Control algorithms for discrete delayed systems with unknown inputs and model parameters using nonparametric technique

The paper deals with the control algorithms for discrete delayed systems with unknown inputs (disturbances) and model parameters. The control algorithm is based on the local criterion with using Kalman filters and nonparametric estimators. The example is given to illustrate the proposed approach.

[1]  Bart De Moor,et al.  Unbiased minimum-variance input and state estimation for linear discrete-time systems , 2007, Autom..

[2]  Konstantin S. Kim,et al.  Control strategies for discrete delayed systems with unknown input using nonparametric algorithms , 2016, 2016 International Conference on Information and Digital Technologies (IDT).

[3]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[4]  Gennady M. Koshkin,et al.  Filtering and prediction for discrete systems with unknown input using nonparametric algorithms , 2014, The 10th International Conference on Digital Technologies 2014.

[5]  李幼升,et al.  Ph , 1989 .

[6]  Jean-Claude Hennet A globally optimal local inventory control policy for multistage supply chains , 2009 .

[7]  Gennady M. Koshkin,et al.  Kalman Filtering and Forecasting Algorithms with Use of Nonparametric Functional Estimators , 2016 .

[8]  Haralambos Sarimveis,et al.  An adaptive model predictive control configuration for production-inventory systems , 2008 .

[9]  V. I. Smagin State Estimation for Nonstationary Discrete Systems with Unknown Input Using Compensations , 2015 .

[10]  Konstantin S. Kim,et al.  Locally Optimal Inventory Control with Time Delay in Deliveries and Incomplete Information on Demand , 2016, 2016 Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO).

[11]  A. V. Kitaeva,et al.  Semi-recursive nonparametric identification in the general sense of a nonlinear heteroscedastic autoregression , 2010 .

[12]  Gennady M. Koshkin,et al.  Kalman filtering and control algorithms for systems with unknown disturbances and parameters using nonparametric technique , 2015, 2015 20th International Conference on Methods and Models in Automation and Robotics (MMAR).

[13]  E. Nadaraya On Estimating Regression , 1964 .

[14]  G. S. Watson,et al.  Smooth regression analysis , 1964 .

[15]  M.M. Arefi,et al.  Nonlinear Model Predictive Control of Chemical Processes with a Wiener Identification Approach , 2006, 2006 IEEE International Conference on Industrial Technology.

[16]  Eduardo F. Camacho,et al.  Model Predictive Controllers , 2007 .

[17]  G. Conte,et al.  INVENTORY CONTROL BY MODEL PREDICTIVE CONTROL METHODS , 2005 .

[18]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[19]  Vladimir Dombrovskii,et al.  Model predictive control for constrained systems with serially correlated stochastic parameters and portfolio optimization , 2015, Autom..