Optimal control: linear quadratic methods
Optimal control: linear quadratic methods
Linear robust control
Robust and optimal control
Matrix computations (3rd ed.)
A new method for computing the stable invariant subspace of a real Hamiltonian matrix
Journal of Computational and Applied Mathematics - Special issue: dedicated to William B. Gragg on the occasion of his 60th Birthday
LAPACK Users' guide (third ed.)
LAPACK Users' guide (third ed.)
ACM Transactions on Mathematical Software (TOMS)
Linear Optimal Control Systems
Linear Optimal Control Systems
Symplectic Balancing of Hamiltonian Matrices
SIAM Journal on Scientific Computing
Numerical Computation of Deflating Subspaces of Skew-Hamiltonian/Hamiltonian Pencils
SIAM Journal on Matrix Analysis and Applications
Existence, Uniqueness, and Parametrization of Lagrangian Invariant Subspaces
SIAM Journal on Matrix Analysis and Applications
The Quadratic Eigenvalue Problem
SIAM Review
Generalized singular values with algorithms and applications.
Generalized singular values with algorithms and applications.
Hamiltonian and symplectic algorithms for the algebraic riccati equation
Hamiltonian and symplectic algorithms for the algebraic riccati equation
Control in an Information Rich World: Report of the Panel on Future Directions in Control, Dynamics, and Systems
Algorithm 854: Fortran 77 subroutines for computing the eigenvalues of Hamiltonian matrices II
ACM Transactions on Mathematical Software (TOMS)
The Maple package SyNRAC and its application to robust control design
Future Generation Computer Systems
Symbolic/numeric analysis of chaotic synchronization with a CAS
Future Generation Computer Systems
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Current and future directions in the development of numerical methods and numerical software for control problems are discussed. Major challenges include the demand for higher accuracy, robustness of the method with respect to uncertainties in the data or the model, and the need for methods to solve large scale problems. To address these demands it is essential to preserve any underlying physical structure of the problem. At the same time, to obtain the required accuracy it is necessary to avoid all inversions or unnecessary matrix products. We will demonstrate how these demands can be met to a great extent for some important tasks in control, the linear-quadratic optimal control problem for first and second order systems as well as stability radius and H∞ norm computations.