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Automatic Control, IEEE Transactions on

Issue 11 • Date Nov. 1999

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Displaying Results 1 - 25 of 45
  • On approximate pulse transfer functions

    Page(s): 2062 - 2067
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (221 KB)  

    A systematic approach to a class of approximations to the pulse transfer function of a system consisting of a zero-order hold and a linear continuous-time plant is presented. It is based on the asymptotic result of Astrom, Hagander, and Sternby (1984) on zeros of sampled systems at high sampling rates, and on the bilinear transformation. Since the number of intrinsic parameters does not change in the discretization process, model matching control, robust control, and identification are suggested as possible areas of application. Superiority of the approximations considered over a δ-operator based truncated approximation is shown. The results are illustrated by an example. View full abstract»

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  • Stochastic control of discrete systems: a separation principle for Wiener and polynomial systems

    Page(s): 2125 - 2130
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    A new separation principle is established for systems represented in discrete frequency-domain Wiener or polynomial forms. The LQG or H 2 optimal controller can be realized using an observer based structure estimating noise free output variables that are fed back through a dynamic gain control block. Surprisingly, there are also two separation principle theorems, depending upon the order in which the ideal output optimal control and the optimal observer problems are solved View full abstract»

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  • On the existence of Caratheodory solutions in mechanical systems with friction

    Page(s): 2086 - 2089
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    It is shown that the differential equation with discontinuous right-hand side, which describes the dynamic behavior of a mechanical system with friction, has a Caratheodory solution. This result supports theoretically the prior work on the control of mechanical systems with friction, which assumes the existence of Caratheodory solutions without proof View full abstract»

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  • Polyhedral regions of local stability for linear discrete-time systems with saturating controls

    Page(s): 2081 - 2085
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (184 KB)  

    The study and the determination of polyhedral regions of local stability for linear systems subject to control saturation is addressed. The analysis of the nonlinear behavior of the closed-loop saturated system is made by dividing the state space in regions of saturation. Inside each of these regions, the system evolution can be represented by a linear system with an additive disturbance. From this representation, a necessary and sufficient condition relative to the contractivity of a given convex compact polyhedral set is stated. Consequently, the polyhedral set can be associated with a Lyapunov function and the local asymptotic stability of the saturated closed-loop system inside the set is guaranteed. Furthermore, it is shown how, in some particular cases, the compactness condition can be relaxed in order to ensure the asymptotic stability in unbounded polyhedra. Finally, an application of the contractivity conditions is presented in order to determine local asymptotic stability regions for the closed-loop saturated system View full abstract»

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  • Iterative algorithm for parameter identification of Hammerstein systems with two-segment nonlinearities

    Page(s): 2145 - 2149
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (132 KB)  

    This paper deals with parameter identification of Hammerstein systems having two-segment polynomial nonlinearities. The application of a simple decomposition technique and using switching sequences provide a special form of a Hammerstein model that is linear-in-parameters. This model is used in an iterative algorithm, enabling simultaneous estimation of all of the model parameters. To demonstrate the feasibility of the identification method, more illustrative examples are included View full abstract»

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  • An alternative motivation for the indirect approach to closed-loop identification

    Page(s): 2206 - 2209
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    Direct prediction error identification of systems operating in closed loop may lead to biased results due to the correlation between the input and the output noise. The authors study this error, what factors affect it, and how it may be avoided. In particular, the role of the noise model is discussed and the authors show how the noise model should be parameterized to avoid the bias. Apart from giving important insights into the properties of the direct method, this provides a nonstandard motivation for the indirect method View full abstract»

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  • Conditional densities for continuous-time nonlinear hybrid systems with applications to fault detection

    Page(s): 2164 - 2169
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    Continuous-time nonlinear stochastic differential state and measurement equations, all of which have coefficients capable of abrupt changes at a random time, are considered; finite-state jump Markov chains are used to model the changes. Conditional probability densities, which are essential in obtaining filtered estimates for these hybrid systems, are then derived. They are governed by a coupled system of stochastic partial differential equations. When the Q matrix of the Markov chain is either lower or upper diagonal, it is shown that the system of conditional density equations is finite-dimensional computable. These findings are then applied to a fault detection problem to compute state estimates that include the failure time View full abstract»

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  • A neural state estimator with bounded errors for nonlinear systems

    Page(s): 2028 - 2042
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    A neural state estimator is described, acting on discrete-time nonlinear systems with noisy measurement channels. A sliding-window quadratic estimation cost function is considered and the measurement noise is assumed to be additive. No probabilistic assumptions are made on the measurement noise nor on the initial state. Novel theoretical convergence results are developed for the error bounds of both the optimal and the neural approximate estimators. To ensure the convergence properties of the neural estimator, a minimax tuning technique is used. The approximate estimator can be designed offline in such a way as to enable it to process on line any possible measure pattern almost instantly View full abstract»

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  • Asymptotic convergence from Lp stability

    Page(s): 2169 - 2170
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    This note recalls that an absolutely continuous function having a uniformly locally integrable (not necessarily essentially bounded) derivative is uniformly continuous on the semi-infinite interval. This observation, in conjunction with Barbalat's lemma, allows concluding asymptotic convergence to zero of an output function for a general class of nonlinear systems with Lp (not necessarily L) disturbances View full abstract»

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  • Fault tolerant decentralized H control for symmetric composite systems

    Page(s): 2108 - 2114
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    Discusses a class of large-scale systems composed of symmetrically interconnected identical subsystems. We consider the decentralized H control design problem and study the fault tolerance of the resulting system. By exploiting the special structure of the systems, a sufficient condition for the existence of a decentralized H controller is derived. Moreover, for the nominal case as well as for contingent situations characterized by control channel failures, the poles and the H-norm of the closed-loop system can be calculated easily based on certain systems of reduced dimensions. Consequently, the tolerance to actuator failure can, be easily tested View full abstract»

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  • Time-varying discrete Riccati equation: l2-invertibility, dichotomy, and disconjugacy

    Page(s): 2158 - 2163
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    Necessary and sufficient existence conditions for the stabilizing solution to the time-varying discrete Riccati equation are given. The result is simply expressed in terms of the original input data via the l 2-uniformly bounded invertibility of an appropriate family of Toeplitz-like operators. Intimate connections with the notions of dichotomy and disconjugacy are also pointed out. Some aspects concerning the dependence of the existence conditions on the monotonicity of the input data are investigated as well View full abstract»

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  • Singular perturbation analysis of cheap control problem for sampled data systems

    Page(s): 2209 - 2214
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (176 KB)  

    This paper studies the discrete-time cheap control problem for sampled data systems using the theory of singular perturbations. It is shown, by using the two time-scale property of singularly perturbed systems, that the problem can be solved in terms of two reduced-order subproblems for which computations can be done in parallel, thus increasing the computational speed. Similarly to the continuous-time case, the singular perturbation approach enables the decomposition of the algebraic Riccati equation into two reduced-order pure-slow and pure-fast continuous-time algebraic equations View full abstract»

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  • Decentralized adaptive control of nonlinear systems using radial basis neural networks

    Page(s): 2050 - 2057
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (332 KB)  

    Stable direct and indirect decentralized adaptive radial basis neural network controllers are presented for a class of interconnected nonlinear systems. The feedback and adaptation mechanisms for each subsystem depend only upon local measurements to provide asymptotic tracking of a reference trajectory. Due to the functional approximation capabilities of radial basis neural networks, the dynamics for each subsystem are not required to be linear in a set of unknown coefficients as is typically required in decentralized adaptive schemes. In addition, each subsystem is able to adaptively compensate for disturbances and interconnections with unknown bounds View full abstract»

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  • Robust stability and design of linear discrete-time SISO systems under l1 uncertainties

    Page(s): 2076 - 2080
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (184 KB)  

    Shows how value sets and the zero exclusion principle can be used to obtain results on the robust stability and design of controllers against a special infinite-dimensional l1 norm bounded parametric uncertainty in both numerator and denominator of scalar discrete-time plants. This parametric uncertainty has properties of both parametric and unstructured uncertainty and allows standard H design tools to be used without conservatism View full abstract»

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  • An analysis of a class of neural networks for solving linear programming problems

    Page(s): 1995 - 2006
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (356 KB)  

    A class of neural networks that solve linear programming problems is analyzed. The neural networks considered are modeled by dynamic gradient systems that are constructed using a parametric family of exact (nondifferentiable) penalty functions. It is proved that for a given linear programming problem and sufficiently large penalty parameters, any trajectory of the neural network converges in finite time to its solution set. For the analysis, Lyapunov-type theorems are developed for finite time convergence of nonsmooth sliding mode dynamic systems to invariant sets. The results are illustrated via numerical simulation examples View full abstract»

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  • Adaptive variable structure set-point control of underactuated robots

    Page(s): 2090 - 2093
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (136 KB)  

    Control of underactuated mechanical systems (robots) represents an important class of control problem. In this correspondence, a model-based adaptive variable structure control scheme is introduced, where the uncertainty bounds only depend on the inertia parameters of the system. Global asymptotic stability is established in the Lyapunov sense. Numerical simulations are conducted to validate the theoretical analysis View full abstract»

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  • The Kharitonov theorem with degree drop

    Page(s): 2218 - 2220
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    The purpose of this paper is to present a proof of the Kharitonov theorem based on Bezoutians. An interesting consequence of this proof is that it shows the validity of Kharitonov's result in the presence of a degree drop View full abstract»

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  • Adaptive internal model control: H optimization for stable plants

    Page(s): 2130 - 2134
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (152 KB)  

    This paper considers the design of a robust adaptive H optimal controller based on the IMC structure. Specific attention is focused on the practically important class of stable plants with one open right half-plane zero. The certainty equivalence principle of adaptive control is used to combine a robust adaptive law with a robust H internal model controller to obtain an adaptive H internal model control scheme with provable guarantees of stability and robustness. The approach used earlier for designing and analyzing adaptive H2 optimal control schemes is seen to carry over in a natural fashion to the adaptive H optimal control case. This attests to the generality of that approach, which not only provides a theoretical basis for analytically justifying some of the reported industrial successes of existing adaptive internal model control schemes, but also opens up the possibility of synthesizing new ones by simply combining a robust adaptive law with a robust internal model controller structure View full abstract»

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  • Global predictive regulation of null-controllable input-saturated linear systems

    Page(s): 2226 - 2230
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (156 KB)  

    A predictive regulator is described for stabilizing linear plants subject to polyhedral input constraints. While the asymptotic stability of general linear plants is ensured subject to a set-membership condition on the initial state, global asymptotic and semiglobal exponential regulation is achieved for input-saturated plants which are asymptotically null controllable with bounded inputs. The design knobs of the regulator can be chosen off line so as to compromise between nonconservative regulation performance and computational complexity, and an online optimization problem of prequantifiable numerical burden results View full abstract»

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  • Generalized (C, A, B)-pairs and parameter insensitive disturbance-rejection problems with dynamic compensator

    Page(s): 2195 - 2200
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (232 KB)  

    In the so-called geometric approach, the notion of the generalized (C, A, B)-pairs is introduced for uncertain linear systems, and its properties are investigated. Further, the parameter insensitive disturbance-rejection problem with dynamic compensator is formulated and some sufficient conditions for the problem to be solvable are presented View full abstract»

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  • Output feedback robust stabilization of uncertain linear systems with saturating controls: an LMI approach

    Page(s): 2230 - 2237
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (216 KB)  

    The problem of robust controller design is addressed for an uncertain linear system subject to control saturation. No assumption is made concerning open-loop stability, and no a priori information is available regarding the domain of stability. A saturating linear output feedback law and a safe set of initial conditions are determined using a heuristic based on iterative LMI relaxation procedures. A readily implementable algorithm based on standard numerical techniques is described and illustrated on two numerical examples View full abstract»

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  • Control of Markovian jump discrete-time systems with norm bounded uncertainty and unknown delay

    Page(s): 2139 - 2144
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    This paper studies the problem of control for discrete time delay linear systems with Markovian jump parameters. The system under consideration is subjected to both time-varying norm-bounded parameter uncertainty and unknown time delay in the state, and Markovian jump parameters in all system matrices. We address the problem of robust state feedback control in which both robust stochastic stability and a prescribed H performance are required to be achieved irrespective of the uncertainty and time delay. It is shown that the above problem can be solved if a set of coupled linear matrix inequalities has a solution View full abstract»

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  • An adaptive output feedback control for a class of nonlinear systems with time-varying parameters

    Page(s): 2190 - 2194
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (196 KB)  

    We consider a class of observable, minimum phase single-input, single-output nonlinear systems evolving in Rn with relative degree ρ and p uncertain differentiable time varying parameters θ(t), belonging to a known compact set Ω⊂Rp, whose time derivatives θ˙(t) are bounded, but are not restricted to be small or to have known bounds. We design a dynamic output feedback controller such that, for any initial condition: 1) all signals are bounded; 2) the effects of parameter uncertainties on the tracking error are arbitrarily attenuated; and 3) when θ˙(t)∈L1, asymptotic tracking is guaranteed with arbitrarily good transient performance. Adaptation may be switched off at any time, still retaining the closed-loop properties 1) and 2) View full abstract»

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  • Consistent identification of NARX models via regularization networks

    Page(s): 2045 - 2049
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (168 KB)  

    Generalization networks are nonparametric estimators obtained from the application of Tychonov regularization or Bayes estimation to the hypersurface reconstruction problem. Under symmetry assumptions, they are a particular type of radial basis function neural network. In this correspondence, it is shown that such networks guarantee consistent identification of a very general (infinite-dimensional) class of NARX models. The proofs are based on the theory of reproducing kernel Hilbert spaces and the notion of frequency of time probability, by means of which it is not necessary to assume that the input is sampled from a stochastic process View full abstract»

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  • The unreasonable effectiveness of neural network approximation

    Page(s): 2043 - 2044
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    Results concerning the approximation rates of neural networks are of particular interest to engineers. The results reported in the literature have “slow approximation rates” O(1/√m), where m is the number of parameters in the neural network. However, many empirical studies report that neural network approximation is quite effective in practice. We give an explanation of this unreasonable effectiveness by proving the existence of approximation schemes that converge at a rate of the order of 1/m2 by using methods from number theory View full abstract»

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In the IEEE Transactions on Automatic Control, the IEEE Control Systems Society publishes high-quality papers on the theory, design, and applications of control engineering.  Two types of contributions are regularly considered

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Meet Our Editors

Editor-in-Chief
P. J. Antsaklis
Dept. Electrical Engineering
University of Notre Dame