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.

Adaptive pole placement without excitation probing signals

Abstract: This paper presents an indirect adaptive control scheme for linear systems which may possibly be a nonminimum phase. The control scheme achieves asymptotical pole placement without either introducing persistent excitation probing signals into the systems or assuming any a priori knowledge on the plant parameters. The system order is the only a priori knowledge required on the plant. The adaptive control law is free from singularities in the sense that the estimated plant model is always controllable. The singularities are overcome by a suitable parameter estimates modification which is based upon standard least squares covariance matrix properties. The analysis of the stability and the global convergence of a closed-loop system is given in detail for both discrete-time and continuous-time systems. >

Stabilization of a mini-rotorcraft having four rotors

Abstract: In this paper we present a controller design and implementation on a mini-rotorcraft having four rotors A Lagrangian model of the helicopter was used for the controller synthesis The proposed controller is based on Lyapunov analysis Experimental results show that the controller is able to perform autonomously the tasks of taking-off, hovering and landing

Global stabilization of a PVTOL aircraft model with bounded inputs

Abstract: We propose a global stabilizing control design for the planar vertical takeoff and landing (PVTOL) aircraft, with bounded inputs. The approach is based on the use of non-linear combinations of linear saturation functions bounding the thrust input and the rolling moment to arbitrary saturation limits. We provide global convergence of the state to the origin, using a relatively simple algorithm.

Trajectory Control of a Quadrotor Subject to 2D Wind Disturbances

Abstract: The paper addresses the flight control of a quad-rotor subject to two dimensional unknown static/varying wind disturbances. A model separation is proposed to simplify the control of the six-degrees-of-freedom (6DOF) nonlinear dynamics of the flying robot. Such approach allows to deal with quad-rotor's 3D-motion via two subsystems: dynamic (altitude and MAV-relative forward velocity) and kinematic (nonholonomic-like navigation) subsystems. In terms of control, a hierarchical control is used as the overall control structure to stabilize the kinematic underactuaded subsystem. A control strategy based on sliding-mode and adaptive control techniques is proposed to deal with slow and fast time-varying wind conditions, respectively. This choice not only provides well tracking control but also improves the estimation of unknown disturbance. The backstepping technique is used to stabilize the inner-loop heading dynamics, such recursive design takes into account a constrained heading rate. Promising simulations results show the validity of the proposed control strategy while tracking a time-parameterized straight-line and sinusoidal trajectory.

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: