IDENTIFICATION BY A COMBINED SMOOTHING NONLINEAR PROGRAMMING ALGORITHM
Copyright policy )IDENTIFICATION BY A COMBINED SMOOTHING NONLINEAR PROGRAMMING ALGORITHM
Abstract An offline algorithm is developed for identification of parameters of linear, stationary, discrete, dynamic systems with known control inputs and subjected to process and measurement noise with known statistics. Results of the algorithm include estimates of the parameters and smoothed estimates of the state and process noise sequences. The problem is stated as the minimization of a quadratic performance index. This minimization problem is then converted to a nonlinear programming problem for determining the optimum parameter estimates. The new algorithm is shown to be cost competitive with the currently popular filtering-sensitivity function method. A third order example with simulated data is presented for comparison.
- Stanford University United States
Estimation and detection in stochastic control theory, Computational methods in stochastic control, Identification in stochastic control theory, minimization problem, filtering- sensitivity function method, smoothing state estimation, filtering, Filtering in stochastic control theory, computational methods, Discrete-time control/observation systems, Nonlinear programming, Linear systems in control theory, combined smoothing nonlinear programming algorithm, discrete systems, nonlinear programming, offline algorithm, identification, parameter estimation, Kalman filtering
Estimation and detection in stochastic control theory, Computational methods in stochastic control, Identification in stochastic control theory, minimization problem, filtering- sensitivity function method, smoothing state estimation, filtering, Filtering in stochastic control theory, computational methods, Discrete-time control/observation systems, Nonlinear programming, Linear systems in control theory, combined smoothing nonlinear programming algorithm, discrete systems, nonlinear programming, offline algorithm, identification, parameter estimation, Kalman filtering
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