DIRECTSD-a toolbox for polynomial design of multivariable sampled-data systems

Polyakov, K. Yu.; Rosenwasser, Efim N.; Lampe, Bernhard P.

doi:10.1109/cacsd.2004.1393857

Cited by 5 publications

(8 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then A() = 0 1 dFHF RN d 3 FHF RN : (11) Assume that A6. There exists a stable polynomial g() such that gg 3 = 0 1 : (12) Introduce the notation d 1 () = dd G = d FHF GG () = gg 3 dGQ 0 n 3 dN d 3 N : (13) Let be the minimal nonnegative integer such that 3 d 3 N + and g 3 are polynomials in .…”

Section: Equivalent Discrete Problemmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Stochastic SD Control With Preview

Polyakov¹,

Rosenwasser²,

Lampe³

2007

IEEE Trans. Automat. Contr.

View full text Add to dashboard Cite

The problem of -optimal sampled-data (SD) preview control is considered. It is assumed that a stochastic reference signal corrupted with additive colored noise acts upon the system so that future values of this input are known within a preview window . A rigorous frequency-domain solution of the problem is given for SISO SD systems on the basis of the parametric transfer function concept. Numerator and denominator degrees of the optimal controller are calculated explicitly on the basis of initial data. The dependence of the optimal cost function on is investigated and the limiting value as is found. Moreover, it is shown that this limit depends on the relation between and the sampling period.Index Terms-Delay systems, polynomial methods, sampled data systems, stochastic optimal control, transfer functions.

show abstract

Section: Equivalent Discrete Problemmentioning

confidence: 99%

“…Curve ( ) for a stable plant.MATLAB[13], we find the transfer function of the optimal digital controller J = 0:09336. The curve J opt ( ) for this system is shown inFig.…”

mentioning

confidence: 99%

Optimal Stochastic SD Control With Preview

Polyakov¹,

Rosenwasser²,

Lampe³

2007

IEEE Trans. Automat. Contr.

View full text Add to dashboard Cite

show abstract

“…Let the LMI solution be (X , Y ) ∈ L D , with L D being defined in (16). We explain how to construct Y such that (X , Y )…”

Section: Algorithmmentioning

confidence: 99%

“…Step 4: To obtain a reduced-order controller, K (z ), use the standard LMI algorithm, (16), (17), (18), and (24) to calculate a reduced-order output-feedback controller; or use (16), (17), and (31) to obtain a static state-feedback controller.…”

Section: Algorithmmentioning

confidence: 99%

Teaching sampled‐data H_∞ control theory using arm robot experiment system

She

Xin

Ohyama

2011

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

This paper explains how to use an arm robot experiment system to teach sampled‐data H∞ control theory. A design procedure is presented for a digital tracking control system for a continuous plant with structured uncertainties; the target is the positioning control of an arm robot. To guarantee the robust stability of the closed‐loop system and provide the desired closed‐loop performance, the design problem is first formulated as a sampled‐data H∞ control problem, and is then transformed into an equivalent discrete‐time H∞ control problem. Finally, linear matrix inequalities are used to obtain a reduced‐order output‐feedback controller and a static state‐feedback controller. In a course, the design procedure is explained and practice is provided through simulations and experiments. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

show abstract

“…As shown in Lampe (1997, 1999), Rosenwasser and Lampe (2000) and Polyakov, Rosenwasser, and Lampe (1999, 2004, the PTF method opens effective ways to investigate SISO SD systems with delays, including the solution of the H 2 -optimisation problem. The present article establishes the application of the PTM method for the solution of the H 2 -optimisation problem for multivariable SD systems with various delays.…”

Section: Introductionmentioning

confidence: 98%

ℋ₂-optimisation of MIMO sampled-data systems with pure time-delays

Lampe

Rosenwasser²

2009

International Journal of Control

Self Cite

View full text Add to dashboard Cite

This article considers the digital control of a MIMO continuous-time process, when we have time-delays at different locations in the control loop. The controller has to be causal (realisable) and stabilising. Under these restrictions, we search for the controller ensuring the minimal H 2 -norm of the closed loop. Using the parametric transfer function concept, a constructive method for the solution of this problem is provided. On the basis of these results, a number of characteristic properties of the closed H 2 -optimal system are formulated, which are important for practical applications.

show abstract

DIRECTSD-a toolbox for polynomial design of multivariable sampled-data systems

Cited by 5 publications

References 8 publications

Optimal Stochastic SD Control With Preview

Optimal Stochastic SD Control With Preview

Teaching sampled‐data H_∞ control theory using arm robot experiment system

ℋ₂-optimisation of MIMO sampled-data systems with pure time-delays

Contact Info

Product

Resources

About

DIRECTSD-a toolbox for polynomial design of multivariable sampled-data systems

Cited by 5 publications

References 8 publications

Optimal Stochastic SD Control With Preview

Optimal Stochastic SD Control With Preview

Teaching sampled‐data H∞ control theory using arm robot experiment system

ℋ2-optimisation of MIMO sampled-data systems with pure time-delays

Contact Info

Product

Resources

About

Teaching sampled‐data H_∞ control theory using arm robot experiment system

ℋ₂-optimisation of MIMO sampled-data systems with pure time-delays