The C4ISR Architecture Framework document issued by the department of Defense specifies three views of an information architecture and defines a set of products that describe each view. These architecture views are to...
The problem of model-based fault-detection for nonlinear systems is addressed in the paper by means of a bank of neural estimators. The solution of nonlinear fault-detection problems is very difficult in general. A di...
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The problem of model-based fault-detection for nonlinear systems is addressed in the paper by means of a bank of neural estimators. The solution of nonlinear fault-detection problems is very difficult in general. A different fault-detection scheme is proposed, basing on finite-memory estimators. Each estimator of the bank enables one to estimate the fault parameters used to describe plant, actuator, and sensor faults. The fault finite-memory estimator is stated in a general nonlinear setting and an efficient technique is proposed to find an approximate solution by means of feedforward neural networks. Successful simulation results have been obtained with the model of a small underwater vehicle.
We show that many open and challenging problems in control theory belong to the class of concave minimization programs. More precisely, these problems can be recast as the minimization of a concave objective function ...
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We show that many open and challenging problems in control theory belong to the class of concave minimization programs. More precisely, these problems can be recast as the minimization of a concave objective function over convex linear matrix inequality (LMI) constraints. In this setting, these problems can then be efficiently handled using local and/or global optimization techniques.
The paper develops a second-order Newton algorithm for finding local solutions of rank-constrained LMI problems in robust synthesis. The algorithm is based on a quadratic approximation of a suitably defined merit func...
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The paper develops a second-order Newton algorithm for finding local solutions of rank-constrained LMI problems in robust synthesis. The algorithm is based on a quadratic approximation of a suitably defined merit function and generates sequences of LMI feasible iterates. The main thrust of the algorithm is that it inherits the good local convergence properties of Newton methods and thus overcomes the difficulties encountered with earlier methods such as the Frank and Wolfe or conditional gradient methods which tend to be very slow in the neighborhood of a local solution. Moreover, it is easily implemented using available Semi-Definite Programming (SDP) codes. Proposed algorithms have proven global and local convergence properties and thus represent improvements over classically used D-K iteration schemes but also outperform earlier conditional gradient algorithms. Reported computational results demonstrate these facts.
This paper presents a new tuning bilinear approximation method together with the interval arithmetic operation to convert a continuous-time (discrete-time) uncertain system to an equivalent discrete-time (continuous-t...
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This paper presents a new tuning bilinear approximation method together with the interval arithmetic operation to convert a continuous-time (discrete-time) uncertain system to an equivalent discrete-time (continuous-time) uncertain model. Based on the bilinear approximation method, we induct one parameter for tuning the error bound between the exact and approximate uncertain system matrices. The bounds of interval system matrices via the proposed interval tuning bilinear approximation method are narrower than those of the existing interval bilinear and the interval pade approximation methods. The resulting interval models (the enclosing interval models) are able to tightly enclose the exact uncertain models. Also the approximate discrete-time interval solution is able to tightly enclose the exact interval solution of the continuous-time uncertain state-space equation. The proposed method will be helpful for analysis and synthesis of uncertain systems.
There are many communication circuits driven by multi-tone signals such as modulators and mixers. If the output frequency components are largely different to each other, the brute force numerical method will take an e...
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There are many communication circuits driven by multi-tone signals such as modulators and mixers. If the output frequency components are largely different to each other, the brute force numerical method will take an enormous computation time to calculate the steady-state responses, because the total period becomes very long. In this paper, we show a SPICE oriented algorithm based on the frequency domain relaxation method which can be efficiently applied to relatively large scale ICs.
A new stiffness adaptation and force regulation methodology using hybrid system approach for constrained robots is presented. We present the hybrid system model and the hybrid systemcontrol synthesis for constrained ...
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A new stiffness adaptation and force regulation methodology using hybrid system approach for constrained robots is presented. We present the hybrid system model and the hybrid systemcontrol synthesis for constrained robots with the stiffness uncertainties is formulated. The hybrid control approach presented has shown to be a very effective strategy to incorporate both the continuous and discrete natures of constraint motion. A nonlinear stiffness function is developed and designed to be hybrid automaton, which consists of some abstracted motion such as increase, decrease, and maintenance of stiffness. The evaluations are evaluated via experimental studies on grinding tasks. The results of experiment are showing the applicability of proposed scheme for constrained tasks.
In this paper, a pseudo measurement model is adopted to deal with the Kalman filter problem with color measurement noise. It is assumed that the measurement noise is color, and is described by an AR(n) model. The stat...
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In this paper, a pseudo measurement model is adopted to deal with the Kalman filter problem with color measurement noise. It is assumed that the measurement noise is color, and is described by an AR(n) model. The state equation is set up according to Singer's model. The measurement equation is set up in polar coordinate and the range rate measurement is combined with ordinary data (i.e., there are four measurement data: range r, azimuth /spl theta//sub a/, elevation /spl theta//sub e/ and range rate r/spl dot/). By means of proper coordinate transformation, the matrix of Kalman gain and the matrix of filter error covariance can be decoupled. Thus the computing requirement of the algorithm is reduced and can be performed in real time.
This paper presents a new real-time architecture for motion control of industrial robots. The new controlsystem obtained has two main advantages: first it provides a total open control architecture and the second adv...
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This paper presents a new real-time architecture for motion control of industrial robots. The new controlsystem obtained has two main advantages: first it provides a total open control architecture and the second advantage is the simplicity and the interactivity of the platform developed. Experimental evaluation of a passivity-based control scheme shows the benefits of the architecture which is unique in the sense that open and advanced control can be combined with built-in safety logic as required in industrial applications.
Power plant monitoring is addressed by means of a sliding-window neural state estimator. The complexity and the nonlinearity of the considered power plant application prevents us from using standard techniques such as...
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Power plant monitoring is addressed by means of a sliding-window neural state estimator. The complexity and the nonlinearity of the considered power plant application prevents us from using standard techniques such as Kalman filtering. The statistics of noises are assumed unknown and the estimator is designed by minimising a given best squares cost function (in general, non-quadratic) under very general assumptions on the state equation and the system measurement channel. The estimator has been designed off-line in such a way as to be able to process any possible measurement online. Extensive simulation of a state estimation problem in a model of a section of a real power plant are reported showing the effectiveness of the applied method as compared to the extended Kalman filter.
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