2008
DOI: 10.1002/cjce.5450840210
|View full text |Cite
|
Sign up to set email alerts
|

State-Space Digital PI Controller Design for Linear Stochastic Multivariable Systems with Input Delay

Abstract: Dans cet article, on propose un schéma de contrôle PI numérique centralisé pour des systèmes multivariables stochastiques linéaires avec un retard d'entrée. On utilise une approche à régulateur quadratique linéaire discret (LQR) avec placement de pôles pour obtenir un suivi des points de consigne satisfaisant avec une stabilité en boucle fermée garantie. En outre, la forme innovante du gain de Kalman est employée pour l'estimation des états sans connaissance préalable des propriétés de bruit. Comparé aux conce… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2016
2016
2017
2017

Publication Types

Select...
2
1

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(1 citation statement)
references
References 25 publications
0
1
0
Order By: Relevance
“…Simulation results are compared for the following two controllers: A linear quadratic regulator (LQR) described in . The LQR is defined as:Control force u is determined by minimizing the quadratic objective function J=falsefalsek=0[xT(kT)Qx(kT)+uT(kT)Ru(kT)] where Q ⩾0 and R >0, the state feedback control law which minimizes J is expressed as u ( k T )=− G x ( k T ).Therefore, by solving for P = P T ⩾0 the discrete Riccati equation P =Φ T P Φ+ Q −(Γ T P Φ) T ( R +Γ T P Γ)(Γ T P Φ) the optimal controller gain is given as G =( R +Γ T P Γ) −1 Γ T P ΦThe feedback gain for the LQR is G=0.05023.79572.2997 An anti‐windup controller Anti‐windup controller consists of a PID controller and an anti‐windup compensator architecture described in , with θ =[ K P , K I , K D ] T contains the PID gains to be tuned where K P , K I and K D are the proportional, integral, and derivative gains, respectively, and K T compensation parameter.The PID gain is determined so that the eigenvalues closed loop system are located in the unit circle on the complex plane, the following controller parameters were obtained: θ=[1.6,0.085,1.12]TandKT=1Tc=0.02 …”
Section: Numerical Applicationmentioning
confidence: 99%
“…Simulation results are compared for the following two controllers: A linear quadratic regulator (LQR) described in . The LQR is defined as:Control force u is determined by minimizing the quadratic objective function J=falsefalsek=0[xT(kT)Qx(kT)+uT(kT)Ru(kT)] where Q ⩾0 and R >0, the state feedback control law which minimizes J is expressed as u ( k T )=− G x ( k T ).Therefore, by solving for P = P T ⩾0 the discrete Riccati equation P =Φ T P Φ+ Q −(Γ T P Φ) T ( R +Γ T P Γ)(Γ T P Φ) the optimal controller gain is given as G =( R +Γ T P Γ) −1 Γ T P ΦThe feedback gain for the LQR is G=0.05023.79572.2997 An anti‐windup controller Anti‐windup controller consists of a PID controller and an anti‐windup compensator architecture described in , with θ =[ K P , K I , K D ] T contains the PID gains to be tuned where K P , K I and K D are the proportional, integral, and derivative gains, respectively, and K T compensation parameter.The PID gain is determined so that the eigenvalues closed loop system are located in the unit circle on the complex plane, the following controller parameters were obtained: θ=[1.6,0.085,1.12]TandKT=1Tc=0.02 …”
Section: Numerical Applicationmentioning
confidence: 99%