A simultaneous combined optimization of the reference trajectory, perturbation controller, and perturbation estimator .for stochastic non-linear systems can improve performance over quadratic synthesis optimization around a nominal trajectory. Summnry--A new approach for optimum control of stochast
Estimation of transfer functions in closed loop stochastic systems
โ Scribed by M.B. Priestley
- Publisher
- Elsevier Science
- Year
- 1969
- Tongue
- English
- Weight
- 843 KB
- Volume
- 5
- Category
- Article
- ISSN
- 0005-1098
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โฆ Synopsis
We compare two methods of estimating transfer functions from observed records of input and output when the output contains an additive noise component of unknown statistical structure. The two methods correspond to (a) a "weighted least-squares" estimate (/fl) and (b) a "simple least-squares" estimate (-42). The estimate .41 is the more attractive theoretically, since it is equivalent to a maximum likelihood approach when the system is open-loop and the noise process is Gaussian, but it is difficult to compute numerically. On the other hand, -42 is the much simpler estimate to compute and is the one most commonly used in practice, particularly as a first approximation in an iterative approach. It should be noted, however, that both ~l and ,'[2 will, in general, be biased in the case of closed-loop systems. In this paper we determine conditions under which -'[2 will be identical with, or close to, -41 in the case of a closed-loop system in which the feedback is linen with an additive noise component.
๐ SIMILAR VOLUMES
An indirect method is introduced that is able to estimate consistently the transfer function of a linear plant on the basis of data obtained from closed loop experiments, even in the situation where the model of the noise disturbance on the data is not accurate. Moreover, the method allows approxima
Necessary and sufficient identifiability conditions for multi-input multi-output linear dynamic systems operating in closed loop indicate that under certain conditions physically meaningful models for forward and reverse paths can be uniquely determined.