By Rafael Vazquez, Miroslav Krstic

This monograph provides new positive layout tools for boundary stabilization and boundary estimation for numerous periods of benchmark difficulties in circulate regulate, with power functions to turbulence keep an eye on, climate forecasting, and plasma keep an eye on. the foundation of the strategy utilized in the paintings is the lately built non-stop backstepping strategy for parabolic partial differential equations, increasing the applicability of boundary controllers for move platforms from low Reynolds numbers to excessive Reynolds quantity conditions.

Efforts in movement keep an eye on over the past few years have resulted in quite a lot of advancements in lots of assorted instructions, yet such a lot implementable advancements so far were received utilizing discretized models of the plant types and finite-dimensional regulate recommendations. by contrast, the layout tools tested during this booklet are in keeping with the “continuum” model of the backstepping strategy, utilized to the PDE version of the move. The postponement of spatial discretization till the implementation level deals more than a few numerical and analytical advantages.

Specific themes and features:

* creation of keep an eye on and kingdom estimation designs for flows that come with thermal convection and electrical conductivity, particularly, flows the place instability should be pushed through thermal gradients and exterior magnetic fields.

* program of a different "backstepping" procedure the place the boundary regulate layout is mixed with a selected Volterra transformation of the circulate variables, which yields not just the stabilization of the circulate, but in addition the specific solvability of the closed-loop system.

* Presentation of a end result exceptional in fluid dynamics and within the research of Navier–Stokes equations: closed-form expressions for the suggestions of linearized Navier–Stokes equations less than feedback.

* Extension of the backstepping method of dispose of one of many well-recognized root reasons of transition to turbulence: the decoupling of the Orr–Sommerfeld and Squire systems.

**Control of Turbulent and Magnetohydrodynamic Channel Flows** is a superb reference for a large, interdisciplinary engineering and arithmetic viewers: keep an eye on theorists, fluid mechanicists, mechanical engineers, aerospace engineers, chemical engineers, electric engineers, utilized mathematicians, in addition to study and graduate scholars within the above parts. The booklet can also be used as a supplementary textual content for graduate classes on regulate of distributed-parameter platforms and on movement control.

**Read Online or Download Control of Turbulent and Magnetohydrodynamic Channel Flows: Boundary Stabilization and State Estimation PDF**

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**Extra resources for Control of Turbulent and Magnetohydrodynamic Channel Flows: Boundary Stabilization and State Estimation**

**Example text**

165) where p1 (x) is an output-injection gain to be determined so that the estimate u ˆ converges to the state u. 168) which is autonomous in u ˆ. The gain p1 is designed to obtain exponential stability of the origin u ˆ ≡ 0, thus guaranteeing convergence of the estimate. 172) which is an exponentially stable system. 5, that the original u˜ variable is exponentially decaying. 174) 0 p(1, ξ) = 0. 175) The domain of evolution for Eq. 131). In addition, the following condition should be satisﬁed: p1 (x) = g(x) − p(x, 0).

11 ◦ C/m. Note that the Prandtl number has a value greater than unity, but not too large; that value is typical, for instance, of water. Interestingly, it can be shown that a discretized version of our plant approximates the ordinary diﬀerential equations of Lorenz’s simpliﬁed model of convection [76]. 2: Exact (solid) and approximate (dashed) control kernels at R2 . 37 m. show chaotic behavior. On the other hand, it is well known that the parameter values that lead to chaos in Lorenz’s equations are not physical [51].

The functions U (t) and V (t) are the actuation variables. Assume that it is known that 2 1 , which means that the dynamics of the v state is much slower than the dynamics of the u state. Hence, u will be the fast variable. 96) in the new time scale. 102) by = 0 and approxi- 1, we set 2/ 1 1. 103) vt = vxx + λ3 u + λ4 v. 103) is no longer an evolution equation of the parabolic type but is now a static equation of the elliptic type, a Poisson equation. 103), which is now called the quasi-steady state (QSS), is determined at each time instant by v (responding instantly to any changes in v) and can be computed from it.