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The general context of this article is the feedback control of an infinite-dimensional system so that the closed-loop system satisfies a fading-memory property and achieves the setpoint tracking of a given reference signal. More specifically, this article is concerned with the proportional–integral (PI) regulation control of the left Neumann trace of a one-dimensional reaction–diffusion equation with a delayed right Dirichlet boundary control. In this setting, the studied reaction–diffusion equation might be either open-loop stable or unstable. The proposed control strategy goes as follows. First, a finite-dimensional truncated model that captures the unstable dynamics of the original infinite-dimensional system is obtained via spectral decomposition. The truncated model is then augmented by an integral component on the tracking error of the left Neumann trace. After resorting to the Artstein transformation to handle the control input delay, the PI controller is designed by pole shifting. Stability of the resulting closed-loop infinite-dimensional system, consisting of the original reaction–diffusion equation with the PI controller, is then established, thanks to an adequate Lyapunov function. In the case of a time-varying reference input and a time-varying distributed disturbance, our stability result takes the form of an exponential input-to-state stability (ISS) estimate with fading memory. Finally, another exponential ISS estimate with fading memory is established for the tracking performance of the reference signal by the system output. In particular, these results assess the setpoint regulation of the left Neumann trace in the presence of distributed perturbations that converge to a steady-state value and with a time derivative that converges to zero. Numerical simulations are carried out to illustrate the efficiency of our control strategy. [ABSTRACT FROM AUTHOR]

In this paper, we develop a geometric analysis and a numerical algorithm, based on indirect methods, to solve optimal guidance of endoatmospheric launch vehicle systems under mixed control-state constraints. Two main difficulties are addressed. First, we tackle the presence of Euler singularities by introducing a representation of the configuration manifold in appropriate local charts. In these local coordinates, not only the problem is free from Euler singularities but also it can be recast as an optimal control problem with only pure control constraints. The second issue concerns the initialization of the shooting method. We introduce a strategy that combines indirect methods with homotopies, thus providing high accuracy. We illustrate the efficiency of our approach by numerical simulations on missile interception problems under challenging scenarios. [ABSTRACT FROM AUTHOR]

The goal of this paper is to design a stabilizing feedback boundary control for a reaction–diffusion partial differential equation (PDE), where the boundary control is subject to a constant delay while the equation may be unstable without any control. For this system, which is equivalent to a parabolic equation coupled with a transport equation, a prediction-based control is explicitly computed by splitting the infinite-dimensional system into two parts: a finite-dimensional unstable part and a stable infinite-dimensional part. A finite-dimensional delayed controller is computed for the unstable part, and it is shown that this controller stabilizes the whole PDE. The proof is based on an explicit expression of the classical Artstein transformation combined with an adequately designed Lyapunov function. A numerical simulation illustrates the constructive feedback design method. [ABSTRACT FROM AUTHOR]

Consider the minimal time control problem for a single-input control-affine system \mathdot x=X(x) + u1 Y1 (x) in \REALR^n, where the scalar control u1(\cdot) satisfies the constraint \vert u1(\cdot )\vert \leq 1. When applying a shooting method for solving this kind of optimal control problem, one may encounter numerical problems due to the fact that the shooting function is not smooth whenever the control is bang-bang. In this article we propose the following smoothing procedure. For \varepsilon > 0 small, we consider the minimal time problem for the control system \mathdot x = X(x) + u1^{\varepsilon } Y1(x)+ \varepsilon\sum i=2^{m} ui^{\varepsilon } Yi \left (x\right), where the scalar controls ui^{\varepsilon} (\cdot), i=1,\ldots , m, with m \geq 2, satisfy the constraint \sum i=1^{m} \left (ui^{\varepsilon }(t) \right )^{2} \leq 1. We prove, under appropriate assumptions, a strong convergence result of the solution of the regularized problem to the solution of the initial problem. [ABSTRACT FROM PUBLISHER]