Wyniki wyszukiwania - BazTech

Ograniczanie wyników

Znaleziono wyników: 3

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: L2 norm

Sortuj według:

Ogranicz wyniki do:

Numerical error bound of optimal control for homogeneous linear systems

Daraghmeh Adnan, Qatanani Naji

Archives of Control Sciences

2019

Vol. 29, No. 2

323--337

In this article we focus on the balanced truncation linear quadratic regulator (LQR) with constrained states and inputs. For closed-loop, we want to use the LQR to find an optimal control that minimizes the objective function which called "the quadratic cost function” with respect to the constraints on the states and the control input. In order to do that we have used formal asymptotes for the Pontryagin maximum principle (PMP) and we introduce an approach using the so called The Hamiltonian Function and the underlying algebraic Riccati equation. The theoretical results are validated numerically to show that the model order reduction based on open-loop balancing can also give good closed-loop performance.

Optimal factors in Vladimir Markov's inequality in L2 norm

Baran M., Kowalska A., Ozorka P.

Science, Technology and Innovation

2018

Vol. 2, no. 1

64--73

In this paper we discuss a problem of computation of constants in Vladimir Markov's type inequality in L2 norm on the interval [-1, 1].

Iterative-interpolation algorithms for L2 model reduction

Krajewski W., Viaro U.

Control and Cybernetics

2009

Vol. 38, no 2

543-554

This paper is concerned with the construction of reduced-order models for high-order linear systems in such a way that the L2 norm of the impulse-response error is minimized. Two convergent algorithms that draw on previous procedures presented by the same authors, are suggested: one refers to s-domain representations, the other to time-domain state-space representations. The algorithms are based on an iterative scheme that, at any step, satisfies certain interpolation constraints deriving from the optimality conditions. To make the algorithms suitable to the reduction of very large-scale systems, resort is made to Krylov subspaces and Arnoldi's method. The performance of the reduction algorithms is tested on two benchmark examples.