norm-bounded, nonlinear/time-varying perturbations, necessary and sufficient conditions for robust stability and performance of the system are provided. It is shown that performance robustness is equivalent to stability robustness in the sense that both problems can be dealt with in the framework of a general stability robustness problem. The resulting stability robustness problem is shown to be equivalent to a simple algebraic one, the solution of which provides the desired necessary and sufficient conditions for performance/stability robustness. These conditions provide an effective tool for robustness analysis and can be applied to a large class of problems. In particular, it is shown that some known results can be obtained immediately as special cases of these conditions. >

Super resolution pitch determination of speech signals

Abstract: Based on a new similarity model for the voice excitation process, a novel pitch determination procedure is derived. The unique features of the proposed algorithm are infinite (super) resolution, better accuracy than the difference limen for F/sub 0/, robustness to noise, reliability, and modest computational complexity. The algorithm is instrumental to speech processing applications which require pitch synchronous spectral analysis. The computational complexity of the proposed algorithm is well within the capacity of modern digital signal processing (DSP) technology and therefore can be implemented in real time. >

Stochastic robustness of linear time-invariant control systems

Abstract: A simple numerical procedure for estimating the stochastic robustness of a linear time-invariant system is described. Monte Carlo evaluation of the system's eigenvalues allows the probability of instability and the related stochastic root locus to be estimated. This analysis approach treats not only Gaussian parameter uncertainties but also nonGaussian cases, including uncertain but bound variations. Confidence intervals for the scalar probability of instability address computational issues inherent in Monte Carlo simulation. Trivial extensions of the procedure admit consideration of alternate discriminants; thus, the probabilities that stipulated degrees of instability will be exceeded or that closed-loop roots will leave desirable regions can also be estimated. Results are particularly amenable to graphical presentation. >

Robust fault detection using eigenstructure assignment: a tutorial consideration and some new results

Abstract: Developments in the eigenstructure assignment approach to robust fault detection are discussed. By suitable assignment of the eigenstructure of an observer, the residual signal is decoupled from disturbances. The main contribution of this work is the novel use of right eigenvector assignment of observers, which gives more freedom for achieving disturbance decoupling. It is shown that, when decoupling conditions are satisfied, the resulting deadbeat design is equivalent to the first-order parity space structure for residual generation. Two tutorial examples are presented to illustrate the disturbance decoupling property and the conditions under which left or right eigenvectors are assignable. >

Robust adaptive control: a unified approach

Abstract: A complete tutorial review of the entire field is presented, beginning with simple instability examples to identify the causes of nonrobust behavior in adaptive control. Some of the mathematical groundwork is presented, and the theory for the design and analysis of adaptive laws is developed. Commonly used adaptive controller structures are discussed, highlighting their particular robustness properties. Particular attention is paid to model reference, pole placement, and linear quadratic controller structures. Designs and analyses of model reference, pole placement, and linear quadratic controllers, based on combining the corresponding controller structures with the various robust adaptive laws, are presented. Suggestions for future research are given. >

Technical notes and correspondence: Stochastic robustness of linear time-invariant control systems

Abstract: A simple numerical procedure for estimating the stochastic robustness of a linear time-invariant system is described. Monte Carlo evaluations of the system's eigenvalues allows the probability of instability and the related stochastic root locus to be estimated. This analysis approach treats not only Gaussian parameter uncertainties but non-Gaussian cases, including uncertain-but-bounded variation. Confidence intervals for the scalar probability of instability address computational issues inherent in Monte Carlo simulation. Trivial extensions of the procedure admit consideration of alternate discriminants; thus, the probabilities that stipulated degrees of instability will be exceeded or that closed-loop roots will leave desirable regions can also be estimated. Results are particularly amenable to graphical presentation.

Robust independent joint controller design for industrial robot manipulators

Abstract: A novel approach is presented for the design of simple robust independent joint controllers for industrial robot manipulators. In this approach, each joint actuator is treated as a simple inertial system plus a disturbance torque representing all the unmodeled dynamics. By a very simple algorithm, the disturbance is instantly estimated and rejected, thus allowing a simple proportional-derivative (PD) control scheme to be used. The stability of the proposed control law is analyzed. Experimental evaluations of the controller on a microcomputer-controlled PUMA 560 arm were performed. It is shown that the control scheme is simple and practical and that it can be easily implemented on an industrial manipulator presently in use. >

Design of integral variable structure controller and application to electrohydraulic velocity servosystems

Abstract: An integral variable structure controller (IVSC) for robust servotracking is proposed. It comprises an integral controller, which is designed for achieving zero steady-state error under step input, and a variable structure controller (VSC) which is designed for enhancing robustness. A procedure is developed for determining the coefi- cients of the switching plane and the integral control gain such that the overall closed-loop system has the desired eigenvalues. Furthermore, a modifed proper continuous function is intro- duced to overcome the chattering problem. An electrohydraulic velocity servocontrol system using the proposed IVSC approach is illustrated. Simulation results show that the proposed IVSC approach can achieve accurate servotracking and is fairly robust to plant parameter variations and external load disturbances.

Robust estimation of a multi-layered motion representation

Abstract: In order to recover an accurate representation of a scene containing multiple moving objects, one must use estimation methods that can recover both model parameters and segmentation at the same time. Traditional approaches to this problem rely on an edge-based discontinuity model, and have problems with transparent phenomena. The authors introduce a layered model of scene segmentation based on explicitly representing the support of a homogeneous region. The model employs parallel robust estimation techniques, and uses a minimal-covering optimization to estimate the number of objects in the scene. Using a simple direct motion model of translating objects, they successfully segment real image sequences containing multiple motions. >

Robust tracking control of robots by a linear feedback law

Abstract: For the trajectory following problem of a robot manipulator, a simple linear robust fedback control law with constant gain matrix is proposed that makes the resulting error system uniformly ultimately bounded. This control law is very easy to implement by simply choosing a feedback gain according to the coefficients of a polynomial function of the tracking errors which is a bounding function for the terms in the Lagrange-Euler formulation. In the limit as the gain approaches infinity the error system becomes globally asymptotically stable. >

Least median of squares estimation in power systems

Abstract: An algorithm for solving LMS (least median of squares) in nonlinear systems has been proposed. The solutions are found through resampling methods based on linear approximations. These methods are suitable for parallel processing. The robustness of the LMS estimator has been verified on several test systems and illustrated on the IEEE 14-bus system. The concept of leverage points shed new light on the meter placement issue as well as the concepts of local redundancy and local breakdown point. A preliminary conclusion is that shorter lines have to be provided with enough measurements in order to increase their local redundancy. Indeed, they tend to be isolated in the factor space and weakly coupled with the surrounding measurements. >

Optimization approaches to nonlinear model predictive control

Abstract: With the development of sophisticated methods for nonlinear programming and powerful computer hardware, it now becomes useful and efficient to formulate and solve nonlinear process control problems through on-line optimization methods This paper explores and reviews control techniques based on repeated solution of nonlinear programming (NLP) problems Here several advantages present themselves These include minimization of readily quantifiable objectives, coordinated and accurate handling of process nonlinearities and interactions, and systematic ways of dealing with process constraints We motivate this NLP-based approach with small nonlinear examples and present a basic algorithm for optimization-based process control As can be seen this approach is a straightforward extension of popular model-predictive controllers (MPCs) that are used for linear systems The statement of the basic algorithm raises a number of questions regarding stability and robustness of the method, efficiency of the control calculations, incorporation of feedback into the controller and reliable ways of handling process constraints Each of these will be treated through analysis and/or modification of the basic algorithm To highlight and support this discussion, several examples are presented and key results are examined and further developed 74 refs, 11 figs

Solving Multi-Item Lot-Sizing Problems Using Strong Cutting Planes

Abstract: We consider two classes of multi-item lot-sizing problems. The first is a class of single stage problems involving joint machine capacity constraints and/or start up costs, and the second is a class of multistage problems with general product structure. The problems are solved as mixed integer programs based on i an appropriate choice of the initial problem formulation and ii the addition of cuts which are generated automatically by a mathematical programming system MPSARX. Our results extend and complement those of Karmarkar and Schrage 1985, Afentakis and Gavish 1986, Eppen and Martin 1987 and Van Roy and Wolsey 1987. A major advantage of this approach is its robustness or flexibility. By using just a matrix generator and a mathematical programming system with automatic cut generation routines we can formulate and solve model variants without incurring the costs of adapting an algorithm.

Balancing free square-root algorithm for computing singular perturbation approximations

Abstract: The author proposes a novel algorithm with enhanced numerical robustness for computing singular perturbation approximations of linear, continuous or discrete systems. This algorithm circumvents the computation of possibly ill-conditioned balancing transformations. Instead, well-conditioned projection matrices are determined for computing state-space representations suitable for applying the singular perturbation formulas. The projection matrices are computed using the Cholesky (square-root) factors of the gramians. The proposed algorithm is intended for efficient computer implementation. It can handle both minimal and nonminimal systems. >

Intelligent failure-tolerant control

Abstract: An overview of failure-tolerant control is presented, focusing on the control of continuous-time dynamic systems (or plants) whose motions can be represented by integrals of nonlinear ordinary differential equations. Failure tolerance may be called upon to improve system reliability, maintainability, and survivability, and the issues attached to achieving these goals are examined. Robustness, which is required in some degree by all failure-tolerant systems, is discussed. The use of parallel redundancy is examined. Analytical redundancy, the principal functions of which are failure detection, failure identification, and control-system reconfiguration, is also considered. The use of expert systems and neural networks is discussed. >

Robust contour decomposition using a constant curvature criterion

Abstract: The problem of decomposing an extended boundary or contour into simple primitives is addressed with particular emphasis on Laplacian-of-Gaussian zero-crossing contours. A technique is introduced for partitioning such contours into constant curvature segments. A nonlinear 'blip' filter matched to the impairment signature of the curvature computation process, an overlapped voting scheme, and a sequential contiguous segment extraction mechanism are used. This technique is insensitive to reasonable changes in algorithm parameters and robust to noise and minor viewpoint-induced distortions in the contour shape, such as those encountered between stereo image pairs. The results vary smoothly with the data, and local perturbations induce only local changes in the result. Robustness and insensitivity are experimentally verified. >

Robust identification and interpolation in H

Abstract: We consider system identification in H∞ in the framework proposed by Helmicki, Jacobson and Nett. An algorithm using the Jackson polynomials is proposed that achieves an exponential convergence rate for exponentially stable systems. It is shown that this, and similar identification algorithms, can be successfully combined with a model reduction procedure to produce low-order models. Connections with the Nevanlinna-Pick interpolation problem are explored, and an algorithm is given in which the identified model interpolates the given noisy data. Some numerical results are provided for illustration. Finally, the case of unbounded random noise is discussed and it is shown that one can still obtain convergence with probability 1 under natural assumptions.

Foundations of Adaptive Control

Abstract: The maturing of adaptive control.- A conceptual framework for parameter adaptive control.- Robust adaptive control: Design, analysis and robustness bounds.- Robust continuous-time adaptive control by parameter projection.- Stability of the direct self-tuning regulator.- Adaptive-invariant discrete control systems.- Stochastic adaptive system theory: Recent advances and a reappraisal.- Adaptive feedback linearization of nonlinear systems.- Adaptive stabilization of nonlinear systems.- Adaptive nonlinear control of induction motors via extended matching.- Global adaptive observers and output-feedback stabilization for a class of nonlinear systems.- Adaptive output-feedback control of systems with output nonlinearities.