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.

Depth control using artifitial vision with time-delay of an AUV

Abstract: The purpose of this paper is to present the analysis of the dynamics immersion of an Autonomous Underwater Vehicle considering the time-delay produced by implementing artificial vision algorithm. The parameter tuning of the PD controller is based on the analysis of the stability region taking into account the vision delay. The PD controller goal is to keep the vehicle in a region close to the landmark, and the performance is shown in simulation and real-time experimental results.

Adaptive control of linear systems with poles in the closed LHP with constrained inputs

Abstract: We propose an indirect adaptive control strategy for possibly nonminimum phase systems but having poles in the closed left hand plane (LHP). The adaptive control is based on a control law for systems with amplitude-constrained inputs. Several simulations performed on different second order systems show that the control scheme brings the state of the system to the origin using a control input with arbitrary bounds. The simulation results presented do not require excitation of the system or projection of the parameter estimates into the admissible region, however this may not be the general case. Extension of the proposed strategy to higher order systems is feasible. The approach based on excitation presents no particular problem to cover higher order systems, nevertheless the approach based on projecting the estimates into the admissible region becomes more complex as the order of the system increases.

PD control of robot manipulators considering joint flexibility, actuators dynamics and friction

Abstract: This paper presents a PD controller for robot manipulators simultaneously considering joint flexibility, actuators dynamics as well as friction. The convergence analysis of the proposed PD controller is carried out using passivity. Global convergence to a desired fixed angular position is proved only using position and velocity measurements.

Generalized order integral sliding mode control for non-differentiable disturbance rejection: A comparative study

Abstract: Aiming at clarifying recent advances on disturbance rejection, we analyze a class of non-differentiable disturbances. We expose some subtle but fundamental differences between the fractional order (FO) and integer order (IO) integral sliding modes (ISM) to control a general class of nonlinear dynamical systems. This comparative study suggests that our proposed FOISM outperforms the extremely popular IOISM scheme when the dynamical system is subject to Holder continuous but not necessarily differentiable disturbances. The drawbacks and advantages of the FOISM are discussed, including its capacity to reject Holder disturbances with chattering alleviation. It is shown that the proposed method provides a uniform continuous control signal assuring robustness and invariance for any initial condition. Simulation results reveal the structural differences of these schemes, showing the viability of our proposal.

An energy based approach to the regulation of a model helicopter near to hover

Abstract: In this paper a simple Lagrangian model of the mechanical dynamics of a helicopter is presented that incorporates the energy associated with the rotation of the main rotor blades This is a novel approach in comparison to the existing models proposed in literature Using the proposed model a passivity based design of a unified path tracking control algorithm is obtained that combines position and ‘yaw’ regulation with the regulation of the angular velocity of the rotors The design method uses relative degree matching and dynamic extension to overcome a mismatch of inputs and desired tracking errors with the natural pure feedback structure of the system

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: