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State estimation method for nonlinear dynamic system under non-Gaussian noise

A nonlinear dynamic, non-Gaussian noise technology, applied in computing, computer components, pattern recognition in signals, etc., can solve problems such as system performance impact

Pending Publication Date: 2021-02-26
WENZHOU UNIVERSITY
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Problems solved by technology

[0008] The technical problem to be solved by the embodiments of the present invention is to provide a state estimation method for nonlinear dynamic systems under non-Gaussian noise, which can overcome the impact of non-Gaussian measurement noise that may exist in nonlinear dynamic systems on system performance in the prior art. Effects and deficiencies of filtering techniques

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  • State estimation method for nonlinear dynamic system under non-Gaussian noise
  • State estimation method for nonlinear dynamic system under non-Gaussian noise
  • State estimation method for nonlinear dynamic system under non-Gaussian noise

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Embodiment

[0113] The nonlinear dynamic process model in the present embodiment is a beer fermentation process in the actual process, and its reaction mechanism is expressed as follows:

[0114] Biomass (yeast) + sugar + H 2 O→alcohol+CO 2 +H 2 O (1)

[0115] Choose the state vector as x k =[S k ,X k ,P k ] T , where S k is the substrate (glucose) concentration, X k is the biomass concentration, P k is the alcohol concentration. Under batch conditions, the process can be described by the following discrete dynamic equations:

[0116]

[0117] Among them, w k-1 =[w S,k-1 ,w X,k-1 ,w P,k-1 ] T is the process noise vector, and is assumed to obey a zero-mean Gaussian distribution w k-1 :N(0,0.01 2 ). T c =0.01h is the sampling period; μ S =0.78,μ X =0.058,μ P =0.35 is the model parameter; constant b=0.0251, K S =0.0252,K X =0.7464,K P = 3.2155; the concentration of glucose, biomass and alcohol can be obtained by dielectric measurement process or online measuring ...

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Abstract

The invention provides a state estimation method for a nonlinear dynamic system under non-Gaussian noise. According to the method, a nonlinear dynamic system model comprising a process noise signal and a non-Gaussian measurement noise signal is adopted; the real-time information of the system state is estimated based on the nonlinear dynamic process model and the measurement feedback signal to obtain a state estimation signal, and output feedback is performed; and the state estimation signal is used as the controller input at the next moment, so that the influence of the non-Gaussian measurement noise signal on the system performance is weakened, and the accuracy of state estimation is improved. According to the method, the model prediction information and the non-Gaussian signal measuredby the sensor are combined, and the current optimal state of the system is estimated by adopting the newly proposed extended Kalman filtering algorithm based on the dynamic data correction technology,so that the limitation on the measurement noise distribution in the existing Kalman filtering technology is broken through; and a selectable scheme is provided for solving the state estimation problem of the nonlinear dynamic system under the condition of non-Gaussian measurement noise.

Description

technical field [0001] The invention relates to the technical field of nonlinear dynamic filtering, in particular to a state estimation method of a nonlinear dynamic system under non-Gaussian noise. Background technique [0002] With the rapid rise of a new round of technological revolution marked by "Industry 4.0", advanced process equipment with a high degree of automation and intelligence is required in practice to meet the growing product quality requirements. However, due to the unavoidable interference from the external environment when using sensors to measure data, the information that can accurately reveal the state of the system is usually not directly accessible. In this case, combining process models and sensor measurement data to estimate real-time information of important states plays a key role in improving product profitability, process safety and efficiency. Generally, using a given noisy measurement signal to extract or infer the desired dynamical system s...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00
CPCG06F2218/02
Inventor 张正江胡桂廷闫正兵戴瑜兴黄世沛朱志亮
Owner WENZHOU UNIVERSITY
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