Carlos Canudas-de-Wit

Bio: Carlos Canudas-de-Wit is an academic researcher from University of Grenoble. The author has contributed to research in topics: Optimization problem & Cell Transmission Model. The author has an hindex of 21, co-authored 88 publications receiving 1919 citations. Previous affiliations of Carlos Canudas-de-Wit include Centre national de la recherche scientifique & Grenoble Institute of Technology.

Constructive tool for orbital stabilization of underactuated nonlinear systems: virtual constraints approach

Abstract: We present a constructive tool for generation and orbital stabilization of periodic solutions for underactuated nonlinear systems. Our method can be applied to any mechanical system with a number of independent actuators smaller than the number of degrees of freedom by one. The synthesized feedback control law is nonlinear and time-dependent. It is derived from a feedback structure that explicitly uses the general or full integral of the systems zero dynamics. The control law generates a periodic solution and makes it exponentially orbitally stable.

A Safe Longitudinal Control for Adaptive Cruise Control and Stop-and-Go Scenarios

Abstract: In this paper, we propose a novel reference model-based control approach for automotive longitudinal control. The reference model is nonlinear and provides dynamic solutions consistent with safety constraints and comfort specifications. The model is based on physical laws of compliant contact and has the particularity that its solutions can be explicitly described by integral curves. This allows to characterize the set of initial condition for which the constraints can be met. This model is combined with a simple feedback loop used to compensate unmodeled dynamics and external disturbances. Model simulations together with experimental results are also presented

Cooperative Control Design for Time-Varying Formations of Multi-Agent Systems

Abstract: This paper deals with cooperative control design for nonlinear multi-agent systems. The control objective is to ensure that a group of agents reaches a formation characterized by external time-varying parameters. First, a translation control design is presented to stabilize the multi-agent system to a circular motion tracking a time-varying center. Then, we propose a new framework based on affine transformations to extend previous results to more complex time-varying formations. Moreover, both control laws are improved adding a cooperative term to distribute the agents uniformly along the formation.

Comments on "A new model for control of systems with friction"

Abstract: This paper provides a complete stability proof of Theorem 1 of the above-mentioned paper (Canudas-de-Wit et al., 1995) for the tracking and the regulation case. In the paper, the proof presented for Theorem 1 only holds for the regulation case where the LaSalle's invariant principle may be invoked. In the tracking case, the closed-loop system becomes a nonautonomous one, and arguments other than the LaSalle's invariant principle should be used. Here we present these arguments and show that Theorem 1 holds for both cases.

Source seeking via collaborative measurements by a circular formation of agents

Abstract: This paper presents a multi-agent algorithm to address the source-seeking problem in which the task is to locate the source of some signal (e.g., a radio transmitter, a location of chemical contamination, etc.). This algorithm is based on the formation control work of another researcher in which they designed a control structure to stabilize a group of non-holonomic vehicles to a circular formation and also to move that formation by changing the location of its center. The source-seeking algorithm builds on these results by providing an outer-loop control law to move this circular formation towards a source. The resulting control law depends only on direct measurements of the signal to calculate an approximate gradient direction which is then used to steer the formation. Under certain assumptions about the spatial propagation of the signal this algorithm causes the center of the agents' formation to asymptotically converge to the location of the source.

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

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:

The Web of Human Sexual Contacts

Abstract: Many ``real-world'' networks are clearly defined while most ``social'' networks are to some extent subjective. Indeed, the accuracy of empirically-determined social networks is a question of some concern because individuals may have distinct perceptions of what constitutes a social link. One unambiguous type of connection is sexual contact. Here we analyze data on the sexual behavior of a random sample of individuals, and find that the cumulative distributions of the number of sexual partners during the twelve months prior to the survey decays as a power law with similar exponents $\alpha \approx 2.4$ for females and males. The scale-free nature of the web of human sexual contacts suggests that strategic interventions aimed at preventing the spread of sexually-transmitted diseases may be the most efficient approach.

Rapidly Exponentially Stabilizing Control Lyapunov Functions and Hybrid Zero Dynamics

Abstract: This paper addresses the problem of exponentially stabilizing periodic orbits in a special class of hybrid models-systems with impulse effects-through control Lyapunov functions. The periodic orbit is assumed to lie in a C1 submanifold Z that is contained in the zero set of an output function and is invariant under both the continuous and discrete dynamics; the associated restriction dynamics are termed the hybrid zero dynamics. The orbit is furthermore assumed to be exponentially stable within the hybrid zero dynamics. Prior results on the stabilization of such periodic orbits with respect to the full-order dynamics of the system with impulse effects have relied on input-output linearization of the dynamics transverse to the zero dynamics manifold. The principal result of this paper demonstrates that a variant of control Lyapunov functions that enforce rapid exponential convergence to the zero dynamics surface, Z, can be used to achieve exponential stability of the periodic orbit in the full-order dynamics, thereby significantly extending the class of stabilizing controllers. The main result is illustrated on a hybrid model of a bipedal walking robot through simulations and is utilized to experimentally achieve bipedal locomotion via control Lyapunov functions.