Technical committee on control education

Presents information of the CS society Presents information of the CS society Technical Committee on Control Education

A survey of good practice in control education

This paper gives a focussed summary of good practice taken primarily from engineers who are responsible for teaching topics related to systems and control. This engineering specialisation allows the paper to give some degree of focus in the discussions around laboratories, software and assessment, although naturally many of the conclusions are generic. A key intention is to provide a summary document or survey paper which can be used by academics as a start point in studies of what is effective in the discipline. It is also hoped that such a summary will will be useful to engineering institutions in drawing together and disseminating open access resources that are freely available to the community at large

Stochastic evolution equations driven by Liouville fractional Brownian motion

Let H be a Hilbert space and E a Banach space. We set up a theory of stochastic integration of L(H,E)-valued functions with respect to H-cylindrical Liouville fractional Brownian motions (fBm) with arbitrary Hurst parameter in the interval (0,1). For Hurst parameters in (0,1/2) we show that a function F:(0,T)\to L(H,E) is stochastically integrable with respect to an H-cylindrical Liouville fBm if and only if it is stochastically integrable with respect to an H-cylindrical fBm with the same Hurst parameter. As an application we show that second-order parabolic SPDEs on bounded domains in \mathbb{R}^d, driven by space-time noise which is white in space and Liouville fractional in time with Hurst parameter in (d/4,1) admit mild solution which are H\"older continuous both and space.Comment: To appear in Czech. Math.

Stochastic equations in Hilbert space with a multiplicative fractional Gaussian noise

In this paper, some explicit solutions are given for stochastic differential equations in a Hilbert space with a multiplicative fractional Gaussian noise. This noise is the formal derivative of a fractional Brownian motion with the Hurst parameter in the interval (1/2,1). These solutions can be weak, strong or mild depending on the specific assumptions. The problem of stochastic stability of these equations is considered and for various notions of stability, sufficient conditions are given for stability. The noise may stabilize or destabilize the corresponding deterministic solutions. Various examples of stochastic partial differential equations are given that satisfy the assumptions for explicit solutions or stability.Fractional Brownian motion Stochastic differential equations in a Hilbert space Explicit solutions of linear stochastic differential equations Fractional Gaussian noise

SEMILINEAR STOCHASTIC EQUATIONS IN A HILBERT SPACE WITH A FRACTIONAL BROWNIAN MOTION

ABSTRACT. The solutions of a family of semilinear stochastic equations in a Hilbert space with a fractional Brownian motion are investigated. The nonlinear term in these equations has primarily only a growth condition assumption. An arbitrary member of the family of fractional Brownian motions can be used in these equations. Existence and uniqueness for both weak and mild solutions are obtained for some of these semilinear equations. The weak solutions are obtained by a measure transformation that verifies absolute continuity with respect to the measure for the solution of the associated linear equation. Some examples of stochastic differential and partial differential equations are given that satisfy the assumptions for the solutions of the semilinear equations. Key Words: Semilinear stochastic equations, fractional Brownian motion, stochastic partial differential equations, absolute continuity of measures

Adaptive control of discrete time Markov processes by the large deviations method

Some discrete time controlled Markov processes in a locally compact metric space whose transition operators depend on an unknown parameter are described. The adaptive controls are constructed using the large deviations of empirical distributions which are uniform in the parameter that takes values in a compact set. The adaptive procedure uses a finite family of continuous, almost optimal controls. Using the large deviations property it is shown that an adaptive control which is a fixed almost optimal control after a finite time is almost optimal with probability nearly 1

Some aspects of the adaptive boundary control of stochastic linear hyperbolic systems

In this paper an adaptive control problem for the boundary or point control of a stochastic linear evolution system is formulated and the solution is described. The infinitesimal generator of the evolution system generates a C0-semigroup that can model many linear hyperbolic systems and the noise in the system is a cylindrical white noise. The solution of the algebraic Riccati equation for the ergodic control problem with a quadratic cost functional is a continuous function of parameters. A Family of least squares estimates of the unknown parameters is exhibited that is strongly consistent. A certainty equivalence adaptive control is constructed that is self-optimizing, that is, the family of average costs using this control converges (almost surely) to the optimal ergodic cost