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New state estimator of nonlinear discrete-time systems

机译：非线性离散时间系统的新状态估计

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摘要

In this paper, a new technique is developed to estimate the states of both deterministic and stochastic discrete-time nonlinear systems. The stochastic system can be uncertain. The corrupting input and/or the output noise vectors may be either Gaussian or non-Gaussian zero-mean sequences. The proposed filter is based on pole placement technique, in which a set of constraints are imposed of the estimated outputs. The stability of the estimator is rigorously analyzed. An extension to the developed estimator is proposed to deal with constrained estimation problems. To illustrate the effectiveness and simplicity of the developed technique, illustrative examples are presented. Simulations results show that, for the deterministic case, the developed procedure leads to better results when compared with High-gain observer, Thau's filter, and the Iterative Regularized Least Square estimator. On the other hand, for the stochastic case, the proposed estimator is superior to the extended Kalman filter and the iterative constrained estimator (ICE) with non-ideal situations where the statistics of the noise signals and/or the system parameters are unknown, or the noise signals are non-Gaussian. Copyright (c) 2016 John Wiley & Sons, Ltd.
机译：在本文中，开发了一种新技术来估计确定性和随机离散时间非线性系统的状态。随机系统可能是不确定的。损坏的输入和/或输出噪声矢量可以是高斯或非高斯零平均序列。所提出的滤波器基于极点放置技术，其中对估计的输出施加一组约束。严格分析了估计器的稳定性。提出了发达估算器的扩展，以处理受约束的估计问题。为了说明所开发技术的有效性和简单性，提出了说明性示例。模拟结果表明，对于确定性案例，与高增益观察者，Thau的过滤器和迭代正规最小二乘估计相比，开发过程会导致更好的结果。另一方面，对于随机壳体，所提出的估计器优于扩展卡尔曼滤波器和迭代约束估计器（ICE），其中具有非理想情况的噪声信号和/或系统参数的统计，或者噪声信号是非高斯。版权所有（c）2016 John Wiley＆Sons，Ltd。

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