基于粒子濾波的間歇過程時滯狀態(tài)估計方法
[Abstract]:Intermittent process is widely used in all kinds of production industries and plays a more and more important role in the development of national economy. However, due to the sensor detection technology, the key variables are difficult to realize on-line detection, and the batch process is highly nonlinear, non-Gao Si and time-varying, which makes the monitoring and optimization of the production process extremely difficult. Based on the state space model of batch process, the state estimation method can estimate the key variables accurately by filtering according to the statistical rule of detection value. In the actual production process, there are two kinds of detection values: the on-line detection value and the off-line detection value, the on-line detection value has no delay but the precision is low, the off-line detection value has the delay but the precision is high. On the other hand, the typical batch process has the characteristic of periodic batch production. Considering the existence of time-delay detection values and the characteristics of multiple batches, the research contents of state estimation for batch processes are as follows: (1) for the key variables in batch processes, the on-line detection accuracy is low, and the off-line detection delay is large. Based on Bayesian method, a state estimation method based on time-delay detection value information is proposed. In view of the different sampling periods of on-line and off-line detection values, only on-line detection values and two detection values coexist to analyze. This algorithm is based on particle filter algorithm and extends it based on Bayesian method to realize the information fusion of two detection values. The experimental results of digital simulation and biological hydrogen production process show that the proposed method can deal with the state estimation problem with time-delay detection value, and the effect is better than that without considering the time-delay detection value. (2) in view of the multi-batch characteristics of the batch process, Two dimensional state space models of time dimension and batch dimension are established, and a delay state estimation algorithm based on two dimensional state space model is proposed, considering the information of time delay detection value at the same time. Based on Bayesian method and forward / backward smoothing, the estimation effect is improved with the increase of batch dimension. The effect of state estimation with time delay detection value is better than that without time delay detection value. The simulation results in digital simulation and biological hydrogen production show that the algorithm is effective. (3) aiming at the problem that the two-dimension state space model is difficult to establish accurately, the multi-batch and repeated characteristics of batch process are combined. A delay state estimation algorithm based on iterative learning is proposed. According to the estimation state and the measurement model of the same sampling time in the previous batch, the algorithm estimates the expected detection value at that time, and the tracking error is obtained from the real detection value, and the estimation accuracy of the current batch is improved by iterating the error. Bayesian method and forward / backward smoothing are also used to fuse the time delay detection values. Finally, the effectiveness and practicability of the proposed method are verified by the digital simulation and the simulation application in the biological hydrogen production process.
【學(xué)位授予單位】:江南大學(xué)
【學(xué)位級別】:碩士
【學(xué)位授予年份】:2017
【分類號】:TN713
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