Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

This paper provides a theoretical framework for analysis of consensus algorithms for multi-agent networked systems with an emphasis on the role of directed information flow, robustness to changes in network topology due to link/node failures, time-delays, and performance guarantees. An overview of basic concepts of information consensus in networks and methods of convergence and performance analysis for the algorithms are provided. Our analysis framework is based on tools from matrix theory, algebraic graph theory, and control theory. We discuss the connections between consensus problems in networked dynamic systems and diverse applications including synchronization of coupled oscillators, flocking, formation control, fast consensus in small-world networks, Markov processes and gossip-based algorithms, load balancing in networks, rendezvous in space, distributed sensor fusion in sensor networks, and belief propagation. We establish direct connections between spectral and structural properties of complex networks and the speed of information diffusion of consensus algorithms. A brief introduction is provided on networked systems with nonlocal information flow that are considerably faster than distributed systems with lattice-type nearest neighbor interactions. Simulation results are presented that demonstrate the role of small-world effects on the speed of consensus algorithms and cooperative control of multivehicle formations

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Consensus and Cooperation in Networked Multi-Agent Systems

Data Mining Practical Machine Learning Tools and Techniques

This paper reviews some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006. Distributed coordination of multiple vehicles, including unmanned aerial vehicles, unmanned ground vehicles, and unmanned underwater vehicles, has been a very active research subject studied extensively by the systems and control community. The recent results in this area are categorized into several directions, such as consensus, formation control, optimization, and estimation. After the review, a short discussion section is included to summarize the existing research and to propose several promising research directions along with some open problems that are deemed important for further investigations.

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An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination

System Identification I

/pdf/flat-rrt-a-sampling-based-optimal-trajectory-planner-for-8tgfw8yi4u.pdf

Flat-RRT*: A sampling-based optimal trajectory planner for differentially flat vehicles with constrained dynamics

Brownian motion is used to model the uncertainty in the motion of cars on a highway. The probability of collision of two adjacent cars within a fixed horizon is calculated and its implications are discussed. Moreover, the probability of collision in the presence of emergency braking is also obtained by modeling the occurrence of emergency braking as a Poisson process.

/pdf/a-probabilistic-framework-for-highway-safety-analysis-2rzdn3uuwq.pdf

A probabilistic framework for highway safety analysis

A mixed-integer distributed approach to prosumers aggregation for providing balancing services

We study hysteresis-based switching control for a class of discrete-time stochastic linear systems. We take as given a family of candidate controllers that is sufficiently rich so as to include at least one controller suitable for each admissible process model. The controller operating in closed-loop with the system is replaced as soon as the measured data is significantly incompatible with the corresponding process model. In practice, this happens when the value taken by a least-squares identification cost exceeds its minimum (over all admissible models) by a certain hysteresis factor. The controller is then replaced by the one tuned to the best-fit model. We show that the proposed switching control system is stable for every value of the hysteresis factor, and that this is ensured despite of the presence of possibly unbounded noise.

/pdf/hysteresis-based-switching-control-of-stochastic-linear-3727uhjtb1.pdf

Hysteresis-based switching control of stochastic linear systems

We describe a methodology for reachability analysis of a certain class of stochastic hybrid systems, whose continuous dynamics is governed by stochastic differential equations and discrete dynamics by state-dependent probabilistic transitions. The main feature of the proposed methodology is that it rests on the weak approximation of the solution to the stochastic differential equation with random mode transitions by a Markov chain. Reachability computations then reduce to propagating the transition probabilities of the approximating Markov chain. An example of applications to system verification is presented.

/pdf/a-numerical-approximation-scheme-for-reachability-analysis-px7hw7ex9d.pdf

Maria Prandini

Papers

Flat-RRT*: A sampling-based optimal trajectory planner for differentially flat vehicles with constrained dynamics

A probabilistic framework for highway safety analysis

A mixed-integer distributed approach to prosumers aggregation for providing balancing services

Hysteresis-based switching control of stochastic linear systems

A numerical approximation scheme for reachability analysis of stochastic hybrid systems with state-dependent switchings