This paper focuses on cyber-security issues of networked control systems in closed-loop forms from the perspective of quantized sampled-datasystems. Quantization of control inputs adds quantization error to the plant...
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This paper focuses on cyber-security issues of networked control systems in closed-loop forms from the perspective of quantized sampled-datasystems. Quantization of control inputs adds quantization error to the plant input, resulting in certain variation in the plant output. On the other hand, sampling can introduce non-minimum phase zeros in discretized systems. We consider zero-dynamics attacks, which is a class of false data injection attacks utilizing such unstable zeros. Although non-quantized zero-dynamics attacks are undetectable from the plant output side, quantized attacks may be revealed by larger output variation. Our setting is that the attack signal is applied with the same uniform quantizer used for the control input. We evaluate the attack stealthiness in the closed-loop system setting by quantifying the output variation. Specifically, we characterize the cases for static and dynamic quantization in the attack signal, while keeping the control input statically quantized. Then we demonstrate that the attacker can reduce such output variation with a modified approach, by compensating the quantization error of the attack signal inside the attack dynamics. We provide numerical examples to illustrate the effectiveness of the proposed approaches. We show that observing the quantized control input value by a mirroring model can reveal the zero-dynamics attacks.
Zero order hold discrete-time models of sampleddata plants with multiple input and output delays are derived. The delay is not assumed to be an integral multiple of the sample period, nor is it assumed identical for ...
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Zero order hold discrete-time models of sampleddata plants with multiple input and output delays are derived. The delay is not assumed to be an integral multiple of the sample period, nor is it assumed identical for all inputs or outputs. It is proven that the discrete model is reachable iff (i) the discrete model with zero integral delay is reachable, (ii) the number of plant inputs is greater than or equal to the number of outputs and (iii) there are no transmission zeros at the origin.
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 t...
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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.
In this paper, we study the invertibility of a sampleddata system. Necessary and sufficient invertibility conditions are given explicitly in terms of transfer function zeros. It is shown that many systems including t...
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In this paper, we study the invertibility of a sampleddata system. Necessary and sufficient invertibility conditions are given explicitly in terms of transfer function zeros. It is shown that many systems including those with "unstable" zeros or those with more inputs than outputs are in fact stably invertible. Detailed design procedures to achieve the system inverse are given. A minimum sensitivity Inverse with respect to under-modeling and noise is also provided in the paper.
An adaptive technique is developed which iteratively determines the time delay between two sampled signals that are highly correlated. Although the procedure does not require a priori information on the input signals,...
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An adaptive technique is developed which iteratively determines the time delay between two sampled signals that are highly correlated. Although the procedure does not require a priori information on the input signals, it does require that the signals have a unimodal or periodically unimodal cross-correlation function. The adaptive delay algorithm uses a gradient technique to find the value of the adaptive delay that minimizes the mean-squared (MS) error function. This iterative algorithm is similar to the adaptive filter coefficient algorithm developed by Widrow. However, the MS error function for the adaptive delay is not quadratic, as it is in the adaptive filter. A statistical analysis determines the value of the convergence parameter which effects rapid convergence of the adaptive delay. This convergence parameter is a function of the power of the input signal. Computer simulations are presented which verify that the adaptive delay correctly estimates the time delay difference between two sinusoids, including those in noisy environments. The adaptive delay is also shown to perform correctly in a time delay tracking application.
This paper is concerned with the problem of controller design in the case of asynchronous sampled data systems. Optimal LQG controllers are obtained for the class of two-rate systems where all the control inputs are h...
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This paper is concerned with the problem of controller design in the case of asynchronous sampled data systems. Optimal LQG controllers are obtained for the class of two-rate systems where all the control inputs are held at a rate which is asynchronously related to the rate with which all of the outputs are sampled. Furthermore for this class, a parameterization of all stabilizing controllers is provided.
In this note, we investigate zero locations of FIR linear systems that are finite length approximations of sampled continuous-time systems. For linear systems with rational transfer functions, it is shown that with a ...
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In this note, we investigate zero locations of FIR linear systems that are finite length approximations of sampled continuous-time systems. For linear systems with rational transfer functions, it is shown that with a relative degree higher than two and a short sampling interval, the resultant FIR sampleddata system is always a nonminimum phase. (C) 1999 Elsevier Science Ltd. All rights reserved.
For sampled data systems, it is possible to express discrete time convolution in terms of appropriate matrix multiplication. A limiting process then yields the steady-state response to a periodic input. The matrix inv...
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For sampled data systems, it is possible to express discrete time convolution in terms of appropriate matrix multiplication. A limiting process then yields the steady-state response to a periodic input. The matrix involved in this operation is a circulant matrix based on the impulse response of the system. Circulant matrices are known to have useful structural properties and permit the association of the frequency domain with the time domain. The use of circulants in the analysis of nonlinear systems provides the means for converting the unwieldy nonlinear equations of continuous systems to simple matrix multiplications. It is then possible to apply numerical range techniques and rederive the circle criterion. The direct application of this approach yields a criterion which is less conservative than that obtained by the simple application of the small-gains theorem. Use of the approach in conjunction with a loop transformation, on the other hand, provides an alternative derivation of the circle criterion for discrete systems. The method can be extended to multivariable systems, and, because of its association with the time domain, it permits the assessment of system stability in the face of imperfect system descriptions, namely truncated impulse responses.
A technique for application of the popular fast Fourier transform (FFT) to the system identification problem is outlined. Smoothing is obtained inherently in the transform and additionally by redundancy in the data. A...
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A technique for application of the popular fast Fourier transform (FFT) to the system identification problem is outlined. Smoothing is obtained inherently in the transform and additionally by redundancy in the data. An iterative technique is discussed for the case of nonzero initial conditions and to avoid problems due to the circular nature of convolutions computed by the discrete Fourier transform (DFT).
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