596 research outputs found
Parametrization and geometric analysis of coordination controllers for multi-agent systems
summary:In this paper, we address distributed control structures for multi-agent systems with linear controlled agent dynamics. We consider the parametrization and related geometric structures of the coordination controllers for multi-agent systems with fixed topologies. Necessary and sufficient conditions to characterize stabilizing consensus controllers are obtained. Then we consider the consensus for the multi-agent systems with switching interaction topologies based on control parametrization
On the interaction structure of linear multi-input feedback control systems
The closely-related problems of designing reliable feedback stabilization strategy and coordinating decentralized feedbacks are considered. Two approaches are taken. A geometric characterization of the structure of control interaction (and its dual) was first attempted and a concept of structural homomorphism developed based on the idea of 'similarity' of interaction pattern. The idea of finding classes of individual feedback maps that do not 'interfere' with the stabilizing action of each other was developed by identifying the structural properties of nondestabilizing and LQ-optimal feedback maps. Some known stability properties of LQ-feedback were generalized and some partial solutions were provided to the reliable stabilization and decentralized feedback coordination problems. A concept of coordination parametrization was introduced, and a scheme for classifying different modes of decentralization (information, control law computation, on-line control implementation) in control systems was developed
Quantifying the Evolutionary Self Structuring of Embodied Cognitive Networks
We outline a possible theoretical framework for the quantitative modeling of
networked embodied cognitive systems. We notice that: 1) information self
structuring through sensory-motor coordination does not deterministically occur
in Rn vector space, a generic multivariable space, but in SE(3), the group
structure of the possible motions of a body in space; 2) it happens in a
stochastic open ended environment. These observations may simplify, at the
price of a certain abstraction, the modeling and the design of self
organization processes based on the maximization of some informational
measures, such as mutual information. Furthermore, by providing closed form or
computationally lighter algorithms, it may significantly reduce the
computational burden of their implementation. We propose a modeling framework
which aims to give new tools for the design of networks of new artificial self
organizing, embodied and intelligent agents and the reverse engineering of
natural ones. At this point, it represents much a theoretical conjecture and it
has still to be experimentally verified whether this model will be useful in
practice.
Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing
Within the context of autonomous driving a model-based reinforcement learning
algorithm is proposed for the design of neural network-parameterized
controllers. Classical model-based control methods, which include sampling- and
lattice-based algorithms and model predictive control, suffer from the
trade-off between model complexity and computational burden required for the
online solution of expensive optimization or search problems at every short
sampling time. To circumvent this trade-off, a 2-step procedure is motivated:
first learning of a controller during offline training based on an arbitrarily
complicated mathematical system model, before online fast feedforward
evaluation of the trained controller. The contribution of this paper is the
proposition of a simple gradient-free and model-based algorithm for deep
reinforcement learning using task separation with hill climbing (TSHC). In
particular, (i) simultaneous training on separate deterministic tasks with the
purpose of encoding many motion primitives in a neural network, and (ii) the
employment of maximally sparse rewards in combination with virtual velocity
constraints (VVCs) in setpoint proximity are advocated.Comment: 10 pages, 6 figures, 1 tabl
Formation Shape Control Based on Distance Measurements Using Lie Bracket Approximations
We study the problem of distance-based formation control in autonomous
multi-agent systems in which only distance measurements are available. This
means that the target formations as well as the sensed variables are both
determined by distances. We propose a fully distributed distance-only control
law, which requires neither a time synchronization of the agents nor storage of
measured data. The approach is applicable to point agents in the Euclidean
space of arbitrary dimension. Under the assumption of infinitesimal rigidity of
the target formations, we show that the proposed control law induces local
uniform asymptotic stability. Our approach involves sinusoidal perturbations in
order to extract information about the negative gradient direction of each
agent's local potential function. An averaging analysis reveals that the
gradient information originates from an approximation of Lie brackets of
certain vector fields. The method is based on a recently introduced approach to
the problem of extremum seeking control. We discuss the relation in the paper
Tools for Nonlinear Control Systems Design
This is a brief statement of the research progress made on Grant NAG2-243 titled "Tools for Nonlinear Control Systems Design", which ran from 1983 till December 1996. The initial set of PIs on the grant were C. A. Desoer, E. L. Polak and myself (for 1983). From 1984 till 1991 Desoer and I were the Pls and finally I was the sole PI from 1991 till the end of 1996. The project has been an unusually longstanding and extremely fruitful partnership, with many technical exchanges, visits, workshops and new avenues of investigation begun on this grant. There were student visits, long term.visitors on the grant and many interesting joint projects. In this final report I will only give a cursory description of the technical work done on the grant, since there was a tradition of annual progress reports and a proposal for the succeeding year. These progress reports cum proposals are attached as Appendix A to this report. Appendix B consists of papers by me and my students as co-authors sorted chronologically. When there are multiple related versions of a paper, such as a conference version and journal version they are listed together. Appendix C consists of papers by Desoer and his students as well as 'solo' publications by other researchers supported on this grant similarly chronologically sorted
Drone flock: formation control of multi-drones
Este trabalho foca-se no controlo de veículos aéreos não tripulados, mais especificamente em cenários multi-drone a agirem cooperativamente. Será estudado, projetado, analisado e implementado um Modelo Preditivo de Controlo Descentralizado para a coordenação de múltiplos veículos
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