Using Extended Kalman Filter to Observe State Parameters of the Twin Rotor MIMO System in Order to Install Model Predictive Control Algorithm Based Phisical Model
|International Journal of Electrical and Electronics Engineering|
|© 2017 by SSRG - IJEEE Journal|
|Volume 4 Issue 12|
|Year of Publication : 2017|
|Authors : Huong T.M. Nguyen|
Huong T.M. Nguyen, "Using Extended Kalman Filter to Observe State Parameters of the Twin Rotor MIMO System in Order to Install Model Predictive Control Algorithm Based Phisical Model" SSRG International Journal of Electrical and Electronics Engineering 4.12 (2017): 1-7.
Huong T.M. Nguyen,(2017). Using Extended Kalman Filter to Observe State Parameters of the Twin Rotor MIMO System in Order to Install Model Predictive Control Algorithm Based Phisical Model. SSRG International Journal of Electrical and Electronics Engineering 4(12), 1-7.
Most modern control methods including model predictive control (MPC) need to be measured or observed the state vectors of the object model. However, for real systems are often not fully measured state variables. Meanwhile, we must use the state observations to estimate the state vectors of the object model. One of the effective solutions to estimate the state of the system is to use Kalman filter for linear systems and extended Kalman filter (EKF) for nonlinear systems. This paper, we will mention the construction of extended Kalman filter to estimate the state parameters of the Twin rotor MIMO system (TRMS) and use this estimation to conduct tests on the real system so as to check the solution of the optimal problem by SQP algorithm mentioned in  and use variational method to solve the optimal problems, announced in  for the TRMS. Specifically the SQP algorithm and variational methods in the MPC given in , , .
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Model predictive control, the Twin rotor MIMO system (TRMS), Kalman filter, state parameters, state observation.