Tridiagonal parametrizations of linear state-space models are proposed for multivariable system identiication. The parametrizations are surjective, i.e. all systems up to a given order can be described. The parametrization is based on the fact that any real square matrix is similar to a real tridiagonal form as well as a compact tridi-agonal form. These parametrizations has signiicantly fewer p...

in this paper, solution of nonlinear optimal control problems and the controlled duffing oscillator, as a special class of optimal control problems, are considered and an efficient algorithm is proposed. this algorithm is based on state parametrization as a polynomial with unknown coefficients. by this method, the control and state variables can be approximated as a function of time. also, the ...

This paper is concerned with the parametrization of nonlinear state feedback controllers via kernel representations. First we de ne an appropriate stability for state feedback systems. Second it is shown that the stability condition can be reduced to the internal stability of a speci c type of output feedback system. Finally the state feedback parametrization will be derived based on the existi...

This paper is concerned with the parametrization of nonlinear state feedback controllers via kernel representations. First we define an appropriate stability for state feedback systems. Second it is shown that the stability condition can be reduced to the internal stability of a specific type of output feedback system. Finally the state feedback parametrization will be derived based on the exis...

Computing a bijective spherical parametrization of a genus-0 surface with low distortion is a fundamental task in geometric modeling and processing. Current methods for spherical parametrization cannot, in general, control the worst case distortion of all triangles nor guarantee bijectivity. Given an initial bijective spherical parametrization, with high distortion, we develop a non-linear cons...

We present a new parametrization of inner functions based on the Schur algorithm. We make use of state space formulas (in practice we obtain a new parametrization of observable pairs). The main advantage of our parametrization is that for each chart the observability gramian is constant: this leads to a very good behavior in some approximation problems.

This paper is concerned with the characterization of all nonlinear stabilizing controllers. Observer based kernel representations are introduced to avoid the di culty which occurs in constructing the state-space realizations of input-output operators. By using observer based kernel representations, the parametrization of all the stabilizing plant-controller pairs is derived which is equivalent ...

In this note we cast parametrization of certain type of covariance extension problems in polynomial model. We show that the Kimura form of state space realization for maximum entropy solution to Carathéodory’s extension problem is Riccati balanced parametrization. As an application we provide a heuristic algorithm for finding solutions to minimum-phase spectral factor with poles and zeros, give...

This paper proposes a control parametrization under Model Predictive Controller (MPC) framework for constrained linear discrete time systems with bounded additive disturbances. The proposed approach has the same feasible domain as that obtained from parametrization over the family of time-varying state feedback policies. In addition, the closed-loop system is stable in the sense that the state ...

A state-space approach to Youla-parametrization of stabilizing controllers for linear and nonlinear systems is suggested. The stabilizing controllers (or a class of stabilizing controllers for nonlinear systems) are characterized as (linearlnonlinear) fractional transformations of stable parameters. The main idea behind this approach is to decompose the output feedback stabilization problem int...