4.6 Article

On the Estimation and Control of Nonlinear Systems With Parametric Uncertainties and Noisy Outputs

期刊

IEEE ACCESS
卷 6, 期 -, 页码 31968-31973

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2846483

关键词

Control systems; nonlinear systems; stochastic systems; state estimation; filtering

资金

  1. Consejo Nacional de Ciencia y Tecnologia through scholarship Sistema Nacional de Investigadores
  2. Instituto Politecnico Nacional [20170629, 20180095]
  3. Estimulo al Desempeno de los Investigadores
  4. Comision de Operacion y Fomento de Actividades Academicas
  5. Beca de Estimulo Institucional de Formacion de Investigadores

向作者/读者索取更多资源

In real-time problems, the possibilities of having a precise mathematical model describing the dynamics of the nonlinear system are scarce. Besides, the measurements invariably are tainted with noise which makes the problem of estimating the actual states of the system more difficult. The most common way of solving this issue involves the application of the Kalman Filter (KF) or the Extended Kalman Filter (EKF), for linear and nonlinear systems, respectively; although in both cases, the estimation heavily relies on linear techniques. In a different way, the James-Stein Filter provides a robust approach to estimate linear and nonlinear systems under parametric uncertainties of the mathematical model. In this brief note, a slightly different James-Stein State Estimator (JSSE), named Modified James-Stein State Estimator (JSSE-M), is presented as an alternative to filtering the states of nonlinear systems within a control scheme. The main contribution of this paper is the comparison of performance between KF, EKF, JSSE, and JSSE-M when they are used on a relatively complex nonlinear system which is extremely dependent on its parameters, namely the quadrotor. In this sense, some interesting comparisons focused on both, the effectiveness and processing time are provided.

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