Rogelio Lozano

Bio: Rogelio Lozano is an academic researcher from University of Technology of Compiègne. The author has contributed to research in topics: Control theory & Adaptive control. The author has an hindex of 58, co-authored 496 publications receiving 14570 citations. Previous affiliations of Rogelio Lozano include University of Illinois at Urbana–Champaign & Instituto Politécnico Nacional.

Robust adaptive identification of slowly time-varying parameters with bounded disturbances

Abstract: In this paper, the robustness limitations of current recursive identification algorithms are highlighted. Then, it is shown how a particular nonrecursive identification algorithm dramatically improves the robustness to bounded disturbances, noise and slowly time-varying parameters. only at the expense of performing an on-line eigenvalue decomposition on a symmetric semidefinite positive matrix. Furthermore, this algorithm do not require any a priori knowledge of a bound on the disturbance and noise and of a bound on the parameter values.

Simulation and robust trajectory-tracking for a Quadrotor UAV

Abstract: In this article, a robust control algorithm using sliding modes is proposed for the efficient regulation on the trajectory tracking tasks, in the nonlinear, multivariable, quadrotor system model, that ensures the asymptotic convergence to a desired trajectory (reference signal - r(t)) in presence of all possible uncertainties and external disturbances. A smooth piecewise continuous function trajectory is proposed where the corresponding derivatives are bounded. Furthermore, we assume that the disturbances on the vehicle are bounded and the signal r(t) are available on line. The proposed algorithm employs a sliding surface based on the errors generated from the current state of the path in order to reach the desired reference r(t). The stability analysis of the closed-loop control system is proven via the use of Lyapunov theory. Finally, a numerical simulation of tracking a smooth trajectory is performed to demonstrate the validity and effectiveness of the proposed robust algorithm in presence of disturbances onto the vehicle.

Stabilization of the reaction wheel pendulum using an energy approach

Abstract: Control design methods for the control of the reaction wheel pendulum using a single actuator are presented in this paper. The control algorithms are based on the energy of the system and its passivity properties.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Higher-order sliding modes, differentiation and output-feedback control

Abstract: Being a motion on a discontinuity set of a dynamic system, sliding mode is used to keep accurately a given constraint and features theoretically-infinite-frequency switching. Standard sliding modes provide for finite-time convergence, precise keeping of the constraint and robustness with respect to internal and external disturbances. Yet the relative degree of the constraint has to be 1 and a dangerous chattering effect is possible. Higher-order sliding modes preserve or generalize the main properties of the standard sliding mode and remove the above restrictions. r-Sliding mode realization provides for up to the rth order of sliding precision with respect to the sampling interval compared with the first order of the standard sliding mode. Such controllers require higher-order real-time derivatives of the outputs to be available. The lacking information is achieved by means of proposed arbitrary-order robust exact differentiators with finite-time convergence. These differentiators feature optimal asymptot...

Linear systems

Abstract: Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge: