The Experts below are selected from a list of 8970 Experts worldwide ranked by ideXlab platform
M.m. Livstone - One of the best experts on this subject based on the ideXlab platform.
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Calculation of discrete-time Process Noise statistics for hybrid continuous/discrete-time applications
Optimal Control Applications & Methods, 1996Co-Authors: Jay A. Farrell, M.m. LivstoneAbstract:Discrete-time models of continuous-time plants are commonly required owing to the popular use of computers to implement control and estimation algorithms. When stochastic design techniques such as the discrete-time Kalman filter are utilized, it is necessary to determine the equivalent discrete-time Process Noise statistics from the continuous-time Process Noise statistics. Herein we present a new solution for the required transformation.
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Exact calculations of discrete-time Process Noise statistics for hybrid continuous/discrete time applications
Proceedings of 32nd IEEE Conference on Decision and Control, 1993Co-Authors: J. Farrell, M.m. LivstoneAbstract:Discrete-time models of continuous-time plants are commonly required due to the popular use of computers to implement control and estimation algorithms. When stochastic design techniques are utilized, it is necessary to determine the equivalent discrete-time Process Noise statistics from the continuous-time Process Noise statistics. Several references on the object of discrete-time control and estimation present approximate solutions for the necessary transformation; in this paper exact solutions are discussed.
Dennis S. Bernstein - One of the best experts on this subject based on the ideXlab platform.
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Kalman-filter-based time-varying parameter estimation via retrospective optimization of the Process Noise covariance
2016 American Control Conference (ACC), 2016Co-Authors: Frantisek M. Sobolic, Dennis S. BernsteinAbstract:Retrospective estimation of the Process Noise covariance is performed by minimizing the cumulative state-estimation error based on the innovations. This technique is applied to parameter estimation problems, where the parameters to be estimated are time-varying and thus do not fit in the classical Kalman filter Noise structure. This technique is compared to the standard Kalman filter with a fixed Process Noise covariance as well as an innovations-based adaptive Kalman filter.
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ACC - Kalman-filter-based time-varying parameter estimation via retrospective optimization of the Process Noise covariance
2016 American Control Conference (ACC), 2016Co-Authors: Frantisek M. Sobolic, Dennis S. BernsteinAbstract:Retrospective estimation of the Process Noise covariance is performed by minimizing the cumulative state-estimation error based on the innovations. This technique is applied to parameter estimation problems, where the parameters to be estimated are time-varying and thus do not fit in the classical Kalman filter Noise structure. This technique is compared to the standard Kalman filter with a fixed Process Noise covariance as well as an innovations-based adaptive Kalman filter.
H. Michalska - One of the best experts on this subject based on the ideXlab platform.
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CDC - Robust stabilization of switched linear systems with Wiener Process Noise
49th IEEE Conference on Decision and Control (CDC), 2010Co-Authors: J. Raouf, H. MichalskaAbstract:A constructive approach to robust stabilization of switched systems subject to a Wiener Process Noise is presented. The uncertainties in the system are assumed to be sector bounded. Multiple Lyapunov functions are employed to develop sufficient conditions for a switched stochastic system to be stable in the mean square sense. The stability criterion is expressed in terms of existence of solutions to a set of linear matrix inequalities. State feedback design procedure is proposed to determine a switching rule and a set of associated feedback controller that robustly stabilizes the closed-loop system. A practical application related to the control of stochastic oscillators is provided to show the effectiveness of the proposed approach.
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Robust stabilization of switched linear systems with Wiener Process Noise
49th IEEE Conference on Decision and Control (CDC), 2010Co-Authors: J. Raouf, H. MichalskaAbstract:A constructive approach to robust stabilization of switched systems subject to a Wiener Process Noise is presented. The uncertainties in the system are assumed to be sector bounded. Multiple Lyapunov functions are employed to develop sufficient conditions for a switched stochastic system to be stable in the mean square sense. The stability criterion is expressed in terms of existence of solutions to a set of linear matrix inequalities. State feedback design procedure is proposed to determine a switching rule and a set of associated feedback controller that robustly stabilizes the closed-loop system. A practical application related to the control of stochastic oscillators is provided to show the effectiveness of the proposed approach.
J. Farrell - One of the best experts on this subject based on the ideXlab platform.
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Exact calculations of discrete-time Process Noise statistics for hybrid continuous/discrete time applications
Proceedings of 32nd IEEE Conference on Decision and Control, 1993Co-Authors: J. Farrell, M.m. LivstoneAbstract:Discrete-time models of continuous-time plants are commonly required due to the popular use of computers to implement control and estimation algorithms. When stochastic design techniques are utilized, it is necessary to determine the equivalent discrete-time Process Noise statistics from the continuous-time Process Noise statistics. Several references on the object of discrete-time control and estimation present approximate solutions for the necessary transformation; in this paper exact solutions are discussed.
Frantisek M. Sobolic - One of the best experts on this subject based on the ideXlab platform.
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Kalman-filter-based time-varying parameter estimation via retrospective optimization of the Process Noise covariance
2016 American Control Conference (ACC), 2016Co-Authors: Frantisek M. Sobolic, Dennis S. BernsteinAbstract:Retrospective estimation of the Process Noise covariance is performed by minimizing the cumulative state-estimation error based on the innovations. This technique is applied to parameter estimation problems, where the parameters to be estimated are time-varying and thus do not fit in the classical Kalman filter Noise structure. This technique is compared to the standard Kalman filter with a fixed Process Noise covariance as well as an innovations-based adaptive Kalman filter.
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ACC - Kalman-filter-based time-varying parameter estimation via retrospective optimization of the Process Noise covariance
2016 American Control Conference (ACC), 2016Co-Authors: Frantisek M. Sobolic, Dennis S. BernsteinAbstract:Retrospective estimation of the Process Noise covariance is performed by minimizing the cumulative state-estimation error based on the innovations. This technique is applied to parameter estimation problems, where the parameters to be estimated are time-varying and thus do not fit in the classical Kalman filter Noise structure. This technique is compared to the standard Kalman filter with a fixed Process Noise covariance as well as an innovations-based adaptive Kalman filter.
