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.

Attitude and altitude control for a fixed wing UAV applied to photogrammetry

Abstract: The use of autonomous vehicles in daily life is more commonly every day, we can find it in security missions, agriculture, windmills inspection and one of its main uses the processes of photogrammetry, because the new systems and technologies to perform the last one could give errors of centimeters in a topographic map. The studies of large areas are carried out with fixed-wing UAV, so it is necessary to ensure that the vehicle could follow a desired path even in presence of external disturbances, in order to avoid uncovered or wrong defined areas during the post-processing. In this article we analyzed and compared by simulation the use of a non-linear control technique with a PID control to stabilize the airplane’s attitude and height, to prove that in some cases only a linear control technique is enough to stabilize the UAV and also probe that the amount of energy to control the UAV is similar in both techniques. So the majority of computational resources can be assigned to object detection and collision avoidance tasks, for vehicles under 5 kilograms.

Quad rotor-UAV stabilization by predictor based control

Abstract: This works deals with the problem to stabilize a Quad rotor-UAV in presence of time-delay and disturbances. The proposed controller is based on the reduction approach, i.e. the system transformation into one without delay which might be stabilized using a regular controller. Mathematical induction method allows to develop a new transformation based on the fundamental theorem of calculus. A closed-loop stability analysis based on Lyapunov theory is address to prove the feasibility of the controller. The proposed technique is applied to an experimental platform with 4 DOF (degrees of freedom) specifically designed to bring a secure and easy way to tune and test experimental controllers.

Finite Horizon Nonlinear Suboptimal Control for an Autonomous Soaring UAV

Abstract: In this paper, a suboptimal nonlinear discrete control for nonlinear discrete affine systems is implemented in an autonomous soaring unmanned aerial vehicle (UAV) to improve energy consumption and performance. General expressions for this controller are obtained from the continuous nonlinear guidance model of a fixed-wing UAV and applied in a multiloop control structure. Simulation results in the task of trajectory tracking inside a thermal updraft and a comparative study with a proportional derivative (PD) controller validated the effectiveness of this method.

Model reference adaptive control with unknown relative degree

Abstract: This work presents an indirect model reference adaptive control for minimum phase linear systems of arbitrary relative degree. Global stability of the closed loop system is proved in spite of bounded disturbances and asympotic tracking is achieved in the ideal case. >

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: