| Summary: | 碩士 === 國立交通大學 === 電控工程研究所 === 101 === The purpose of this research is to design a state observer specifically for networked controlled systems. We propose a probabilistic model for the network controlled system which consists of a Markov-chained random variable to represent the transferring status of each data packet.
Because of the inherent probabilistic nature of the proposed model, we design the observer by using particle filtering techniques. To improve the performance, both resampling and regularization methods are incorporated into the particle filter, and the importance sampling function is carefully selected. In addition, we simplify the way to update the weightings of the samples in order to reduce the demanding computational power and the need for memory space, while to maintain reasonable state estimation accuracy.
In the simulation and experiment parts, a heating system is chosen. The system has multiple sensing nodes for a remote application, and it can change various state variables with a few control commands. After collecting the temperature data of the system, the data are sent through a real network to the observer for state estimation. The result of the estimation is then compared with the outcome of a traditional Kalman filter in order to verify the feasibility and effectiveness of the proposed method.
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