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.

Parameter Adaptation Algorithms—Deterministic Environment

Abstract: On-line estimation of the parameters of a plant model or of a controller is one of the key steps in building an adaptive control system. As shown in Chapter 1 the direct estimation of the controller parameters (when possible) can also be interpreted as a plant model estimation in a re-parameterized form. Therefore the problem of on-line estimation of plant model parameters is a generic problem in adaptive control. Such systems will feature a parametric adaptation algorithm which will up-date the estimated parameters at each sampling instant.

Enhanced UAV pose estimation using a KF: experimental validation

Abstract: An experimental validation for improving pose estimation using a linear Kalman Filter (KF) is presented in this paper The procedure is designed to lead with localization data degraded or lost The methodology is focused on determination, tuning and dynamics changes in the covariance matrices in the KF algorithm Several simulations are carried out in order to validate the methodology Similarly several flights tests are conducted in real time for validating the observer scheme A localization system is used and modified for emulating the GPS performance Main results show the good behavior of the proposed methodology and a video of them is available for showing the capabilities of the algorithm

Altitude control of a quad-rotor using adaptive sliding mode

Abstract: This paper addresses simulation results about the altitude control of a quad-rotor unmanned flying vehicle (UAV) in the presence of uncertainty like wind gust. A sliding mode control was proposed, the gain of control can be decreased and, as a result, the chattering amplitude is reduced. The objective is to introduce an adaptation in the control law in order to decrease the gain to the minimal value preserving the sliding mode control and keeping his property of a finite-time convergence.

Design and Construction of a UAV VTOL in Ducted-Fan and Tilt-Rotor Configuration

Abstract: This paper presents the development of an hybrid Unmanned Aerial Vehicle that combines the capabilities of Vertical Takeoff and Landing (VTOL) as well as the hover flight of an helicopter, with the autonomy, load capacity and speed offered by an airplane. We start from an aircraft concept that uses the Tilt-Rotor configuration to perform the transition phase from one flight mode to another and simultaneously we used Ducted-Fans for increasing the flight performance during the vertical flight phase. The aerodynamic design is developed using computational tools, such as XFLR5 for preliminary design and ANSYS FLuent®for the verification of the preliminary fixed wing design. While for the vertical flight, experimental tests were conducted in order to determine the lift system requirements by using Ducted-Fans. Afterwards, the manufacturing process derived from the aerodynamic design is presented. The avionics developed is described for handling the control surfaces during the airplane mode and the main lift system during the vertical flight as well as the integration of mechanism and sensors used. Finally, the obtained results from the conducted vertical flight tests at outdoors environments are presented in order to show the effectiveness of the aircraft concept.

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: