For multi-input, multi-output stochastic systems, by means of auxiliary models - finite impulse response (FIR) models, we develop an identification algorithm to estimate the FIR model parameters of each entry (sub-sub...
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For multi-input, multi-output stochastic systems, by means of auxiliary models - finite impulse response (FIR) models, we develop an identification algorithm to estimate the FIR model parameters of each entry (sub-submodel) of transfer matrices with an increasing order for the FIR model. The basic idea is to use auxiliary models to predict/estimate the outputs of the sub-submodels, and further to use the recursive least squares algorithm or the Pade approximation method to produce the parameter estimates of sub-submodels. Some simulation results are included.
This paper is concerned with the derivation of the kinematics model of the University of Tehran-Pole Climbing Robot (UT-PCR). As the first step, an appropriate set of coordinates is selected and used to describe the s...
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This paper is concerned with the derivation of the kinematics model of the University of Tehran-Pole Climbing Robot (UT-PCR). As the first step, an appropriate set of coordinates is selected and used to describe the state of the robot. Nonholonomic constraints imposed by the wheels are then expressed as a set of differential equations. By describing these equations in terms of the state of the robot an underactuated driftless nonlinear control system with affine inputs that governs the motion of the robot is derived. A set of experimental results are also given to show the capability of the UT-PCR in climbing a stepped pole.
The focus of this paper is on control design and simulation for an air-breathing hypersonic vehicle. The challenges for control design in this class of vehicles lie in the inherent coupling between the propulsion syst...
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In this paper, we focus on the issue of adaptive H ∞ -control design for a class of linear parameter-varying (LPV) systems based on the Hamiltonian-Jacobi-Isaac (HJI) method. By combining the idea of polynomially par...
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In this paper, we focus on the issue of adaptive H ∞ -control design for a class of linear parameter-varying (LPV) systems based on the Hamiltonian-Jacobi-Isaac (HJI) method. By combining the idea of polynomially parameter-dependent quadratic functions and vector projection method to derive an adaptive H ∞ -control, sufficient conditions with high precision are given to guarantee both robust asymptotic stability and disturbance attenuation of the LPV systems with unknown constant parameters. The applicability of the proposed design method is illustrated on a simple example.
In this paper, we deal with the issue of robust delay-independent asymptotic stability and robust disturbance attenuation problem for linear parameter-dependent systems. Using Hamiltonian-Jacoby-Isaac approach, a para...
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In this paper, we deal with the issue of robust delay-independent asymptotic stability and robust disturbance attenuation problem for linear parameter-dependent systems. Using Hamiltonian-Jacoby-Isaac approach, a parameter-dependent LMI optimization is obtained. It is shown that by utilizing polynomial parameter-dependent quadratic Lyapunov functions, a parameter-dependent LMI optimization problem is derived. Therefore, state feedback control is determined by solving a parameter-independent LMI. Finally, the applicability of the proposed design is illustrated on a simple example
Many researchers have been interested in approximation properties of fuzzy logic systems (FLS), which like neural networks, can be seen as approximation schemes. Almost all of them tackled Mamdani fuzzy model, which w...
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Many researchers have been interested in approximation properties of fuzzy logic systems (FLS), which like neural networks, can be seen as approximation schemes. Almost all of them tackled Mamdani fuzzy model, which was shown to have many interesting features. This paper aims to present an alternative for traditional inference mechanisms and CRI method. The most attractive advantage of this new method is its higher robustness with respect to changes in rule base and ability to operate when latter is sparse. In this paper interpolation with high order polynomials and /spl beta/-function is reported.
This paper explores feedback controller design for cavity flows based on reduced-order models derived using Proper Orthogonal Decomposition (POD) along with Galerkin projection method. Our preliminary analysis shows t...
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This paper explores feedback controller design for cavity flows based on reduced-order models derived using Proper Orthogonal Decomposition (POD) along with Galerkin projection method. Our preliminary analysis shows that the equilibrium of the POD model is unstable and a static output feedback controller cannot stabilize it. We develop Linear Quadratic (LQ) optimal state feedback controllers and LQ optimal observers for the linearized models. The linear controllers and observers are applied to the nonlinear system using simulations. The controller robustness is numerically tested with respect to different POD models generated at different forcing frequencies. An estimation for the region of attraction of the linear controllers is also provided.
We present explicit bounds on the classical communication cost and inefficiency of entanglement dilution via the Lo-Popescu protocol, for the case of two-term (single-qubit) entangled states. By considering a two-stag...
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We present explicit bounds on the classical communication cost and inefficiency of entanglement dilution via the Lo-Popescu protocol, for the case of two-term (single-qubit) entangled states. By considering a two-stage dilution, we consequently use prior results to obtain meaningful bounds on the classical communication cost and inefficiency of dilution between two-term partially entangled states
In this paper, new types of recursive least square (RLS) algorithms, without using the initial information of a parameter or a state to be estimated, are proposed. The proposed RLS algorithm is first obtained for a ge...
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In this paper, new types of recursive least square (RLS) algorithms, without using the initial information of a parameter or a state to be estimated, are proposed. The proposed RLS algorithm is first obtained for a generic linear model and is then extended to a state estimator for a stochastic state-space model. Compared with the existing algorithms, the proposed RLS algorithms are simpler and more numerically stable. It is shown, by simulation studies, that the proposed RLS algorithms have better numerical stability for digital computation than existing algorithms.
We propose a wide class of distillation schemes for multi-partite entangled states that are CSS-states. Our proposal provides not only superior efficiency, but also new insights on the connection between CSS-states an...
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ISBN:
(纸本)0780391500
We propose a wide class of distillation schemes for multi-partite entangled states that are CSS-states. Our proposal provides not only superior efficiency, but also new insights on the connection between CSS-states and bipartite graph states. We then consider the applications of our distillation schemes for two cryptographic tasks - namely, (a) conference key agreement and (b) quantum sharing of classical secrets. In particular, we construct "prepare-and-measure" protocols. Also we study the yield of those protocols and the threshold value of the fidelity above which the protocols can function securely. Surprisingly, our protocols function securely even when the initial state does not violate the standard Bell-inequalities for GHZ states. Experimental realization involving only bipartite entanglement is also suggested
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