对于具有不确定量的奇异摄动系统 ,利用 L yapunov稳定性理论研究其稳定鲁棒控制问题。在一定条件下得到了任一理想奇异摄动系统的稳定鲁棒控制 ,也是不确定奇异摄动系统具有一定鲁棒界的稳定控制。给出了闭环奇异摄动系统渐近稳定的条...对于具有不确定量的奇异摄动系统 ,利用 L yapunov稳定性理论研究其稳定鲁棒控制问题。在一定条件下得到了任一理想奇异摄动系统的稳定鲁棒控制 ,也是不确定奇异摄动系统具有一定鲁棒界的稳定控制。给出了闭环奇异摄动系统渐近稳定的条件 ,讨论了鲁棒界及其变化范围。展开更多
A robust finite-horizon Kalman filter is designed for linear discrete-time systems subject to norm-bounded uncertainties in the modeling parameters and missing measurements.The missing measurements were described by a...A robust finite-horizon Kalman filter is designed for linear discrete-time systems subject to norm-bounded uncertainties in the modeling parameters and missing measurements.The missing measurements were described by a binary switching sequence satisfying a conditional probability distribution,the commonest cases in engineering,such that the expectation of the measurements could be utilized during the iteration process.To consider the uncertainties in the system model,an upperbound for the estimation error covariance was obtained since its real value was unaccessible.Our filter scheme is on the basis of minimizing the obtained upper bound where we refer to the deduction of a classic Kalman filter thus calculation of the derivatives are avoided.Simulations are presented to illustrate the effectiveness of the proposed approach.展开更多
基金Supported by the National Natural Science Foundation for Outstanding Youth(61422102)
文摘A robust finite-horizon Kalman filter is designed for linear discrete-time systems subject to norm-bounded uncertainties in the modeling parameters and missing measurements.The missing measurements were described by a binary switching sequence satisfying a conditional probability distribution,the commonest cases in engineering,such that the expectation of the measurements could be utilized during the iteration process.To consider the uncertainties in the system model,an upperbound for the estimation error covariance was obtained since its real value was unaccessible.Our filter scheme is on the basis of minimizing the obtained upper bound where we refer to the deduction of a classic Kalman filter thus calculation of the derivatives are avoided.Simulations are presented to illustrate the effectiveness of the proposed approach.
