We prove existence of minimizing movements for thedislocation dynamics evolution law of a propagating front, in which the normal velocity of the front is the sum of a non-local term and a mean curvature term. We prove that any such minimizing movement is a weak solution of this evolution law, in a sense related to viscositysolutions of the corresponding level-set equation. We also prove theconsistency of this approach, by showing that any minimizing movementcoincides with the smooth evolution as long as the latter exists. Inrelation with this, we finally prove short time existence and uniqueness of a smoothfront evolving according to our law, provided the initial shape issmooth enough.

We study the local exponential stabilization of the 2D and 3DNavier-Stokes equations in a bounded domain, around a givensteady-state flow, by means of a boundary control. We look for acontrol so that the solution to the Navier-Stokes equations be astrong solution. In the 3D case, such solutions may exist if theDirichlet control satisfies a compatibility condition with theinitial condition. In order to determine a feedback law satisfyingsuch a compatibility condition, we consider an extended systemcoupling the Navier-Stokes equations with an equation satisfied bythe control on the boundary of the domain. We determine a linearfeedback law by solving a linear quadratic control problem for thelinearized extended system. We show that this feedback law alsostabilizes the nonlinear extended system.

We study existence and approximation of non-negative solutions of partial differential equations of the type 
 $$\partial_t u - \div (A(\nabla (f(u))+u\nabla V )) = 0 \qquad \mbox{in } (0,+\infty )\times \mathbb{R}^n,\qquad\qquad (0.1)$$ where A is a symmetric matrix-valued function of the spatial variable satisfying a uniform ellipticity condition, $f:[0,+\infty) \rightarrow[0,+\infty)$ is a suitable non decreasing function, $V:\mathbb{R}^n \rightarrow\mathbb{R}$ is a convex function.Introducing the energy functional $\phi(u)=\int_{\mathbb{R}^n} F(u(x))\,{\rm d}x+\int_{\mathbb{R}^n}V(x)u(x)\,{\rm d}x$ ,where F is a convex function linked to f by $f(u) = uF'(u)-F(u)$ ,we show that u is the “gradient flow” of ϕ with respect to the2-Wasserstein distance between probability measures onthe space $\mathbb{R}^n$ , endowed with the Riemannian distance induced by $A^{-1}.$ In the case of uniform convexity of V, long time asymptotic behaviour and decay rate to the stationary statefor solutions of equation (0.1) are studied.A contraction property in Wasserstein distance for solutions of equation (0.1)is also studied in a particular case.

A corrected version of [P. Grabowski and F.M. Callier, ESAIM: COCV12 (2006) 169–197], Theorem 4.1, p. 186, and Example, is given.

This paper is concerned with the sampled-data based adaptivelinear quadratic (LQ) control of hybrid systems with bothunmeasurable Markov jump processes and stochastic noises.By the least matching error estimation algorithm, parameter estimatesare presented. By a double-step (DS) sampling approach and the certaintyequivalence principle, a sampled-data based adaptive LQ control isdesigned. The DS-approach is characterized by a comparatively largeestimation step for parameter estimation and a sufficient small controlstep for control updating. Under mild conditions, the closed-loop systemis shown to be stable. It is found that the key factor determining theperformance index is the estimation step rather than the control step.When the estimation step becomes too small, the system performance willbecome worse. When the estimation step is fixed, the system performancecan indeed be improved by reducing the control step, but cannot reachthe optimal value. The index difference between the sampled-data basedadaptive LQ control and the conventional LQ optimal control is asymptoticallybounded by a constant depending on the estimation step and the prioriinformation of the parameter set.