Adaptive state estimation method for autoregressive moving average system and closed-loop control system

Abstract

The invention discloses a method and device for carrying out adaptive state estimation on an autoregressive moving average system with additive output noise and control variables and a closed-loop control system. The method comprises the following steps: performing state space implementation on the autoregressive moving average system of which a preset application background is provided with additive output noise and a control variable; modeling the additive output noise of the autoregressive moving average system by utilizing an L2 norm regular term; simultaneously estimating the state valueand the output noise of the autoregressive moving average system by using a regularization least square method; and taking a regularization parameter for adjusting the detection intensity of the output noise as an optimal regularization parameter when an error between the sample variance of an estimated residual error and the variance of actual system noise is minimized. According to the invention, the negative influence of the output noise caused by the autocorrelation can be eliminated under the condition that the output of the system has the autocorrelation, and the method for performing adaptive unbiased estimation on the state value of the system is provided.

Description

technical field [0001] The present application relates to the technical field of discrete linear system filtering, in particular to a method, device and closed-loop control system for adaptive state estimation of an autoregressive moving average system with additive output noise and control variables. Background technique [0002] In control engineering, the autoregressive moving average system with control variables is widely used to describe the actual physical system. Autoregressive moving average system with control variables is a special discrete time series system, which is widely used in modeling and control design of actual physical systems in control engineering. The general autoregressive moving average system with control variables can achieve a good balance between the complexity of the model and the accuracy of the modeling when describing many real physical systems or dynamic processes. But because it does not take into account the influence of noise acting on...

AI Technical Summary

Problems solved by technology

However, how to estimate the state of this type of system has not been well studied and solved

[0005] To sum up, the existing problems in the existing technology are: the existing estimators are only designed for the general autoregressive moving average system, based on the assumption that the output of the system can be completely and accurately measured

When the system output is disturbed by noise, due to the autocorrelation of the system output, the state estimation value at subsequent moments will also be seriously disturbed, so the existing estimator cannot provide the state value of the autoregressive moving average system with output noise. unbiased and accurate estimate

When the output of the system has autocorrelation characteristics, commonly used estimators, such as Kalman filter, maximum likelihood estimator based on t distribution, etc., are difficult to solve the problem that the output noise propagates through the autocorrelation characteristics of the system, which leads to It is difficult to accurately estimate the state of an autoregressive moving average system with output noise

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