دانلود رایگان مقاله فیلتر سیمپلکس برای تخمین حالت سیستم های غیر خطی

ABSTRACT

 This paper introduces a new filter for nonlinear systems state estimation. The new filter formulates the state estimation problem as a stochastic dynamic optimization problem and utilizes a new stochastic method based on simplex technique to find and track the best estimation. The vertices of the simplex search the state space dynamically in a similar scheme to the optimization algorithm, known as Nelder-Mead simplex. The parameters of the proposed filter are tuned, using an information visualization technique to identify the optimal region of the parameters space. The visualization is performed using the concept of parallel coordinates. The proposed filter is applied to estimate the state of some nonlinear dynamic systems with noisy measurement and its performance is compared with other filters.

6. Conclusion

In this paper, a new heuristic filter was proposed for nonlinear systems state estimation. The proposed filter formulates the state estimation problem as a stochastic dynamic optimization problem. A new stochastic method, based on Nelder-Mead simplex, was proposed to solve this problem. The proposed scheme introduces a new filter, called simplex filter. This filter uses reflection, contraction and expansion operators to search the state space dynamically, in a scheme similar to the simplex algorithm. The parameters of the new filter were tuned utilizing a sample based visualization technique. This technique visualizes the proper ranges of the parameters on a series of parallel coordinates. The performance of the new filter was evaluated using a series of benchmarks and the results were compared with other filters. Numerical results show that simplex filter has a great performance in solving nonlinear state estimation problems.