Showing papers by "John B. Moore published in 1981"

Detectability and Stabilizability of Time-Varying Discrete-Time Linear Systems

Abstract: The concepts of detectability and stabilizability are explored for time-varying systems. We study duality, invariance under feedback, an extended version of the lemma of Lyapunov, existence of stabilizing feedback laws, linear quadratic filtering and control, and the existence of approximate canonical forms.

Performance and robustness trades in LQG regulator design

Abstract: The use of fictitious noise processes to improve the robustness of controllers based on linear quadratic Gaussian synthesis is studied in this paper. By constructing the noise process so that its effective energy is primarily in the frequency bands where robustness is lacking, improvements can be made without changing the closed-loop characteristics outside those frequency bands. Specifically, the use of fictitious colored input noise rather than white input noise, as in earlier studies, to improve control loop gain and phase margins, permits a more desirable trade-off between robustness and performance of the nominal plant.

Adaptive Equalization Via Fast Quantized-State Methods

Abstract: In adaptive equalization, there is a tradeoff between convergence rate of the equalizer tap coefficients, the computational speed for each adjustment, the implementation complexity, and algorithm robustness. Parameter update schemes called quantized state (QS) schemes, and fast versions of these schemes termed fast quantized state (FQS) schemes developed within a related context, are here applied to achieve attractive tradeoff options not previously available for adaptive equalization. Three novel simplifications to the QS schemes are introduced and justified by their performance characteristics in adaptive equalization. One simplification is to abandon the likeness to the method of instrumental variables (IV), where the "instrumental variable" is the quantized state vector, and introduce more quantization. Another simplification is to replace asymptotically Toeplitz matrices, or their inverses, by Toeplitz matrices to take advantage of fast schemes for updating QS schemes or taking their inverses.

Global convergence of output error recursions in colored noise

Abstract: This paper presents a variation on a known extended least squares algorithm of the "output error" or "parallel model" type. Under reasonable conditions, the algorithms achieve global convergence of the one-step-ahead prediction error to the additive independent (possible colored) measurement noise. The convergence of the algorithms proposed is not critically sensitive to the color in the noise as are related extended least squares schemes which require a simultaneous noise model identification, nor is the convergence critically sensitive to the input signals as are realizations of the method of instrumental variables. The algorithms are also simpler to implement than for the competing schemes. In the paper, there is also studied an add on scheme which consists of additional processing of the prediction errors to achieve simultaneous noise model identification, and improved prediction. Such a scheme is attractive from the computational cost point of view. Global convergence results are developed for the algorithms based on martingale convergence theorems as in earlier theories for extended least squares schemes. The key contribution of the paper as far as the theory is concerned is to show how to cope with the colored noise in the martingale framework.

In colored noise

Abstract: This paper presents a variation on a known extended least squares algorithm of the "output error" or "parallel model" type. Under reasonable conditions, the algorithms achieve global convergence of the one-step-ahead prediction error to the additive independent (possible colored) measurement noise. The convergence of the algorithms proposed is not critically sensitive to the color in the noise as are related extended least squares schemes which require a simultaneous noise model identification, nor is the convergence critically sensitive to the input signals as are realizations of the method of instrumental variables. The algorithms are also simpler to implement than for the competing schemes. In the paper, there is also studied an add on scheme which consists of additional processing of the prediction errors to achieve simultaneous noise model identification, and improved prediction. Such a scheme is attractive from the computational cost point of view. Global convergence results are developed for the algorithms based on martingale convergence theorems as in earlier theories for extended least squares schemes. The key contribution of the paper as far as the theory is concerned is to show how to cope with the colored noise in the martingale framework.